AMD Ryzen2 3700X Review & Benchmarks – CPU 8-core/16-thread Performance

What is “Ryzen2” ZEN2?

AMD’s Zen2 (“Matisse”) is the “true” 2nd generation ZEN core on 7nm process shrink while the previous ZEN+ (“Pinnacle Ridge”) core was just an optimisation of the original ZEN (“Summit Ridge”) core that while socket compatible it introduces many design improvements over both previous cores. An APU version (with integrated “Navi” graphics) is scheduled to be launched later.

While new chipsets (500 series) will also be introduced and required to support some new features (PCIe 4.0), with an BIOS/firmware update older boards may support them thus allowing upgrades to existing systems adding more cores and thus performance. [Note: older boards will not be enabled for PCIe 4.0 after all]

The list of changes vs. previous ZEN/ZEN+ is extensive thus performance delta is likely to be very different also:

  • Built around “chiplets” of up to 2 CCX (“core complexes”) each of 4C/8T and 8MB L3 cache (7nm)
  • Central I/O hub with memory controller(s) and PCIe 4.0 bridges connected through IF (“Infinity Fabric”) (12nm)
  • Up to 2 chiplets on desktop platform thus up to 2x2x4C (16C/32T 3950X) (same amount as old ThreadRipper 1950X/2950X)
  • 2x larger L3 cache per CCX thus up to 2x2x16MB (64MB) L3 cache (3900X+)
  • 24 PCIe 4.0 lanes (2x higher transfer rate over PCIe 3.0)
  • 2x DDR4 memory controllers up to 4266Mt/s

To upgrade from Ryzen+/Ryzen1 or not?

Micro-architecturally there are more changes that should improve performance:

  • 256-bit (single-op) SIMD units 2x Fmacs (fixing a major deficiency in ZEN/ZEN+ cores)
  • TLB (2nd level) increased (should help out-of-page access latencies that are somewhat high on ZEN/ZEN+)
  • Memory latencies claim to be reduced through higher-speed memory (note all requests go through IF to Central I/O hub with memory controllers)
  • Load/Store 32bytes/cycle (2x ZEN/ZEN+) to keep up with the 256-bit SIMD units (L1D bandwidth should be 2x)
  • L3 cache is 2x ZEN/ZEN+ but higher latency (cache is exclusive)
  • Infinity Fabric is 512-bit (2x ZEN/ZEN+) and can run 1x or 1/2x vs. DRAM clock (when higher than 3733Mt/s)
  • AMD processors have thankfully not been affected by most of the vulnerabilities bar two (BTI/”Spectre”, SSB/”Spectre v4″) that have now been addressed in hardware.
  • HWM-P (hardware performance state management) transitions latencies reduced (ACPI/CPPCv2)

In this article we test CPU core performance; please see our other articles on:

Hardware Specifications

We are comparing the middle-of-the-range Ryzen2 (3700X) with previous generation Ryzen+ (2700X) and competing architectures with a view to upgrading to a mid-range high performance design.

CPU Specifications AMD Ryzen 9 3900X (Matisse)
AMD Ryzen 7 3700X (Matisse) AMD Ryzen 7 2700X (Pinnacle Ridge) Intel i9 9900K (Coffeelake-R) Intel i9 7900X (Skylake-X) Comments
Cores (CU) / Threads (SP) 12C / 24T 8C / 16T 8C / 16T 8C / 16T 10C / 20T Core counts remain the same.
Topology 2 chiplets, each 2 CCX, each 3 cores (1 disabled) (12C) 1 chiplet, 2 CCX, each 4 cores (8C) 2 CCX, each 4 cores (8C) Monolithic die Monolithic die 1 chiplet+1 sio rather than 1 die
Speed (Min / Max / Turbo) 3.8 / 4.6GHz 3.6 / 4.4GHz 3.7 / 4.2GHz 3.6 / 5GHz 3.3 / 4.3GHz 3700x base clock is lower than 2700x but turbo is higher
Power (TDP / Turbo) 105 / 135W 65 / 90W 105 / 135W 95 / 135W 140 / 308W TDP has been greatly reduced vs. ZEN+
L1D / L1I Caches 12x 32kB 8-way / 12x 32kB 8-way 8x 32kB 8-way / 8x 32kB 8-way 8x 32kB 8-way / 8x 64kB 4-way 8x 32kB 8-way / 8x 32kB 8-way 10x 32kB 8-way / 10x 32kB 8-way L1I has been halved but better no. ways
L2 Caches 12x 512kB (6MB) 8-way 8x 512kB (4MB) 8-way 8x 512kB (4MB) 8-way 8x 256kB (2MB) 16-way 10x 1MB (10MB) 16-way No changes to L2
L3 Caches 2x2x 16MB (64MB) 16-way 2x 16MB (32MB) 16-way 2x 8MB (16MB) 16-way 16MB 16-way 13.75MB 11-way L3 is 2x ZEN+
Mitigations for Vulnerabilities BTI/”Spectre”, SSB/”Spectre v4″ hardware BTI/”Spectre”, SSB/”Spectre v4″ hardware BTI/”Spectre”, SSB/”Spectre v4″ software/firmware RDCL/”Meltdown”, L1TF hardware, BTI/”Spectre”, MDS/”Zombieload”, software/firmware RDCL/”Meltdown” , L1TF, BTI/”Spectre”, MDS/”Zombieload”, all software/firmware Ryzen2 addresses the remaining 2 vulnerabilities while Intel was forced to add MDS to its long list…
Microcode MU-8F7100-11 MU-8F7100-11 MU-8F0802-04 MU-069E0C-9E MU-065504-49 The latest microcodes included in the respective BIOS/Windows have been loaded.
SIMD Units 256-bit AVX/FMA3/AVX2 256-bit AVX/FMA3/AVX2 128bit AVX/FMA3/AVX2 256-bit AVX/FMA3/AVX2 512-bit AVX512 ZEN2 SIMD units are 2x wider than ZEN+

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, FMA3, AVX, etc.). Ryzen2 supports all modern instruction sets including AVX2, FMA3 and even more like SHA HWA but not AVX-512.

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64, latest AMD and Intel drivers. 2MB “large pages” were enabled and in use. Turbo / Boost was enabled on all configurations. All mitigations for vulnerabilities (Meltdown, Spectre, L1TF, MDS, etc.) were enabled as per Windows default where applicable.

Native Benchmarks AMD Ryzen 7 3700X (Matisse)
AMD Ryzen 7 2700X (Pinnacle Ridge)
Intel i9 9900K (Coffeelake-R)
Intel i9 7900X (Skylake-X)
Comments
CPU Arithmetic Benchmark Native Dhrystone Integer (GIPS) 336 [=] 334 400 485 We start with no improvement over ZEN+
CPU Arithmetic Benchmark Native Dhrystone Long (GIPS) 339 [=] 335 393 485 With a 64-bit integer workload nothing much changes.
CPU Arithmetic Benchmark Native FP32 (Float) Whetstone (GFLOPS) 202 [+2%] 198 236 262 Floating-point performance does not change delta either – only 2% faster
CPU Arithmetic Benchmark Native FP64 (Double) Whetstone (GFLOPS) 170 [=] 169 196 223 With FP64 nothing much changes again.
In the legacy integer/floating-point benchmarks ZEN2 is not any faster than ZEN+ despite the change in clocks. Perhaps future microcode updates will help?
BenchCpuMM Native Integer (Int32) Multi-Media (Mpix/s) 1023 [+78%] 574 985 1590 ZEN2 is ~80% faster than ZEN+ despite what we’ve seen before.
BenchCpuMM Native Long (Int64) Multi-Media (Mpix/s) 374 [+2x] 187 414 581 With a 64-bit AVX2 integer vectorised workload, ZEN2 is now 2x faster.
BenchCpuMM Native Quad-Int (Int128) Multi-Media (Mpix/s) 6.56 [+13%] 5.8 6.75 7.56 This is a tough test using Long integers to emulate Int128 without SIMD; here ZEN2 is still 13% faster.
BenchCpuMM Native Float/FP32 Multi-Media (Mpix/s) 100 [+68%] 596 914 1760 In this floating-point AVX/FMA vectorised test, ZEN2 is ~70% faster.
BenchCpuMM Native Double/FP64 Multi-Media (Mpix/s) 618 [+84%] 335 535 533 Switching to FP64 SIMD code, ZEN2 is now ~90% faster than ZEN+
BenchCpuMM Native Quad-Float/FP128 Multi-Media (Mpix/s) 24.22 [+55%] 15.6 23 40.3 In this heavy algorithm using FP64 to mantissa extend FP128, ZEN2 is still 55% faster
With its brand-new 256-bit SIMD units, ZEN2 is anywhere from 55% to 100% faster than ZEN+/ZEN1 a huge upgrade from one generation to the next. For SIMD loads upgrading to ZEN2 gives a huge performance uplift.
BenchCrypt Crypto AES-256 (GB/s) 18 [+12%] 16.1 17.63 23 With AES/HWA support all CPUs are memory bandwidth bound  but ZEN2 manages a 12% improvement.
BenchCrypt Crypto AES-128 (GB/s) 18.76 [+17%] 16.1 17.61 23 What we saw with AES-256 just repeats with AES-128; ZEN2 is now 17% faster.
BenchCrypt Crypto SHA2-256 (GB/s) 20.21 [+9%] 18.6 12 26 With SHA/HWA ZEN2 similarly powers through hashing tests leaving Intel in the dust – and is still ~10% faster than ZEN+
BenchCrypt Crypto SHA1 (GB/s) 20.41 [+6%] 19.3 22.9 38 The less compute-intensive SHA1 does not change things due to acceleration.
BenchCrypt Crypto SHA2-512 (GB/s) 3.77 9 21
ZEN2 with AES/SHA HWA is memory bound like all other CPUs, but it still manages 6-17% better performance than ZEN+ using the same memory. But as ZEN2 is rated for faster memory – using such memory would greatly improve the results.
BenchFinance Black-Scholes float/FP32 (MOPT/s) 257 276 309
BenchFinance Black-Scholes double/FP64 (MOPT/s) 229 [+5%] 219 238 277 Switching to FP64 code, ZEN2 is just 5% faster.
BenchFinance Binomial float/FP32 (kOPT/s) 107 59.9 70.5 Binomial uses thread shared data thus stresses the cache & memory system;
BenchFinance Binomial double/FP64 (kOPT/s) 57.98 [-4%] 60.6 61.6 68 With FP64 code ZEN2 is 4% slower.
BenchFinance Monte-Carlo float/FP32 (kOPT/s) 54.2 56.5 63 Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure on the caches;
BenchFinance Monte-Carlo double/FP64 (kOPT/s) 46.34 [+13%] 41 44.5 50.5 Switching to FP64 nothing much changes, ZEN2 is 13% faster.
Ryzen always did well on non-SIMD floating-point algorithms and here it does not disappoint: ZEN2 does not improve much and is pretty much tied with ZEN+ – thus for non SIMD workloads you might as well stick with the older versions.
BenchScience SGEMM (GFLOPS) float/FP32 263 [-12%] 300 375 413 In this tough vectorised algorithm ZEN2 is strangely slower.
BenchScience DGEMM (GFLOPS) double/FP64 193 [+63%] 119 209 212 With FP64 vectorised code, ZEN2 comes back to be over 60% faster.
BenchScience SFFT (GFLOPS) float/FP32 22.78 [+2.5x] 9 22.33 28.6 FFT is also heavily vectorised but stresses the memory sub-system more; ZEN2 is 2.5x (times) faster.
BenchScience DFFT (GFLOPS) double/FP64 11.16 [+41%] 7.92 11.21 14.6 With FP64 code, ZEN2 is ~40% faster.
BenchScience SNBODY (GFLOPS) float/FP32 612 [+2.2x] 280 557 638 N-Body simulation is vectorised but fewer memory accesses; ZEN2 is over 2x faster.
BenchScience DNBODY (GFLOPS) double/FP64 220 [+2x] 113 171 195 With FP64 precision ZEN2 is almost 2x faster.
With highly vectorised SIMD code ZEN2 improves greatly over ZEN2 sometimes managing to be over 2x faster using the same memory.
CPU Image Processing Blur (3×3) Filter (MPix/s) 2049 [+42%] 1440 2560 4880 In this vectorised integer workload ZEN2 starts over 40% faster than ZEN+.
CPU Image Processing Sharpen (5×5) Filter (MPix/s) 950 [+52%] 627 1000 1920 Same algorithm but more shared data makes ZEN2 over 50% faster.
CPU Image Processing Motion-Blur (7×7) Filter (MPix/s) 495 [+52%] 325 519 1000 Again same algorithm but even more data shared still 50% faster
CPU Image Processing Edge Detection (2*5×5) Sobel Filter (MPix/s) 826 [+67%] 495 827 1500 Different algorithm but still vectorised workload ZEN2 is almost 70% faster.
CPU Image Processing Noise Removal (5×5) Median Filter (MPix/s) 89.68 [+24%] 72.1 78 221 Still vectorised code now ZEN2 drops to just 25% faster.
CPU Image Processing Oil Painting Quantise Filter (MPix/s) 25.05 [+5%] 23.9 42.2 66.7 This test has always been tough for Ryzen so ZEN2 does not improve much.
CPU Image Processing Diffusion Randomise (XorShift) Filter (MPix/s) 1763 [+76%] 1000 4000 4070 With integer workload, Intel CPUs seem to do much better but ZEN2 is still almost 80% faster.
CPU Image Processing Marbling Perlin Noise 2D Filter (MPix/s) 321 [+32%] 243 596 777 In this final test again with integer workload ZEN2 is 32% faster
As we’ve seen before, the new SIMD units are anywhere from 5% (worst-case) to 2x faster than ZEN+/1, a huge performance improvement.
Aggregate Score (Points) 8,200 [+40%] 5,850 7,930 11,810 Across all benchmarks, ZEN2 is ~40% faster than ZEN+.
Aggregating all the various scores, the result was never in doubt: ZEN2 (3700X) is 40% faster than the old ZEN+ (2700X) that itself improved over the original 1700X.

ZEN2’s 256-bit wide SIMD units are a big upgrade and show their power in every SIMD workload; otherwise there is only minor improvement.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

Executive Summary: For SIMD workloads you really have to upgrade to Ryzen2; otherwise stick with Ryzen+ unless lower power is preferred. 9/10 overall.

The big change in Ryzen2 are the 256-bit wide SIMD units and all vectorised workloads (Multi-Media, Scientific, Image processing, AI/Machine Learning, etc.) using AVX/FMA will greatly benefit – anything between 50-100% which is a significant increase from just one generation to the next.

But for all other workloads (e.g. Financial, legacy, etc.) there is not much improvement over Ryzen+/1 which were already doing very well against competition.

Naturally it all comes at lower TDP (65W vs 95) which may help with overclocking and also lower noise (from the cooling system) and power consumption (if electricity is expensive or you are running it continuously) thus the performance/W(att) is still greatly improved.

Overall the 3700X does represent a decent improvement over the old 2700X (which is no slouch and was a nice upgrade over 1700X due to better Turbo speeds) and should still be usable in older AM4 300/400-series mainboards with just a BIOS upgrade (without PCIe 4.0).

However, while 2700X (and 1700X/1800X) were top-of-the-line, 3700X is just middle-ground, with the new top CPUs being the 3900X and even the 3950X with twice (2x) more cores and thus potentially huge performance rivaling HEDT Threadripper. The goad-posts have thus moved and thus far higher performance can be yours with just upgrading the CPU. The future is bright…

AMD Ryzen2 3900X Review & Benchmarks – CPU 12-core/24-thread Performance

What is “Ryzen2” ZEN2?

AMD’s Zen2 (“Matisse”) is the “true” 2nd generation ZEN core on 7nm process shrink while the previous ZEN+ (“Pinnacle Ridge”) core was just an optimisation of the original ZEN (“Summit Ridge”) core that while socket compatible it introduces many design improvements over both previous cores. An APU version (with integrated “Navi” graphics) is scheduled to be launched later.

While new chipsets (500 series) will also be introduced and required to support some new features (PCIe 4.0), with an BIOS/firmware update older boards may support them thus allowing upgrades to existing systems adding more cores and thus performance. [Note: older boards will not be enabled for PCIe 4.0 after all]

The list of changes vs. previous ZEN/ZEN+ is extensive thus performance delta is likely to be very different also:

  • Built around “chiplets” of up to 2 CCX (“core complexes”) each of 4C/8T and 8MB L3 cache (7nm)
  • Central I/O hub with memory controller(s) and PCIe 4.0 bridges connected through IF (“Infinity Fabric”) (12nm)
  • Up to 2 chiplets on desktop platform thus up to 2x2x4C (16C/32T 3950X) (same amount as old ThreadRipper 1950X/2950X)
  • 2x larger L3 cache per CCX thus up to 2x2x16MB (64MB) L3 cache (3900X+)
  • 24 PCIe 4.0 lanes (2x higher transfer rate over PCIe 3.0)
  • 2x DDR4 memory controllers up to 4266Mt/s

AMD Ryzen2 3950X chiplets

What’s new in the Ryzen2 core?

Micro-architecturally there are more changes that should improve performance:

  • 256-bit (single-op) SIMD units 2x Fmacs (fixing a major deficiency in ZEN/ZEN+ cores)
  • TLB (2nd level) increased (should help out-of-page access latencies that are somewhat high on ZEN/ZEN+)
  • Memory latencies claim to be reduced through higher-speed memory (note all requests go through IF to Central I/O hub with memory controllers)
  • Load/Store 32bytes/cycle (2x ZEN/ZEN+) to keep up with the 256-bit SIMD units (L1D bandwidth should be 2x)
  • L3 cache is 2x ZEN/ZEN+ but higher latency (cache is exclusive)
  • Infinity Fabric is 512-bit (2x ZEN/ZEN+) and can run 1x or 1/2x vs. DRAM clock (when higher than 3733Mt/s)
  • AMD processors have thankfully not been affected by most of the vulnerabilities bar two (BTI/”Spectre”, SSB/”Spectre v4″) that have now been addressed in hardware.
  • HWM-P (hardware performance state management) transitions latencies reduced (ACPI/CPPCv2)

In this article we test CPU core performance; please see our other articles on:

Hardware Specifications

We are comparing the top-of-the-range Ryzen2 (3900X, 3700X) with previous generation Ryzen+ (2700X) and competing architectures with a view to upgrading to a mid-range high performance design.

CPU Specifications AMD Ryzen 9 3900X (Matisse)
AMD Ryzen 7 3700X (Matisse) AMD Ryzen 7 2700X (Pinnacle Ridge) Intel i9 9900K (Coffeelake-R) Intel i9 7900X (Skylake-X) Comments
Cores (CU) / Threads (SP) 12C / 24T 8C / 16T 8C / 16T 8C / 16T 10C / 20T Matching core-count with CFL (3800X) but 3900X has 50% more cores – more than SKL-X.
Topology 2 chiplets, each 2 CCX, each 3 cores (1 disabled) (12C) 1 chiplet, 2 CCX, each 4 cores (8C) 2 CCX, each 4 cores (8C) Monolithic die Monolithic die AMD uses discrete dies/chiplets unlike Intel
Speed (Min / Max / Turbo) 3.8 / 4.6GHz 3.6 / 4.4GHz 3.7 / 4.2GHz 3.6 / 5GHz 3.3 / 4.3GHz Base clock and turbo are competitive with 3800X having higher base while 3900X higher turbo.
Power (TDP / Turbo) 105 / 135W 65 / 90W 105 / 135W 95 / 135W 140 / 308W TDP remains the same but 3900X may exceed that having more cores.
L1D / L1I Caches 12x 32kB 8-way / 12x 32kB 8-way 8x 32kB 8-way / 8x 32kB 8-way 8x 32kB 8-way / 8x 64kB 4-way 8x 32kB 8-way / 8x 32kB 8-way 10x 32kB 8-way / 10x 32kB 8-way ZEN2 matches L1I with CFL/SKL-X (1/2x ZEN+ but 8-way), L1D is unchanged (also matches Intel)
L2 Caches 12x 512kB (6MB) 8-way 8x 512kB (4MB) 8-way 8x 512kB (4MB) 8-way 8x 256kB (2MB) 16-way 10x 1MB (10MB) 16-way No changes to L2, still 2x CFL. Only SKL-X has its massive 1MB L2 per core which 3900X almost matches!
L3 Caches 2x2x 16MB (64MB) 16-way 2x 16MB (32MB) 16-way 2x 8MB (16MB) 16-way 16MB 16-way 13.75MB 11-way L3 is 2x ZEN/ZEN+ and thus 2x CFL (3800X) with 3900X having a massive 64MB unheard of on the desktop platform! SKL-X can’t match it either.
Mitigations for Vulnerabilities BTI/”Spectre”, SSB/”Spectre v4″ hardware BTI/”Spectre”, SSB/”Spectre v4″ hardware BTI/”Spectre”, SSB/”Spectre v4″ software/firmware RDCL/”Meltdown”, L1TF hardware, BTI/”Spectre”, MDS/”Zombieload”, software/firmware RDCL/”Meltdown” , L1TF, BTI/”Spectre”, MDS/”Zombieload”, all software/firmware Ryzen2 addresses the remaining 2 vulnerabilities while Intel was forced to add MDS to its long list…
Microcode MU-8F7100-11 MU-8F7100-11 MU-8F0802-04 MU-069E0C-9E MU-065504-49 The latest microcodes included in the respective BIOS/Windows have been loaded.
SIMD Units 256-bit AVX/FMA3/AVX2 256-bit AVX/FMA3/AVX2 128bit AVX/FMA3/AVX2 256-bit AVX/FMA3/AVX2 512-bit AVX512 ZEN2 finally matches Intel/CFL but SKL-X’s secret weapon is AVX512 with even consumer CPUs able to do 2x 512-bit FMA ops.

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, FMA3, AVX, etc.). Ryzen2 supports all modern instruction sets including AVX2, FMA3 and even more like SHA HWA but not AVX-512.

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64, latest AMD and Intel drivers. 2MB “large pages” were enabled and in use. Turbo / Boost was enabled on all configurations. All mitigations for vulnerabilities (Meltdown, Spectre, L1TF, MDS, etc.) were enabled as per Windows default where applicable.

Native Benchmarks AMD Ryzen 9 3900X (Matisse)
AMD Ryzen 7 2700X (Pinnacle Ridge)
Intel i9 9900K (Coffeelake-R)
Intel i9 7900X (Skylake-X)
Comments
CPU Arithmetic Benchmark Native Dhrystone Integer (GIPS) 551 [+38%] 334 400 485 Right off Ryzen2 demolishes all CPUs, it is 40% faster than CFL-R!
CPU Arithmetic Benchmark Native Dhrystone Long (GIPS) 556 [+41%] 335 393 485 With a 64-bit integer workload nothing much changes.
CPU Arithmetic Benchmark Native FP32 (Float) Whetstone (GFLOPS) 331 [+40%] 198 236 262 Floating-point performance does not change delta either – still 40% faster!
CPU Arithmetic Benchmark Native FP64 (Double) Whetstone (GFLOPS) 280 [+43%] 169 196 223 With FP64 nothing much changes again.
Ryzen2 starts with an astonishing display, with 3900X demolishing both 9900X and 7900X winning all tests by a large margin 38-43%! It does have 50% more cores (12 vs. 8) but it is not easy to realise gains just by increasing core counts. Intel will need to add far more cores in future CPUs in order to compete!
BenchCpuMM Native Integer (Int32) Multi-Media (Mpix/s) 1449 [+47%] 574 985 1590 Ryzen2 starts off by blowing CFL-R away by 47% and almost matching SKL-X with AVX512!
BenchCpuMM Native Long (Int64) Multi-Media (Mpix/s) 553 [+34%] 187 414 581 With a 64-bit AVX2 integer vectorised workload, Ryzen2 is still 34% faster!
BenchCpuMM Native Quad-Int (Int128) Multi-Media (Mpix/s) 9.52 [+41%] 5.8 6.75 7.56 This is a tough test using Long integers to emulate Int128 without SIMD; here Ryzen2 is again 41% faster!
BenchCpuMM Native Float/FP32 Multi-Media (Mpix/s) 1480 [+62%] 596 914 1760 In this floating-point AVX/FMA vectorised test, Ryzen2 is now over 60% faster than CFL-R and not far off SKL-X!
BenchCpuMM Native Double/FP64 Multi-Media (Mpix/s) 906 [+69%] 335 535 533 Switching to FP64 SIMD code, Ryzen2 is now 70% faster even beating SKL-X!!!
BenchCpuMM Native Quad-Float/FP128 Multi-Media (Mpix/s) 35.23 [+53%] 15.6 23 40.3 In this heavy algorithm using FP64 to mantissa extend FP128, Ryzen2 is still 53% faster!
With its brand-new 256-bit SIMD units, Ryzen2 finally goes toe-to-toe with Intel, soundly beating CFL-R in all benchmarks (+34-69%) sometimes by more than just core count increase (+50%). Only SKL-X with AVX512 manages to be faster (but also with its extra 2 cores). Intel had better release AVX512 for desktop soon but even that will not be enough without increasing core counts to match AMD.
BenchCrypt Crypto AES-256 (GB/s) 15.44 [-12%] 16.1 17.63 23 With AES/HWA support all CPUs are memory bandwidth bound – thus Ryzen2 scores less than Ryzen+ and CFL-R.
BenchCrypt Crypto AES-128 (GB/s) 15.44 [-12%] 16.1 17.61 23 What we saw with AES-256 just repeats with AES-128; Ryzen2 is again slower by 12%.
BenchCrypt Crypto SHA2-256 (GB/s) 29.84 [+2.5x] 18.6 12 26 With SHA/HWA Ryzen2 similarly powers through hashing tests leaving Intel in the dust – 2.5x faster than CFL-R and beating SKL-X with AVX512!
BenchCrypt Crypto SHA1 (GB/s) 19.3 22.9 38
BenchCrypt Crypto SHA2-512 (GB/s) 3.77 9 21
Ryzen2 with AES/SHA HWA is memory bound thus needs faster memory than 3200Mt/s in order to feed all the cores; otherwise due to increased contention for the same bandwidth it may end up slower than Ryzen+ and Intel designs. Here you see the need for HEDT platforms and thus ThreadRipper but at much increased cost.
BenchFinance Black-Scholes float/FP32 (MOPT/s) 257 276 309
BenchFinance Black-Scholes double/FP64 (MOPT/s) 379 [+55%] 219 238 277 Switching to FP64 code, nothing much changes, Ryzen2 55% faster than CFL-R.
BenchFinance Binomial float/FP32 (kOPT/s) 107 59.9 70.5 Binomial uses thread shared data thus stresses the cache & memory system;
BenchFinance Binomial double/FP64 (kOPT/s) 95.73 [+55%] 60.6 61.6 68 With FP64 code Ryzen2 is still 55% faster!
BenchFinance Monte-Carlo float/FP32 (kOPT/s) 54.2 56.5 63 Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure on the caches;
BenchFinance Monte-Carlo double/FP64 (kOPT/s) 76.72 [+72%] 41 44.5 50.5 Switching to FP64 nothing much changes, Ryzen2 is 70% faster than CFL-R and still beating SKL-X.
Ryzen always did well on non-SIMD floating-point algorithms and here it does not disappoint: Ryzen2 is over 50% faster than CFL-R (+55-72%) and soundly beats SKL-X too! As before for financial algorithms there is only one choice and that is Ryzen, be it Ryzen1, Ryzen+ or Ryzen2!
BenchScience SGEMM (GFLOPS) float/FP32 300 375 413 In this tough vectorised algorithm Ryzen2.
BenchScience DGEMM (GFLOPS) double/FP64 212 [+1%] 119 209 212 With FP64 vectorised code, Ryzen2 matches CFL-R and SKL-X.
BenchScience SFFT (GFLOPS) float/FP32 9 22.33 28.6 FFT is also heavily vectorised but stresses the memory sub-system more;
BenchScience DFFT (GFLOPS) double/FP64 12.69 [+13%] 7.92 11.21 14.6 With FP64 code, Ryzen2 is 13% faster than CFL-R.
BenchScience SNBODY (GFLOPS) float/FP32 280 557 638 N-Body simulation is vectorised but fewer memory accesses;
BenchScience DNBODY (GFLOPS) double/FP64 332 [+94%] 113 171 195 With FP64 precision Ryzen2 is almost 2x faster than CFL-R.
With highly vectorised SIMD code Ryzen2 remains competitive but finds some algorithms tougher than others. The new 256-bit SIMD units help but it seems the cores are starved of bandwidth (especially due to SMT) and some workloads may perform better with SMT off.
CPU Image Processing Blur (3×3) Filter (MPix/s) 3056 [+20%] 1440 2560 4880 In this vectorised integer workload Ryzen2 is 20% faster than CFL-R.
CPU Image Processing Sharpen (5×5) Filter (MPix/s) 1499 [+50%] 627 1000 1920 Same algorithm but more shared data makes Ryzen2 50% faster!
CPU Image Processing Motion-Blur (7×7) Filter (MPix/s) 767 [+48%] 325 519 1000 Again same algorithm but even more data shared still 50% faster
CPU Image Processing Edge Detection (2*5×5) Sobel Filter (MPix/s) 1298 [+57%] 495 827 1500 Different algorithm but still vectorised workload Ryzen2 is almost 60% faster.
CPU Image Processing Noise Removal (5×5) Median Filter (MPix/s) 136 [+74%] 72.1 78 221 Still vectorised code now Ryzen2 is 70% faster.
CPU Image Processing Oil Painting Quantise Filter (MPix/s) 38.23 [-9%] 23.9 42.2 66.7 This test has always been tough for Ryzen but Ryzen2 is competitive.
CPU Image Processing Diffusion Randomise (XorShift) Filter (MPix/s) 1384 [-65%] 1000 4000 4070 With integer workload, Intel CPUs seem to do much better.
CPU Image Processing Marbling Perlin Noise 2D Filter (MPix/s) 487 [-18%] 243 596 777 In this final test again with integer workload Ryzen2 is 20% slower.
Thanks to AVX512 SKL-X does win all tests but Ryzen2 beats CFL-R between 20-74% with a few test mixing integer & floating-point SIMD instructions seemingly heavily favouring Intel but nothing to worry about. Overall for image processing Ryzen2 should be your 1st choice.
Aggregate Score (Points) 10,250 [+29%] 5,850 7,930 11,810 Across all benchmarks, Ryzen2 is ~30% faster than CFL-R!
Aggregating all the various scores, the result was never in doubt: Ryzen2 (3900X) is almost 2x faster than Ryzen+ (2700X) and 30% faster than CFL-R, almost catching up HEDT SKL-X.

Ryzen2 (unlike Ryzen1/+) has no trouble with SIMD code due to its widened SIMD units (256-bit) and thus soundly beats the opposition into dust (CFL-R 9900K flagship) sometimes more than just core count increase alone (+50% i.e. 12 cores vs. 8). Sometimes it even beats the AVX512 opposition (SKL-X 7900K) with more cores (10 cores vs. 12).

The only “problematic” algorithms are the memory bound ones where the cores/threads (due to SMT we have 24!) are starved for data and due to contention we see performance lower than less-core devices. While larger caches help (thus the massive 4x 16MB L3 caches) higher clocked memory should be used to match the additional core requirements.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

Executive Summary: Ryzen2 is phenomenal and a huge upgrade over Ryzen1/+ that (most) AM4 users can enjoy and Intel has no answer to. 10/10.

Just as original Ryzen forced Intel to increase (double really) core counts to match (from 4 to 6 then 8), Ryzen2 will force Intel to come up with even more (and better) cores in order to compete. 3900X with its 12-cores soundly beats CFL-R 9900K (8-cores) in just about all benchmarks and in some tests goes toe-to-toe with HEDT SKL-X AVX512-enabled (10-cores) except in memory-bound algorithms where the 4 DDR4 memory channels with 2x more bandwidth count. For that you need ThreadRipper!

Ryzen1/+ was already competitive with Intel on integer and floating-point (non-SIMD) workloads but would fare badly on SIMD (AVX/FMA3/AVX2) workloads due to its 128-bit units; Ryzen2 “fixes” this issue, with its 256-bit units matching Intel. Only SKL-X with its 512-bit units (AVX512) is faster and Intel will have to finally include AVX512 for consumer CPUs in order to compete (IceLake?).

For compute-bound workloads, the forthcoming 3950X with its 16-cores/32-threads brings unprecedented performance to the consumer/desktop segment pretty much unheard of just a few years ago when 4-core/8-threads (e.g. 7700K) were all you could hope for – unless paying a lot more for HEDT where 8/10-core CPUs were far far more expensive. Naturally we shall see how the reduced memory bandwidth affects its performance with likely very fast DDR4 memory (4300Mt/s+) required for best performance.

Let’s also remember than Ryzen2 adds hardware mitigation to its remaining 2 vulnerabilities while Intel has been forced to add MDS/”Zombieload” even to its very latest CFL-R that now loses its trump card: hardware RDCL/”Meltdown” fix not to forget the recommendation to disable SMT/Hyperthreading that would mean a sizeable performance drop.

What is astonishing is that TDP has remained similar and with a BIOS/firmware upgrade, owners of older 300-series boards can now upgrade to these CPUs – and likely not even change the cooler unit! Naturally for PCIe4.0 a 500-series board is recommended and 400-series boards do support more features in Ryzen2/+ but let’s remember than on Intel you can only go back/forward 1 generation even though there is pretty much no core difference from Skylake (Gen 6) to Coffeelake-R (Gen 9)!

From top-end (3950X), high-end (3800X) to low-end/APU (3200G) Ryzen2 is such a compelling choice it is hard to recommend anything else… at least at this time…

The new Neural Networks (AI/ML) Benchmarks: RNN Architecture

What is a Recurrent Neural Network (RNN/LSTM)?

A RNN is a type of neural network that is primarily made of of neurons that store their previous states thus are said to ‘have memory’. In effect this allows them to ‘remember’ patterns or sequences.

However, they can still be used as ‘classifiers’ i.e. recognising visual patterns in images and thus can be used in visual recognition software.

What is VGG(net) is why use it now?

VGGNet is the baseline (or benchmark) CNN-type network that while did not win the ILSVRC 2014 competition (won by GoogleNet/Inception) it is still the preferred choice in the community for classification due to its uniform and thus relatively simple architecture.

While it is generally implemented using CNN layers, either directly or combination like ResNet, it can also be implemented using RNN layers which is what we have done here.

We believe this is a good test scenario and thus a relevant benchmark for today’s common systems.

We are considering much complex neurons, like LSTM, for future tests specifically designed for high-end systems as those used in research and academia.

What is the MNIST dataset and why use it now?

The MNIST database (https://en.wikipedia.org/wiki/MNIST_database) is a decently sized dataset of handwritten digits used for training and testing image processing systems like neural networks. It contains 60K training and 10K testing images of 28×28 pixel anti-aliased gray levels. The number of classes is only 10 (digits ‘0’ to ‘9’).

While they are only 28×28 and not colour, they can be up-scaled to any size by common up-scaling algorithms to test neural networks with little source data.

Today (2019) the digits would be captured in much higher resolution similar to the standard input resolution of the image processing networks of today (between 200×200 and 300×300 pixels).

As Sandra is designed to be small and easily downloadable, it is not possible to include gigabytes (GB) of data for either inference or training. Even the low-resolution (32x32x3) ILSVRC is 3GB thus unusable for our purpose.

What is Sandra’s RNN network architecture and why was it designed this way?

Due to the low complexity of the data and in order to maintain good performance even on low-end hardware, a standard RNN was chosen as the architecture. The features are:

  • Input is 224x224x1 as MNIST images are grey-scale only (up-scaled from 28×28)
  • Output is 10 as there are only 10 classes
  • 4 layer network, 1 RNN, 3 fully connected layers

What are the implementation details of the network?

The CPU version of the neural network supports all common instruction sets and precision and will be continuously updated as the industry moves forward.

  • Both inference/forward and train/back-propagation tested and supported.
  • Precision: single and double floating-point supported with future half/FP16.
  • SIMD Instruction Sets: CPU, SSE2, SSE4.x, AVX, AVX2/FMA and AVX512 with future VNNI.
  • Threads/Cores: Up to the maximum operating system 384 threads in 64-thread groups are supported with hard affinity as all other benchmarks.
  • NUMA: NUMA is supported up to 16 nodes with data allocated to the closest node.

What kind of BTT (Back-propagation Through Time) is used?

Unfortunately as we only know the output (digit) at the end of the sequence (i.e. once all pixels have been presented) we cannot calculate intermediate errors in order to use TBTT (Truncated BTT) which relies on known output at intermediate sequence time-steps.

What kind of detection rate and error does Sandra’s implementation achieve?

Naturally due to the low source resolution, a much shallower/simpler network would have sufficed. However due to up-scaling and the relatively large number of training images there is no danger of over-fitting.

It achieves a % detection rate (over the 10K testing images) after just 1 epoch (Epoch 0) and % after 30 epochs.

Training (30 epochs) took just X* hours on an i9-7900X (10C/20T) using AVX512/single-precision.

Does Sandra fully infer or train the full image set when benchmarking?

As with all other Sandra benchmarks the tests are limited to 30 seconds (in order to complete reasonably quickly) – within this time as many images at random from the data-sets (60K train, 10K test) will be processed.

The new Neural Networks (AI/ML) Benchmarks: CNN Architecture

What is a Convolution Neural Network (CNN/ConvNet)?

A CNN is a type of neural network that is primarily made of of neuron layers connected in such a way that they perform convolution over the previous layers: in effect they are filters over the input – the same way a blur/sharpen/edge/etc filter would be applied over a picture.

They are used as ‘classifiers’ i.e. recognising visual patterns in images and thus are used in visual recognition software.

What is VGG(net) is why use its architecture now?

VGGNet is the baseline (or benchmark) CNN-type network that while did not win the ILSVRC 2014 competition (won by GoogleNet/Inception) it is still the preferred choice in the community for classification due to its uniform and thus relatively simple architecture.

Thus while today (2019) there are far deeper and more complex neural networks, as Sandra is intended to run on common systems we had to choose the most common but relatively simple network.

We believe this is a good test scenario and thus a relevant benchmark for today’s common systems.

We are considering much deeper networks, like ResNet, for future tests specifically designed for high-end systems as those used research and academia.

Why not use Tensorflow, Caffee, etc. as back-end?

As with all Sandra benchmarks we develop our own code which is optimised in the conjunction with the community which includes hardware makers. This allows us to control all the benchmark stack adding new features and support as required which we would not be able to do when using a back-end.

Using a specific vendor’s libraries (e.g. cuDNN, MKL, etc.) would lock-us into a specific platform while we provide implementation for all platforms including all CPU SIMD instruction sets (SSE2, SSE4, AVX, AVX2/FMA, AVX512) and major GP (GPGPU) run-times (CUDA, OpenCL, DirectX 11/12 Compute and future Vulkan*).

What is the MNIST dataset and why use it now?

The MNIST database (https://en.wikipedia.org/wiki/MNIST_database) is a decently sized dataset of handwritten digits used for training and testing image processing systems like neural networks. It contains 60k (thousand) training and 10k testing images of 28×28 pixel anti-aliased gray levels. The number of classes is only 10 (digits ‘0’ to ‘9’).

While they are only 28×28 and not colour (1 channel), they can be up-scaled to any size by common up-scaling algorithms to test neural networks with little source data. Here we up-scale them 8x to 224x224x1.

Today (2018) the digits would be captured in much higher resolution similar to the standard input resolution of the image processing networks of today (between 200×200 and 300×300 pixels).

As Sandra is designed to be small and easily downloadable, it is not possible to include gigabytes (GB) of data for either inference or training. Even the low-resolution ImageNet ILSVRC is 3GB thus unusable for our purpose.

What are the CIFAR datasets and why use them now?

The CIFAR datasets (https://www.cs.toronto.edu/~kriz/cifar.html) are also decently sized datasets of objects used for training and testing image processing systems like neural networks. They both consists of 50k (thousand) training and 10k testing images of 32x32x3 pixel colour images with CIFAR-10 having 10 classes and CIFAR-100 having 100 classes.

Unlike MNIST the pictures are colour (3 channels RGB) and can also be up-scaled to any size by common up-scaling algorithms to test neural networks with little source data. Here we up-scale them 7x to 224x224x3.

Again, just as with MNIST this allows us to include more datasets while processing them in high resolution similar to modern neural networks without including a large dataset like ImageNet ILSVRC dataset.

What are ImageNet ILSVRC datasets and why *not* use them?

The ImageNet (ImageNet Large Scale Visual Recognition Challenge) datasets (http://www.image-net.org/challenges/LSVRC/) are used in the yearly challenge for researchers in object detection, image classification at large scale. They are used to measure progress in computer vision in the World today.

The yearly challenge/competition has thus yielded many recent advancements in the field with winners (and in some cases runner-ups) providing the classical neural networks of today: AlexNet, VGG, ResNet, Inception, etc.

Naturally the task is non-trivial and requires cutting-edge complex neural networks that generally require similarly high-end hardware that is not the domain of mass-market. While old(er) neural networks like AlexNet, VGG or ResNet can today (2018) work on consumer hardware – they are usually deployed in inference/classification mode. Training them (from scratch) would still require significant processing power and time which does not make sense for our benchmark.

Due to the nature of our software (mass-market, small, fast) the size of the datasets (about 3GB for 32x32x3 1.2 million training images) makes them unsuitable to be included either as standard or downloadable. As we aready use low-resolution datasets, it would not make sense to include another – and the high resolution versions (e.g. 256x256x3) are far larger (about 137GB train, 6.3GB test).

Another issue is the licensing: they are licensed for research which Sandra as a commercial product – even though we provide the benchmarks free of charge – would likely not qualify.

What is Sandra’s CNN network architecture and why was it designed this way?

Due to the low complexity of the data and in order to maintain good performance even on low-end hardware, VGG-16 was chosen as the architecture. The features are:

  • For MNIST dataset
    • Input is 224x224x1 as MNIST images are grey-scale (upscaled from 28×28)
    • Output is 10* as there are only 10 classes
    • 8 convolution (3×3 step 1), 5 pooling (2×2 step 2), 3 full-connect layers
  • Network/Engine features
    • Layers: Fully Connected/Dense, Convolution, Max Pooling, Recurrent, Dropout.
    • Activation: ReLU, Leaky ReLU, Smooth ReLU, Sigmoid, TanH. Activation functions are fused to the layers for reduced memory size/bandwidth footprint.
    • Back-propagation Optimiser: 2nd order Hessian.
    • Alignment: For performance, some layer sizes may be increased (e.g. output) to match SIMD alignment; the performance due to SIMD is higher than the overhead due to more un-needed neurons.
    • SIMD Float Width: Up to 64 single-precision pixels per cycle when using AVX512.
    • SIMD Half Width: Up to 128 half-precision pixels per cycle when using AVX512/BFloat16*.
    • SIMD Int8 Width: Up to 256 int8 pixels per cycle when using AVX512/VNNI*.

What are the implementation details of the network?

The CPU version of the neural network supports all common instruction sets and precision and will be continuously updated as the industry moves forward.

  • Both inference/forward and train/back-propagation tested and supported.
  • Processor:
    • Precision: single/FP32 and double/FP64 supported.
    • SIMD Instruction Sets: FPU, SSE2, SSE4.x, AVX, AVX2/FMA, AVX512 with future VNNI*.
    • Threads/Cores: Up to the maximum operating system 384 threads in 64-thread groups are supported with hard affinity as all other benchmarks.
    • Atomic Updates: TSX/RTE used where supported otherwise 128/64/32-bit interlock/update.
    • NUMA: NUMA is supported up to 16 nodes with data allocated to the closest node.
    • Large Pages: Large (2/4MB) pages used where supported and enabled.
  • GP (GPGPU):
    • Precision: single/FP32 and half/FP16 supported.
    • Run-Times: CUDA 10+, OpenCL 1.2+, DirectX 11/12 Compute.
    • Multi-GPU: Up to 8 devices are supported including CPU pseudo-device.

How is the data stored/processed?

We use the CHW format for simple SIMD implementation and performance load/store.

What activation function do you use?

We use the Sigmoid activation function with a fast (but naturally somewhat low-precision) SIMD tanh/exp implementation; while many modern networks (and VGG itself) use ReLU (for speed reasons) we’ve found the Sigmoid to work “better” for us without appreciable performance impact. By better we mean fast convergence and no need for batch normalisation.

What kind of detection rate and error does Sandra’s implementation achieve?

Naturally due to the low source resolution, a much shallower/simpler network would have sufficed. However due to upscaling and the relatively large number of training images there is no danger of overfitting.

It achieves a 95.3% detection rate (over the 10k testing images) after just 1 epoch (Epoch 0) and 99.82% after 30 epochs.

Training (30 epochs) took just 7* hours on an i9-7900X (10C/20T) using AVX512/single-precision.

Does Sandra fully infer or train the full image set when benchmarking?

As with all other Sandra benchmarks the tests are limited to 30 seconds (in order to complete resonably quickly) – within this time as many images at random from the datasets (60k train, 10k test) will be processed.

SiSoftware Sandra 20/20 (2020) Released!

FOR IMMEDIATE RELEASE

Contact: Press Office

SiSoftware Sandra 20/20 (2020) Released:
Brand-new benchmarks, hardware support

Updates: N/A.

London, UK, July 1st, 2019 – We are pleased to announce the launch of SiSoftware Sandra 20/20 (2020), the latest version of our award-winning utility, which includes remote analysis, benchmarking and diagnostic features for PCs, servers, mobile devices and networks.

It adds two Neural Networks AI/ML (Artificial Intelligence/Machine Learning) benchmarks for both CPU and GP (GPU) to measure both CNN (Convolution Neural Network) & RNN (Recurrent Neural Networks) performance on modern hardware.

It also adds hardware support and optimisations for brand-new CPU architectures (AMD Ryzen 2 (3000 series); Intel IceLake, CometLake) not forgetting GPGPU architectures across the various interfaces (CUDA, OpenCL, DirectX ComputeShader, OpenGL Compute).

As SiSoftware operates a “just-in-time” release cycle, some features were introduced in Sandra 2017 service packs: in Sandra Titanium they have been updated and enhanced based on all the feedback received.

Operating System Module

Broad Operating System Support

All current versions supported: Windows 10, 8.1*, 8*, 7*; Server 2019, 2016, 2012/R2 and 2008/R2*

Brand new AI/ML benchmarks featuring both CNN & RNN networks testing both inference/forward and training/back-propagation performance.

Processor Neural Networks (AI/ML)

A combined performance index of CNN (inference/forward & training) & RNN (inference/forward & training) for all precisions (single/FP32, double/FP64 floating-point) and instruction sets (AVX512, AVX2/FMA, AVX, SSE4, SSE2, RTM/HLE with NUMA and large-page support)

Ranker: Processor Neural Networks (Normal/Single Precision)
Ranker: Processor Neural Networks (High/Double Precision)

GP (GPU) Neural Networks (AI/ML)

A combined performance index of CNN (inference/forward & training) & RNN (inference/forward & training) for all precisions (half/FP16, single/FP32 floating-point) and platforms (CUDA, OpenCL, DirectX Compute)

GP (GPU) Neural Networks (Normal/Single Precision)
GP (GPU) Neural Networks (Low/Half Precision)

CNN (Convolution Neural Network) Architecture

Detailed document on the CNN architecture, data-sets and results that underpin our choices for the new benchmarks.

The new Neural Networks (AI/ML) Benchmarks: CNN Architecture

RNN (Recurrent Neural Network) Architecture

Detailed document on the RNN architecture, data-sets and results that underpin our choices for the new benchmarks.

The new Neural Networks (AI/ML) Benchmarks: RNN Architecture

Major changes

  • All connections to website engines (Ranker, Information, Price) are now secured by SSL through HTTP.
  • Sandra client (management console) is now installed as native 64-bit (on x64 and arm64) and thus needs 64-bit Access components (2016, 2013, 2010, etc.) or SQL Server (2017, 2016, 2014, etc) for its database.

Key features of Sandra 20/20

  • 4 native architectures support (x86, x64 – Windows; ARM, ARM64, x86, x64 – Android)
  • Huge official hardware support through technology partners (AMD/ATI, nVidia, Intel).
  • 4 native (GP)GPU/APU platforms support (OpenCL 2.1+, CUDA 10.1+, DirectX Compute Shader 11/10+, OpenGL Compute 4.5+, Vulkan 1.0+).
  • 4 native Graphics platforms support (DirectX 11.x/10.x, OpenGL 4.0+, Vulkan 1.0+).
  • 9 language versions (English, German, French, Italian, Spanish, Japanese, Chinese (Traditional, Simplified), Russian) in a single installer.
  • Enhanced Sandra Lite (Eval) version (free for personal/educational use, evaluation for other uses)

Articles & Benchmarks

For more details, please see the following articles:

Purchasing

For more details, and to purchase the commercial versions, please click here.

Updating or Upgrading

To update your existing commercial version, please click here.

Downloading

For more details, and to download the Lite (Evaluation) version, please click here.

Reviewers and Editors

For your free review copies, please contact us.

About SiSoftware

SiSoftware, founded in 1995, is one of the leading providers of computer analysis, diagnostic and benchmarking software. The flagship product, known as “SANDRA”, was launched in 1997 and has become one of the most widely used products in its field. Many worldwide IT publications, magazines and review sites use SANDRA to analyse the performance of today’s computers. Thousands on-line reviews of computer hardware that use SANDRA are catalogued on our website alone.

Since launch, SiSoftware has always been at the forefront of the technology arena, being among the first providers of benchmarks that show the power of emerging new technologies such as multi-core, GPGPU, OpenCL, OpenGL, DirectCompute, x64, ARM, NUMA, SMT (Hyper-Threading), SMP (multi-threading), AVX512, AVX2/FMA3, AVX, NEON, SSE4.2/4, SSSE3, SSE2, SSE, Java and .NET.

SiSoftware is located in London, UK. For more information, please visit www.sisoftware.net, www.sisoftware.eu, or www.sisoftware.co.uk

SiSoftware Sandra Titanium (2018) SP4/a/c Update: Retpoline and hardware support

Note: Updated 2019/June with information regarding MDS as well as change of recent CFL-R microcode vulnerability reporting.

We are pleased to release SP4/a/c (version 28.69) update for Sandra Titanium (2018) with the following updates:

Sandra Titanium (2018) Press Release

  • Reporting of Operating System (Windows) speculation control settings for the recently discovered vulnerabilities:
    • Kernel Retpoline mitigation status (for RDCL) in recent Windows 10 / Server 2019 updates
    • Kernel Address Table Import Optimisation (“KATI”) status (as above)
    • L1TFL1 data terminal fault mitigation status
    • MDSMicroarchitectural Data Sampling/”ZombieLoad” mitigation status
  • Hardware Support:
    • AMD Ryzen2 (Matisse), Stoney Ridge support
    • Intel CometLake (CML), CannonLake (CNL), IceLake (ICL) support (based on public information)
  • CPU Benchmarks:
    • Image Processing: SIMD code improvement (SSE2/SSE4/AVX/AVX2-FMA/AVX512)
    • Multi-Media: Lock-up on NUMA systems (e.g. AMD ThreadRipper) thanks to Rob @ TechGage.
  • Memory/Cache Benchmarks
    • Return memory controller firmware version to Ranker
  • GPGPU Benchmarks:
    • CUDA SDK 10.1
    • OpenCL: Processing (Fractals/Mandelbrot) variable vector width based on reported FP16/32/64 optimal SIMD width.
  • Ranker, Price & Information Engines
    • HTTPS (encryption) support for all engines as well as the main website

What is Retpoline?

It is a mitigation against ‘Spectre‘ 2 variant (BTI – Branch Target Injection) that affects just about all CPUs (not just Intel but AMD, ARM, etc.). While ‘Spectre’ does not have the same overall performance impact degradation as ‘Meltdown‘ (RDCL – Rogue Data Cache Load) it can have a sizeable impact on some processors and workloads. At this time no CPUs contain hardware mitigation for Spectre without performance impact.

Retpoline (Return Trampoline) is a faster way to mitigate against it without restricting branch speculation in kernel mode (using IBRS/IBPB) and has recently been added to Linux and now Windows version 1809 builds with KB4482887. Note that it still needs to be enabled in registry via the Mitigation Features Override flags as by default it is not enabled.

What CPUs can Retpoline be used on?

Unfortunately Retpoline is only safe to use on some CPUs: AMD CPUs (though does not engage on Ryzen, see below), Intel Broadwell or older (v5 and earlier) – thus not Skylake (v6 or later).

Windows speculation control settings reporting:

Intel Haswell (Core v4), Broadwell (v5) – Retpoline enabled, KATI enabled
Kernel Retpoline Speculation Control – Enabled

Kernel Address Table Import Optimisation – Enabled

(Note RDCL mitigations KVA, L1TF are also enabled as required)

Intel Skylake (Core v6), Kabylake (v7), Skylake/Kabylake-X (v6x) – no Retpoline, KATI can be enabled
Kernel Retpoline Speculation Control – no

Kernel Address Table Import Optimisation – no/yes (can be enabled)

(Note RDCL mitigations KVA, L1TF are enabled as required)

Intel Coffeelake-R (Core v8r), Whiskeylake/AmberLake (Core v8r), CometLake* – no Retpoline, KATI not enabled
Kernel Retpoline Speculation Control – no

Kernel Address Table Import Optimisation – Enabled

Note 2019/June: Latest microcode (AEh) with MDS vulnerability support cause Windows to report KVA/L1TF mitigations as required despite CPU claiming to not be vulnerable to RDCL.

Intel Atom Braswell (Atom v5), GeminiLake/ApolloLake (Atom v6) – no Retpoline but KATI enabled
Kernel Retpoline Speculation Control – no

Kernel Address Table Import Optimisation – Enabled

(Note RDCL mitigations KVA, L1TF are enabled as required)

AMD Ryzen (Threadripper) 1, 2 – no Retpoline, no KATI
Kernel Retpoline Speculation Control – no (should be usable?)

Kernel Address Table Import Optimisation – no (should be usable)

(Note CPU does not require RDCL mitigation thus no KVA, L1TF required)

From our somewhat limited testing above it seems that:

  • Intel Haswell/Broadwell (Core v4/v5) and perhaps earlier (Ivy Bridge/Sandy Bridge Core v3/v2) users are in luck, Retpoline is enabled and should improve performance; unfortunately RDCL (“Meltdown” mitigation) remains.
  • Intel Coffeelake-R (Core v8r refresh), Whiskylake ULV (v8r) users do benefit a bit more for their investment – while Retpoline is not enabled, KATI is enabled and should help. Not requiring KVA is the biggest gain of CFL-R. 2019/June: latest microcode (AEh) causes Windows to require KVA/L1TF thus negating any benefit CFL-R had over original CFL/KBL/SKL.
  • Intel Skylake (Core v6), Kabylake (v7) and Coffeelake (v8) are not able to benefit from Retpoline but KATI can work on some systems (driver dependent). However, on our Skylake ULV, Skylake-X test systems KATI could not be enabled. We are investigating further.
  • Intel Atom (v4/v5+) users should be able to use Retpoline but it seems it cannot be enabled currently. KATI is enabled.
  • AMD Ryzen (Threadripper) 1/2 users should also be able to use Retpoline but it seems it cannot be enabled currently. While RDCL is not required, mitigations for Spectre v2 are required and should be enabled. We are investigating further.

Reviews using Sandra 2018 SP4:

Update & Download

Commercial version customers can download the free updates from their software distributor; Lite users please download from your favourite download site.

Download Sandra Lite

AMD Ryzen 2 Mobile (2500U) Vega 8 GP(GPU) Performance

What is “Ryzen2” ZEN+ Mobile?

It is the long-awaited Ryzen2 APU mobile “Bristol Ridge” version of the desktop Ryzen 2 with integrated Vega graphics (the latest GPU architecture from AMD) for mobile devices. While on desktop we had the original Ryzen1/ThreadRipper – there was no (at least released) APU version or a mobile version – leaving only the much older designs that were never competitive against Intel’s ULV and H APUs.

After the very successful launch of the original “Ryzen1”, AMD has been hard at work optimising and improving the design in order to hit TDP (15-35W) range for mobile devices. It has also added the brand-new Vega graphics cores to the APU that have been incredibly performant in the desktop space. Note that mobile versions have a single CCX (compute unit) thus do not require operating system kernel patches for best thread scheduling/power optimisation.

Here’s what AMD says it has done for Ryzen2 mobile:

  • Process technology optimisations (12nm vs 14nm) – lower power but higher frequencies
  • Radeon RX Vega graphics core (DirectX 12.1)
  • Optimised boost (aka Turbo) algorithm – sharing between CPU & GPU cores

In this article we test GP(GPU) integrated graphics performance; please see our other articles on:

Hardware Specifications

We are comparing the graphics units of Ryzen2 mobile with competitive APUs with integrated graphics  to determine whether they are good enough for modest use, especially for compute (GPGPU) use supporting the CPU.

GPGPU Specifications AMD Radeon RX Vega 8 (2500U)
Intel UHD 630 (7200U)
Intel HD Iris 520 (6500U)
Intel HD Iris 540 (6550U)
Comments
Arch Chipset GCN1.5 GT2 / EV9.5 GT2 / EV9 GT3 / EV9 All graphics cores are minor revisions of previous cores with extra functionality.
Cores (CU) / Threads (SP) 8 / 512 24 / 192 24 / 192 48 / 384 Vega has the most SPs though only a few but powerful CUs
ROPs / TMUs 8 / 32 8 / 16 8 / 16 16 / 24 Vega has less ROPs than GT3 but more TMUs.
Speed (Min-Turbo) 300-1100 300-1000 300-1000 300-950 Turbo boost puts Vega in top position power permitting.
Power (TDP) 25-35W 15-25W 15-25W 15-25W TDP is about the same for all though both Ryzen2 and CFL-U have somewhat higher TDP (25W).
Constant Memory 2.7GB 1.6GB 1.6GB 3.2GB There is no dedicated constant memory thus a large chunk is available to use (GB) unlike a dedicated video card with very fast but small (kB).
Shared (Local) Memory 32kB 64kB 64kB 64kB Intel has 2x larger shared/local memory but slow (likely non dedicated) unlike Vega.
Global Memory 2.7 / 3GB 1.6 / 3.2GB 1.6 / 3.2GB 3.2 / 6.4GB About 50% of main memory can be used as global memory – thus pretty large workloads can be run.
Memory System 128-bit DDR4 2400Mt/s 128-bit DDR3L 1866Mt/s 128-bit DDR3L 1866Mt/s 128-bit DDR4 2133MT/s Ryzen2’s memory controller is rated for faster data rates thus should be able to use faster (laptop) memory.
Memory Bandwidth (GB/s)
36 30 30 33 The high data rate of DDR4 can result in higher bandwidth useful for the GPU cores.
L2 Cache ? 512kB 512kB 1MB L2 is comparable to Intel units.
FP64/double ratio Yes, 1/16x Yes, 1/8x Yes, 1/8 Yes, 1/8x FP64 is supported and at good ratio but lower than Intel’s.
FP16/half ratio
Yes, 2x Yes, 2x Yes, 2x Yes, 2x FP16 is also now supported at twice the rate – again unlike gimped dedicated cards.

Processing Performance

We are testing both OpenCL performance using the latest SDK / libraries / drivers from both AMD and competition.

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64, latest Intel drivers, OpenCL 2.x. Turbo / Boost was enabled on all configurations.

Processing Benchmarks Intel UHD 630 (7200U) Intel HD Iris 520 (6500U) Intel HD Iris 540 (6550U) AMD Radeon RX Vega 8 (2500U) Comments
GPGPU Arithmetic Benchmark Mandel FP16/Half (Mpix/s) 831 927 1630 2000 [+23%] Thanks to FP16 support we see double the performance over FP32 but Vega is only 23% faster than GT3.
GPGPU Arithmetic Benchmark Mandel FP32/Single (Mpix/s) 476 478 865 1350 [+56%] Vega rules FP32 and is over 50% faster than GT3.
GPGPU Arithmetic Benchmark Mandel FP64/Double (Mpix/s) 113 122 209 111 [-47%] FP64 lower rate makes Vega 1/2 the speed of GT3 and only matching GT2 units.
GPGPU Arithmetic Benchmark Mandel FP128/Quad (Mpix/s) 5.71 6.29 10.78 7.11 [-34%] Emulated FP128 precision depends entirely on FP64 performance thus not a lot changes.
Vega is over 50% faster than Intel’s top-end Iris/GT3 graphics but only in FP32 precision – while it gains from FP16 Intel scales better reducing the lead to just 25% or so. In FP64 precision though it’s relatively low 1/16x ratio means it only ties with GT2 low-end-models while GT3 is 2x (twice) as fast. Pity.
GPGPU Crypto Benchmark Crypto AES-256 (GB/s) 0.858 0.87 1.23 2.58 [+2.1x] No wonder AMD is crypto-king: Vega is over 2x faster than even GT3.
GPGPU Crypto Benchmark Crypto AES-128 (GB/s) 1 1.08 1.52 3.3 [+2.17x] Nothing changes here, Vega is over 2.2x faster.
GPGPU Crypto Benchmark Crypto SHA2-256 (GB/s) 2.72 3 4.7 14.29 [+3x] In this heavy integer workload, Vega is now 3x faster no wonder it’s used for crypto mining.
GPGPU Crypto Benchmark Crypto SHA1 (GB/s) 6 6.64 11.59 18.77 [+62%] SHA1 is less compute intensive allowing Intel to catch up but Vega is still over 60% faster.
GPGPU Crypto Benchmark Crypto SHA2-512 (GB/s) 1.019 1.08 1.86 3.36 [+81%] With 64-bit integer workload, Vega does better and is 80% (almost 2x) faster than GT3.
Nobody will be using integrated graphics for crypto-mining any time soon, but if you needed to (perhaps using encrypted containers, VMs, etc.) then Vega is your choice – even GT3 is left in the dust despite big improvement over low-end GT2. Intel would need at least 2x more cores to be competitive here.
GPGPU Finance Benchmark Black-Scholes half/FP16 (MOPT/s) 1000 1140 1470 1720 [+17%] If 16-bit precision is sufficient for financial work, Vega is 20% faster than GT3.
GPGPU Finance Benchmark Black-Scholes float/FP32 (MOPT/s) 694 697 794 829 [+4%] In this relatively simple FP32 financial workload Vega is just 4% faster than GT3.
GPGPU Finance Benchmark Black-Scholes double/FP64 (MOPT/s) 142 154 281 185 [-33%] Switching to FP64 precision, Vega is 33% slower than GT3.
GPGPU Finance Benchmark Binomial half/FP16 (kOPT/s) 86 95 155 270 [+74%] Switching to 16-bit precision allows Vega to gain over GT3 and is almost 2x faster.
GPGPU Finance Benchmark Binomial float/FP32 (kOPT/s) 92 93 153 254 [+66%] Binomial uses thread shared data thus stresses the internal memory sub-system, and here Vega shows its power – it is 66% faster than GT3.
GPGPU Finance Benchmark Binomial double/FP64 (kOPT/s) 18 18.86 32 15.67 [-51%] With FP64 precision Vega loses again vs. GT3 at 1/2 the speed and just matches GT2 units.
GPGPU Finance Benchmark Monte-Carlo half/FP16 (kOPT/s) 211 236 395 584 [+48%] With 16-bit precision, Vega dominates again and is almost 50% faster than GT3.
GPGPU Finance Benchmark Monte-Carlo float/FP32 (kOPT/s) 223 236 412 362 [-12%] Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure – but Vega somehow loses against GT3.
GPGPU Finance Benchmark Monte-Carlo double/FP64 (kOPT/s) 29.5 33.36 58.7 47.13 [-20%] Switching to FP64 precision as expected Vega is slower.
Financial algorithms perform well on Vega – at least in FP16 & FP32 precision but FP64 is too “gimped” (1/16x FP32 rate) and thus loses against GT3 despite more powerful cores.
GPGPU Science Benchmark HGEMM (GFLOPS) half/FP16 127 140 236 884 [+3.75x] With 16-bit precision Vega runs away with GEMM and is almost 4x faster than GT3.
GPGPU Science Benchmark SGEMM (GFLOPS) float/FP32 105 107 175 214 [+79%] GEMM makes heavy use of shared/local memory which is likely why Vega is 80% faster than GT3.
GPGPU Science Benchmark DGEMM (GFLOPS) double/FP64 38.8 41.69 70 62.6 [-11%] As expected, due to gimped FP64 rate Vega falls behind GT3 but only by just 11%.
GPGPU Science Benchmark HFFT (GFLOPS) half/FP16 34.2 34.7 45.85 61.34 [+34%] 16-bit precision helps reduce memory bandwidth pressure thus Vega is 34% faster.
GPGPU Science Benchmark SFFT (GFLOPS) float/FP32 20.9 21.45 29.69 31.48 [+6%] FFT is memory access bound but Vega does well to beat GT3.
GPGPU Science Benchmark DFFT (GFLOPS) double/FP64 4.3 5.4 6.07 14.19 [+2.34x] Despite the FP64 rate, Vega manages its memory accesses better beating GT3 by over 2x (two times).
GPGPU Science Benchmark HNBODY (GFLOPS) half/FP16 270 284 449 623 [+39%] 16-bit precision still benefits N-Body and here Vega is 40% faster than GT3.
GPGPU Science Benchmark SNBODY (GFLOPS) float/FP32 162 181 291 537 [+85%] Back to FP32 and Vega has a pretty large 85% lead – almost 2x GT3.
GPGPU Science Benchmark DNBODY (GFLOPS) double/FP64 22.73 26.1 43.34 44 [+2%] With FP64 precision, Vega and GT3 are pretty much tied.
Vega performs well on compute heavy scientific algorithms (making heavy use of shared/local memory) and also benefits from half/FP16 to reduce memory bandwidth pressure, but FP64 rate comes back to haunt it where it loses against Intel’s GT3. Pity.
GPGPU Image Processing Blur (3×3) Filter half/FP16 (MPix/s) 888 937 1390 2273 [+64%] With 16-bit precision Vega doubles its lead to 64% over GT3 despite its gain over FP32.
GPGPU Image Processing Blur (3×3) Filter single/FP32 (MPix/s) 461 491 613 781 [+27%] In this 3×3 convolution algorithm, Vega does well but only 30% faster than GT3.
GPGPU Image Processing Sharpen (5×5) Filter half/FP16 (MPix/s) 279 302 409 582 [+42%] Again a huge gain by using FP16, over 40% faster than GT3.
GPGPU Image Processing Sharpen (5×5) Filter single/FP32 (MPix/s) 100 107 144 157 [+9%] Same algorithm but more shared data reduces the gap to 9%.
GPGPU Image Processing Motion Blur (7×7) Filter half/FP16 (MPix/s) 254 272 396 619 [+56%] Large gain again by switching to FP16 with 3x performance over FP32.
GPGPU Image Processing Motion Blur (7×7) Filter single/FP32 (MPix/s) 103 111 156 161 [+3%] With even more shared data the gap falls to just 3%.
GPGPU Image Processing Edge Detection (2*5×5) Sobel Filter half/FP16 (MPix/s) 259 281 363 595 [+64%] Another huge gain and over 3x improvement over FP32.
GPGPU Image Processing Edge Detection (2*5×5) Sobel Filter single/FP32 (MPix/s) 99 106 145 155 [+7%] Still convolution but with 2 filters – the gap is similar to 5×5 – Vega is 7% faster.
GPGPU Image Processing Noise Removal (5×5) Median Filter half/FP16 (MPix/s) 7.39 9.4 8.56 7.688 [-18%] Big gain but not enough to beat GT3 here.
GPGPU Image Processing Noise Removal (5×5) Median Filter single/FP32 (MPix/s) 7 7.57 7.08 4 [-47%] Vega does not like this algorithm (lots of branching causing divergence) and is 1/2 GT3 speed.
GPGPU Image Processing Oil Painting Quantise Filter half/FP16 (MPix/s) 8.55 9.32 9.22 <BSOD> This test would cause BSOD; we are investigating.
GPGPU Image Processing Oil Painting Quantise Filter single/FP32 (MPix/s) 8 8.65 6.77 2.59 [-70%] Vega does not like this algorithms either (complex branching) and neither does GT3.
GPGPU Image Processing Diffusion Randomise (XorShift) Filter half/FP16 (MPix/s) 941 967 1580 2091 [+32%] In order to prevent artifacts most of this test runs in FP32 thus not much gain here.
GPGPU Image Processing Diffusion Randomise (XorShift) Filter single/FP32 (MPix/s) 878 952 1550 2100 [+35%] This algorithm is 64-bit integer heavy allowing Vega 35% better performance over GT3.
GPGPU Image Processing Marbling Perlin Noise 2D Filter half/FP16 (MPix/s) 341 390 343 1046 [+2.5x] Switching to FP16 makes a huge difference to Vega which is over 2x faster.
GPGPU Image Processing Marbling Perlin Noise 2D Filter single/FP32 (MPix/s) 384 425 652 608 [-7%] One of the most complex and largest filters, Vega is a bit slower than GT3 by 7%.
For image processing Vega generally performs well in FP32 beating GT3 hands down; but there are a few algorithms that may need to be optimised for it that don’t perform as well as expected. Switching to FP16 though doubles/triples scores – thus Vega may be starved of memory.

Memory Performance

We are testing both OpenCL performance using the latest SDK / libraries / drivers from both AMD and competition.

Results Interpretation: Higher values (MB/s, etc.) mean better performance. Lower time values (ns, etc.) mean better performance.

Environment: Windows 10 x64, latest Intel drivers, OpenCL 2.x. Turbo / Boost was enabled on all configurations.

Memory Benchmarks Intel UHD 630 (7200U) Intel HD Iris 520 (6500U) Intel HD Iris 540 (6550U) AMD Radeon RX Vega 8 (2500U) Comments
GPGPU Memory Bandwidth Internal Memory Bandwidth (GB/s) 12.17 21.2 24 27.32 [+14%] With higher speed DDR4 memory, Vega has 14% more bandwidth.
GPGPU Memory Bandwidth Upload Bandwidth (GB/s) 6 10.4 11.7 4.74 [-60%] The GPU<>CPU link seems a bit slow here at 1/2 bandwidth of Intel.
GPGPU Memory Bandwidth Download Bandwidth (GB/s) 6 10.5 11.75 5 [-57%] Download bandwidth shows a similar issue, 1/2 bandwidth expected.
All designs have to rely on the shared memory controller and Vega performs as expected with good internal bandwidth due to higher speed DDR4 memory. But – transfer up/down speeds are disappointing possibly due to the driver as “zero-copy” mode should be engaged and working on such transfers (APU mode).
GPGPU Memory Latency Global (In-Page Random Access) Latency (ns) 246 244 288 412 [+49%] Similarly with CPU data latencies, global “in-page/random” (aka “TLB hit”) latencies are a bit high though not by a huge amount.
GPGPU Memory Latency Global (Full Range Random Access) Latency (ns) 365 372 436 519 [+19%] Due to faster memory clock but increased timings “full/random” latencies appear a bit higher.
GPGPU Memory Latency Global (Sequential Access) Latency (ns) 156 158 213 201 [-6%] Sequential access latencies are less than competition by 6%.
GPGPU Memory Latency Constant Memory (In-Page Random Access) Latency (ns) 245 243 252 411 [+63%] None have dedicated constant memory thus we see a similar picture to global memory: somewhat high latencies.
GPGPU Memory Latency Shared Memory (In-Page Random Access) Latency (ns) 82 84 100 22.5 [1/5x] Vega has dedicated shared/local memory and it shows – it’s about 5x faster than Intel’s designs.
GPGPU Memory Latency Texture (In-Page Random Access) Latency (ns) 1152 1157 1500 278 [1/5x] Texture access is also very fast on Vega, with latencies 5x lower (aka 1/5) than Intel’s designs.
GPGPU Memory Latency Texture (Full Range Random Access) Latency (ns) 1178 1162 1533 418 [1/3x] Even full/random accesses are fast, 3x (three times) faster than Intel’s.
GPGPU Memory Latency Texture (Sequential Access) Latency (ns) 1077 1081 1324 122 [1/10x] With sequential access we see a crazy 10x lower latency as if AMD uses prefetchers and Intel does not.
As we’ve seen in Ryzen 2’s data latency tests – “in-page/random” latencies are higher than competition but the rest are comparative, with sequential (prefetched) latencies especially small. But dedicated shared/local memory is far faster (5x) and texture accesses are also very fast (3-5x) which should greatly help algorithms making use of them.
Plotting the global (or constant) memory latencies together we see that the “in-page/random” access latencies should perhaps peak somewhat lower but still nothing close to what we’ve seen in the (CPU) data memory latencies article. It is not very clear (unlike the texture latencies graph) where the caches are located.
The texture latencies graph is far clearer where we can see each level’s caches; unlike the global (or constant) latencies we see “in-page/random” latency peak and hold at a somewhat lower level (4MB).

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

Vega mobile, as its desktop big siblings, is undoubtedly powerful and a good upgrade from the older integrated GPU cores; it also supports modern features like half/FP16 compute (which needs vectorisation what the driver reports as “optimised width”) and relishes complex algorithms making use of shared/local memory which is efficient. However Intel’s GT3 EV9.x can get close to it in some workloads and due to better FP64 ratio (1/8x vs 1/16x) even beat it in most FP64 precision tests which is somewhat disappointing.

Luckily for AMD, GT3 variant is very rare and thus Vega has an easy job defeating GT2 in just about all tests; but it shows that should Intel “get serious” and continue to improve integrated graphics (and CPUs) like they used to do before Skylake (SKL/KBL) – AMD might have more serious competition on its hands.

Note that until recently (2019) Ryzen2 mobile APUs were not supported by AMD’s main drivers (“Adrenalin”) and had to rely on pretty old OEM (HP, etc.) drivers that were somewhat problematic especially with Windows 10 changing every 6 months while the drivers were almost 1 year old. Thankfully this has now changed and users (and us) can benefit from updated, stable and performant drivers.

In any case if you want a laptop/ultraportable with just an APU and no dedicated graphics, then Vega is pretty much your only choice which means a Ryzen2 system. That pretty much means it is worthy of a recommendation.

In a word: Highly Recommended

In this article we test GP(GPU) integrated graphics performance; please see our other articles on:

AMD Ryzen 2 Mobile 2500U Review & Benchmarks – Cache & Memory Performance

What is “Ryzen2” ZEN+ Mobile?

It is the long-awaited Ryzen2 APU mobile “Bristol Ridge” version of the desktop Ryzen 2 with integrated Vega graphics (the latest GPU architecture from AMD) for mobile devices. While on desktop we had the original Ryzen1/ThreadRipper – there was no (at least released) APU version or a mobile version – leaving only the much older designs that were never competitive against Intel’s ULV and H APUs.

After the very successful launch of the original “Ryzen1”, AMD has been hard at work optimising and improving the design in order to hit TDP (15-35W) range for mobile devices. It has also added the brand-new Vega graphics cores to the APU that have been incredibly performant in the desktop space. Note that mobile versions have a single CCX (compute unit) thus do not require operating system kernel patches for best thread scheduling/power optimisation.

Here’s what AMD says it has done for Ryzen2:

  • Process technology optimisations (12nm vs 14nm) – lower power but higher frequencies
  • Improvements for cache & memory speed & latencies (we shall test that ourselves!)
  • Multi-core optimised boost (aka Turbo) algorithm – XFR2 – higher speeds

Why review it now?

With Ryzen3 soon to be released later this year (2019) – with a corresponding Ryzen3 APU mobile – it is good to re-test the platform especially in light of the many BIOS/firmware updates, many video/GPU driver updates and not forgetting the many operating system (Windows) vulnerabilities (“Spectre”) mitigations that have greatly affected performance – sometimes for the good (firmware, drivers, optimisations) sometimes for the bad (mitigations).

In this article we test CPU Cache and Memory performance; please see our other articles on:

Hardware Specifications

We are comparing the top-of-the-range Ryzen2 (2700X, 2600) with previous generation (1700X) and competing architectures with a view to upgrading to a mid-range high performance design.

 

CPU Specifications AMD Ryzen2 2500U Bristol Ridge Intel i7 6500U (Skylake ULV) Intel i7 7500U (Kabylake ULV) Intel i5 8250U (Coffeelake ULV) Comments
L1D / L1I Caches 4x 32kB 8-way / 4x 64kB 4-way 2x 32kB 8-way / 2x 32kB 8-way 2x 32kB 8-way / 2x 32kB 8-way 4x 32kB 8-way / 4x 32kB 8-way Ryzen2 icache is 2x of Intel with matching dcache.
L2 Caches 4x 512kB 8-way 2x 256kB 16-way 2x 256kB 16-way 4x 256kB 16-way Ryzen2 L2 cache is 2x bigger than Intel and thus 4x larger than older SKL/KBL-U.
L3 Caches 4MB 16-way 4MB 16-way 4MB 16-way 6MB 16-way Here CFL-U brings 50% bigger L3 cache (6 vs 4MB) which may help some workloads.
TLB 4kB pages
64 full-way / 1536 8-way 64 8-way / 1536 6-way 64 8-way / 1536 6-way 64 8-way / 1536 6-way No TLB changes.
TLB 2MB pages
64 full-way / 1536 2-way 8 full-way  / 1536 6-way 8 full-way  / 1536 6-way 8 full-way  / 1536 6-way No TLB changes, same as 4kB pages.
Memory Controller Speed (MHz) 600 2600 (400-3100) 2700 (400-3500) 1600 (400-3400) Ryzen2’s memory controller runs at memory clock (MCLK) base rate thus depends on memory installed. Intel’s UNC (uncore) runs between min and max CPU clock thus perhaps faster.
Memory Speed (MHz) Max
1200-2400 (2667) 1033-1866 (2133) 1067-2133 (2400) 1200-2400 (2533) Ryzen2 now supports up to 2667MHz (officially) which should improve its performance quite a bit – unfortunately fast DDR4 is very expensive right now.
Memory Channels / Width
2 / 128-bit 2 / 128-bit 2 / 128-bit 2 / 128-bit All have 128-bit total channel width.
Memory Timing (clocks)
17-17-17-39 8-56-18-9 1T 14-17-17-40 10-57-16-11 2T 15-15-15-36 4-51-17-8 2T 19-19-19-43 5-63-21-9 2T Timings naturally depend on memory which for laptops is somewhat limited and quite expensive.
Memory Controller Firmware
2.1.0 3.6.0 3.6.4 Firmware is the same as on desktop devices.

Core Topology and Testing

As discussed in the previous articles (Ryzen1 and Ryzen2 reviews), cores on Ryzen are grouped in blocks (CCX or compute units) each with its own L3 cache – but connected via a 256-bit bus running at memory controller clock. However – unlike desktop/workstations – so far all Ryzen2 mobile designs have a single (1) CCX thus all the issues that “plagued” the desktop/workstation Ryzen designs do note apply here.

However, AMD could have released higher-core mobile designs to go against Intel’s H-line (beefed to 6-core / 12-threads with CFL-H) that would have likely required 2 CCX blocks. At this time (start 2019) considering that Ryzen3 (mobile) will launch soon that seems unlikely to happen…

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, AVX, etc.). Ryzen2 mobile supports all modern instruction sets including AVX2, FMA3 and even more.

Results Interpretation: Higher rate values (GOPS, MB/s, etc.) mean better performance. Lower latencies (ns, ms, etc.) mean better performance.

Environment: Windows 10 x64, latest AMD and Intel drivers. 2MB “large pages” were enabled and in use. Turbo / Boost was enabled on all configurations.

Native Benchmarks AMD Ryzen2 2500U Bristol Ridge Intel i7 6500U (Skylake ULV) Intel i7 7500U (Kabylake ULV) Intel i5 8250U (Coffeelake ULV) Comments
CPU Multi-Core Benchmark Total Inter-Core Bandwidth – Best (GB/s) 18.65 [-21%] 16.81 18.93 23.65 Ryzen2 L1D is not as wide as Intel’s designs (512-bit) thus inter-core transfers in L1D are 20% slower.
CPU Multi-Core Benchmark Total Inter-Core Bandwidth – Worst (GB/s) 9.29 [=] 6.62 7.4 9.3 Using the unified L3 caches – both Ryzen2 and CFL-U manage the same bandwidths.
CPU Multi-Core Benchmark Inter-Unit Latency – Same Core (ns) 16 [-24%] 21 18 19 Within the same core (share L1D) Ryzen2 has lower latencies by 24% than all Intel CPUs.
CPU Multi-Core Benchmark Inter-Unit Latency – Same Compute Unit (ns) 46 [-23%] 61 54 56 Within the same compute unit (shareL3) Ryzen2 again yields 23% lower latencies.
CPU Multi-Core Benchmark Inter-Unit Latency – Different Compute Unit (ns) n/a n/a n/a n/a With a single CCX we have no latency issues.
While the L1D cache on Ryzen2 is not as wide as on Intel SKL/KBL/CFL-U to yield the same bandwidth (20% lower), both it and L3 manage lower latencies by a relatively large ~25%. With a single CCX design we have none of the issues seen on the desktop/workstation CPUs.
Aggregated L1D Bandwidth (GB/s) 267 [-67%] 315 302 628 Ryzen2’s L1D is just not wide enough – even 2-core SKL/KBL-U have more bandwidth and CFL-U has almost 3x more.
Aggregated L2 Bandwidth (GB/s) 225 [-29%] 119 148 318 The 2x larger L2 caches (512 vs 256kB) perform better but still CFL-U manages 30% more bandwidth.
Aggregated L3 Bandwidth (GB/s) 130 [-31%] 90 95 188 CFL-U not only has 50% bigger L3 (6 vs 4MB) but also somehow manages 30% more bandwidth too while SKL/KBL-U are left in the dust.
Aggregated Memory (GB/s) 24 [=]
21 21 24 With the same memory clock, Ryzen2 ties with CFL-U which means good bandwidth for the cores.
While we saw big improvements on Ryzen2 (desktop) for all caches L1D/L2/L3 – more work needs to be done: in particular the L1D caches are not wide enough compared to Intel’s CPUs – and even L2/L3 need to be wider. Most likely Ryzen3 with native wide 256-bit SIMD (unlike 128-bit as Ryzen1/2) will have twice as wide L1D/L2 that should be sufficient to match Intel.

The memory controller performs well matching CFL-U and is officially rated for higher DDR4 memory – though on laptops the choices are more limited and more expensive.

Data In-Page Random Latency (ns) 91.8 [4-13-32] [+2.75x] 34.6 [3-10-17] 27.6 [4-12-22] 24.5 As on desktop Ryzen1/2 in-page random latencies are large compared to the competition while L1D/L2 are OK but L3 also somewhat large.
Data Full Random Latency (ns) 117 [4-13-32] [-16%] 108 [3-10-27] 84.7 [4-12-33] 139 Out-of-page latencies are not much different which means Ryzen2 is a lot more competitive but still somewhat high.
Data Sequential Latency (ns) 4.1 [4-6-7] [-31%]
5.6 [3-10-11] 6.5 [4-12-13] 5.9 Ryzen’s prefetchers are working well with sequential access with lower latencies than Intel
Ryzen1/2 desktop issues were high memory latencies (in-page/full random) and nothing much changes here. “In-Page/Random pattern” (TLB hit) latencies are almost 3x higher – actually not much lower compared to “Full/Random pattern” (TBL miss) – which are comparable to Intel’s SKL/KBL/CFL. On the other hand “Sequential pattern” yields lower latencies (30% less) than Intel thus simple access patterns work better than complex/random access patterns.
Looking at the data access latencies’ graph for Ryzen2 mobile – we see the “in-page/random” following the “full/random” latencies all the way to 8MB block where they plateau; we would have expected them to plateau at a lower value. See the “code access latencies” graph below.
Code In-Page Random Latency (ns) 17.6 [5-9-25] [+14%] 13.3 [2-9-18] 14.9 [2-11-21] 15.5 Code latencies were not a problem on Ryzen1/2 and they are OK here, 14% higher.
Code Full Random Latency (ns) 108 [5-15-48] [+19%] 91.8 [2-10-38] 90.4 [2-11-45] 91 Out-of-page latency is also competitive and just 20% higher.
Code Sequential Latency (ns) 8.2 [5-13-20] [+37%] 5.9 [2-4-8] 7.8 [2-4-9] 6 Ryzen’s prefetchers are working well with sequential access pattern latency but not as fast as Intel.
Unlike data, code latencies (any pattern) are competitive with Intel though CFL-U does have lower latencies (between 15-20%) but in exchange you get a 2x bigger L1I (64 vs 32kB) which should help complex software.
This graph for code access latencies is what we expected to see for data: “in-page/random” latencies plateau much earlier than “full/random” thus “TLB hit” latencies being much lower than “TLB miss” latencies.
Memory Update Transactional (MTPS) 7.17 [-7%] 6.5 7.72 7.2 As none of Intel’s CPUs have HLE enabled Ryzen2 performs really well with just 7% less transactions/second.
Memory Update Record Only (MTPS) 5.66 [+5%] 4.66 5.25 5.4 With only record updates it manages to be 5% faster.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

We saw good improvement on Ryzen2 (desktop/workstation) but still not enough to beat Intel and a lot more work is needed both on L1/L2 cache bandwidth/widening and memory latency (“in-page” aka “TBL hit” random access pattern) that cannot be improved with firmware/BIOS updates (AGESA firmware). Ryzen2 mobile does have the potential to use faster DDR4 memory (officially rated 2667MHz) thus could overtake Intel using faster memory – but laptop DDR4 SODIMM choice is limited.

Regardless of these differences – the CPU results we’ve seen are solid thus sufficient to recommend Ryzen2 mobile especially when at a much lower cost than competing designs. Even if you do choose Intel – you will be picking up a better design due to Ryzen2 mobile competition – just compare the SKL/KBL-U and CFL/WHL-U results.

We are looking forward to see what improvements Ryzen3 mobile brings to the mobile platform.

In a word: Recommended – with reservations

In this article we tested CPU Cache and Memory performance; please see our other articles on:

AMD Ryzen 2 Mobile 2500U Review & Benchmarks – CPU Performance

What is “Ryzen2” ZEN+ Mobile?

It is the long-awaited Ryzen2 APU mobile “Bristol Ridge” version of the desktop Ryzen 2 with integrated Vega graphics (the latest GPU architecture from AMD) for mobile devices. While on desktop we had the original Ryzen1/ThreadRipper – there was no (at least released) APU version or a mobile version – leaving only the much older designs that were never competitive against Intel’s ULV and H APUs.

After the very successful launch of the original “Ryzen1”, AMD has been hard at work optimising and improving the design in order to hit TDP (15-35W) range for mobile devices. It has also added the brand-new Vega graphics cores to the APU that have been incredibly performant in the desktop space. Note that mobile versions have a single CCX (compute unit) thus do not require operating system kernel patches for best thread scheduling/power optimisation.

Here’s what AMD says it has done for Ryzen2:

  • Process technology optimisations (12nm vs 14nm) – lower power but higher frequencies
  • Improvements for cache & memory speed & latencies (we shall test that ourselves!)
  • Multi-core optimised boost (aka Turbo) algorithm – XFR2 – higher speeds

Why review it now?

With Ryzen3 soon to be released later this year (2019) – with a corresponding Ryzen3 APU mobile – it is good to re-test the platform especially in light of the many BIOS/firmware updates, many video/GPU driver updates and not forgetting the many operating system (Windows) vulnerabilities (“Spectre”) mitigations that have greatly affected performance – sometimes for the good (firmware, drivers, optimisations) sometimes for the bad (mitigations).

In this article we test CPU core performance; please see our other articles on:

Hardware Specifications

We are comparing the top-of-the-range Ryzen2 mobile (2500U) with competing architectures (Intel gen 6, 7, 8) with a view to upgrading to a mid-range but high performance design.

 

CPU Specifications AMD Ryzen2 2500U Bristol Ridge
Intel i7 6500U (Skylake ULV)
Intel i7 7500U (Kabylake ULV)
Intel i5 8250U (Coffeelake ULV)
Comments
Cores (CU) / Threads (SP) 4C / 8T 2C / 4T 2C / 4T 4C / 8T Ryzen has double the cores of ULV Skylake/Kabylake and only recently Intel has caught up by also doubling cores.
Speed (Min / Max / Turbo) 1.6-2.0-3.6GHz (16x-20x-36x) 0.4-2.6-3.1GHz (4x-26x-31x) 0.4-2.7-3.5GHz (4x-27x-35x) 0.4-1.6-3.4GHz (4x-16x-34x) Ryzen2 has higher base and turbo than CFL-U and higher turbo than all Intel competition.
Power (TDP) 25-35W 15-25W 15-25W 25-35W Both Ryzen2 and CFL-U have higher TDP at 25W and turbo up to 35W depending on configuration while older devices were mostly 15W with turbo 20-25W.
L1D / L1I Caches 4x 32kB 8-way / 4x 64kB 4-way 2x 32kB 8-way / 2x 32kB 8-way 2x 32kB 8-way / 2x 32kB 8-way 4x 32kB 8-way / 4x 32kB 8-way Ryzen2 icache is 2x of Intel with matching dcache.
L2 Caches 4x 512kB 8-way 2x 256kB 16-way 2x 256kB 16-way 4x 256kB 16-way Ryzen2 L2 cache is 2x bigger than Intel and thus 4x larger than older SKL/KBL-U.
L3 Caches 4MB 16-way 4MB 16-way 4MB 16-way 6MB 16-way Here CFL-U brings 50% bigger L3 cache (6 vs 4MB) which may help some workloads.
Microcode (Firmware) MU8F1100-0B MU064E03-C6 MU068E09-8E MU068E09-96 On Intel you can see just how many updates the platforms have had – we’re now at CX versions but even Ryzen2 has had a few.

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, AVX, etc.). Ryzen supports all modern instruction sets including AVX2, FMA3 and even more like SHA HWA (supported by Intel’s Atom only) but has dropped all AMD’s variations like FMA4 and XOP likely due to low usage.

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64, latest AMD and Intel drivers. 2MB “large pages” were enabled and in use. Turbo / Boost was enabled on all configurations.

Native Benchmarks AMD Ryzen2 2500U Bristol Ridge Intel i7 6500U (Skylake ULV) Intel i7 7500U (Kabylake ULV) Intel i5 8250U (Coffeelake ULV) Comments
CPU Arithmetic Benchmark Native Dhrystone Integer (GIPS) 103 [-6%] 52 73 109 Right off Ryzen2 does not beat CFL-U but is very close, soundly beating the older Intel designs.
CPU Arithmetic Benchmark Native Dhrystone Long (GIPS) 102 [-4%] 51 74 106 With a 64-bit integer workload – the difference drops to 4%.
CPU Arithmetic Benchmark Native FP32 (Float) Whetstone (GFLOPS) 79 [+18%] 39 45 67 Somewhat surprisingly, Ryzen2 is almost 20% faster than CFL-U here.
CPU Arithmetic Benchmark Native FP64 (Double) Whetstone (GFLOPS) 67 [+22%] 33 37 55 With FP64 nothing much changes, with Ryzen2 over 20% faster.
You can see why Intel needed to double the cores for ULV: otherwise even top-of-the-line i7 SKL/KBL-U are pounded into dust by Ryzen2. CFL-U does trade blows with it and manages to pull ahead in Dhrystone but Ryzen2 is 20% faster in floating-point. Whatever you choose you can thank AMD for forcing Intel’s hand.
BenchCpuMM Native Integer (Int32) Multi-Media (Mpix/s) 239 [-32%] 183 193 350 In this vectorised AVX2 integer test Ryzen2 starts 30% slower than CFL-U but does beat the older designs.
BenchCpuMM Native Long (Int64) Multi-Media (Mpix/s) 53.4 [-58%] 68.2 75 127 With a 64-bit AVX2 integer vectorised workload, Ryzen2 is even slower.
BenchCpuMM Native Quad-Int (Int128) Multi-Media (Mpix/s) 2.41 [+12%] 1.15 1.12 2.15 This is a tough test using Long integers to emulate Int128 without SIMD; here Ryzen2 has its 1st win by 12% over CFL-U.
BenchCpuMM Native Float/FP32 Multi-Media (Mpix/s) 222 [-20%] 149 159 277 In this floating-point AVX/FMA vectorised test, Ryzen2 is still slower but only by 20%.
BenchCpuMM Native Double/FP64 Multi-Media (Mpix/s) 126 [-22%] 88.3 94.8 163 Switching to FP64 SIMD code, nothing much changes still 20% slower.
BenchCpuMM Native Quad-Float/FP128 Multi-Media (Mpix/s) 6.23 [-16%] 3.79 4.04 7.4 In this heavy algorithm using FP64 to mantissa extend FP128 with AVX2 – Ryzen2 is less than 20% slower.
Just as on desktop, we did not expect AMD’s Ryzen2 mobile to beat 4-core CFL-U (with Intel’s wide SIMD units) and it doesn’t: but it remains very competitive and is just 20% slower. In any case, it soundly beats all older but ex-top-of-the-line i7 SKL/KBL-U thus making them all obsolete at a stroke.
BenchCrypt Crypto AES-256 (GB/s) 10.9 [+1%] 6.29 7.28 10.8 With AES/HWA support all CPUs are memory bandwidth bound – here Ryzen2 ties with CFL-U and soundly beats older versions.
BenchCrypt Crypto AES-128 (GB/s) 10.9 [+1%] 8.84 9.07 10.8 What we saw with AES-256 just repeats with AES-128; Ryzen2 is marginally faster but the improvement is there.
BenchCrypt Crypto SHA2-256 (GB/s) 6.78 [+60%] 2 2.55 4.24 With SHA/HWA Ryzen2 similarly powers through hashing tests leaving Intel in the dust; SHA is still memory bound but Ryzen2 is 60% faster than CFL-U.
BenchCrypt Crypto SHA1 (GB/s) 7.13 [+2%] 3.88 4.07 7.02 Ryzen also accelerates the soon-to-be-defunct SHA1 but CFL-U with AVX2 has caught up.
BenchCrypt Crypto SHA2-512 (GB/s) 1.48 [-44%] 1.47 1.54 2.66 SHA2-512 is not accelerated by SHA/HWA thus Ryzen2 falls behind here.
Ryzen2 mobile (like its desktop brother) gets a boost from SHA/HWA but otherwise ties with CFL-U which is helped by its SIMD units. As before older 2-core i7 SKL/KBL-U are left with no hope and cannot even saturate the memory bandwidth.
BenchFinance Black-Scholes float/FP32 (MOPT/s) 93.3 [-4%] 44.7 49.3 97 In this non-vectorised test we see Ryzen2 matches CFL-U.
BenchFinance Black-Scholes double/FP64 (MOPT/s) 77.8 [-8%] 39 43.3 84.7 Switching to FP64 code, nothing much changes, Ryzen2 is 8% slower.
BenchFinance Binomial float/FP32 (kOPT/s) 35.5 [+61%] 10.4 12.3 22 Binomial uses thread shared data thus stresses the cache & memory system; here the arch(itecture) improvements do show, Ryzen2 is 60% faster than CFL-U.
BenchFinance Binomial double/FP64 (kOPT/s) 19.5 [-7%] 10.1 11.4 21 With FP64 code Ryzen2 drops back from its previous win.
BenchFinance Monte-Carlo float/FP32 (kOPT/s) 20.1 [+1%] 9.24 9.87 19.8 Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure on the caches; Ryzen2 cannot match its previous gain.
BenchFinance Monte-Carlo double/FP64 (kOPT/s) 15.3 [-3%] 7.38 7.88 15.8 Switching to FP64 nothing much changes, Ryzen2 matches CFL-U.
Unlike desktop where Ryzen2 is unstoppable, here we are a more mixed result – with CFL-U able to trade blows with it except one test where Ryzen2 is 60% faster. Otherwise CFL-U does manage to be just a bit faster in the other tests but nothing significant.
BenchScience SGEMM (GFLOPS) float/FP32 107 [+16%] 92 76 85 In this tough vectorised AVX2/FMA algorithm Ryzen2 manages to be almost 20% faster than CFL-U.
BenchScience DGEMM (GFLOPS) double/FP64 47.2 [-6%] 44.2 31.7 50.5 With FP64 vectorised code, Ryzen2 drops down to 6% slower.
BenchScience SFFT (GFLOPS) float/FP32 3.75 [-53%] 7.17 7.21 8 FFT is also heavily vectorised (x4 AVX2/FMA) but stresses the memory sub-system more; Ryzen2 does not like it much.
BenchScience DFFT (GFLOPS) double/FP64 4 [-7%] 3.23 3.95 4.3 With FP64 code, Ryzen2 does better and is just 7% slower.
BenchScience SNBODY (GFLOPS) float/FP32 112 [-27%] 96.6 104.9 154 N-Body simulation is vectorised but many memory accesses and not a Ryzen2 favourite.
BenchScience DNBODY (GFLOPS) double/FP64 45.3 [-30%] 29.6 30.64 64.8 With FP64 code nothing much changes.
With highly vectorised SIMD code Ryzen2 remains competitive but finds some algorithms tougher than others. Just as with desktop Ryzen1/2 it may require SIMD code changes for best performance due to its 128-bit units; Ryzen3 with 256-bit units should fix that.
CPU Image Processing Blur (3×3) Filter (MPix/s) 532 [-39%] 418 474 872 In this vectorised integer AVX2 workload Ryzen2 is quite a bit slower than CFL-U.
CPU Image Processing Sharpen (5×5) Filter (MPix/s) 146 [-58%] 168 191 350 Same algorithm but more shared data makes Ryzen2 even slower, 1/2 CFL-U.
CPU Image Processing Motion-Blur (7×7) Filter (MPix/s) 123 [-32%] 87.6 98 181 Again same algorithm but even more data shared reduces the delta to 1/3.
CPU Image Processing Edge Detection (2*5×5) Sobel Filter (MPix/s) 185 [-37%] 136 164 295 Different algorithm but still AVX2 vectorised workload still Ryzen2 is ~35% slower.
CPU Image Processing Noise Removal (5×5) Median Filter (MPix/s) 26.5 [-1%] 13.3 14.4 26.7 Still AVX2 vectorised code but here Ryzen2 ties with CFL-U.
CPU Image Processing Oil Painting Quantise Filter (MPix/s) 9.38 [-38%] 7.21 7.63 15.09 Again we see Ryzen2 fall behind CFL-U.
CPU Image Processing Diffusion Randomise (XorShift) Filter (MPix/s) 660 [-53%] 730 764 1394 With integer AVX2 workload, Ryzen2 falls behind even SKL/KBL-U.
CPU Image Processing Marbling Perlin Noise 2D Filter (MPix/s) 94.1 [-55%] 99.6 105 209 In this final test again with integer AVX2 workload Ryzen2 is 1/2 speed of CFL-U.

With all the modern instruction sets supported (AVX2, FMA, AES and SHA/HWA) Ryzen2 does extremely well in all workloads – and makes all older i7 SKL/KBL-U designs obsolete and unable to compete. As we said – Intel pretty much had to double the number of cores in CFL-U to stay competitive – and it does – but it is all thanks to AMD.

Even then Ryzen2 does beat CFL-U in non-SIMD tests with the latter being helped tremendously by its wide (256-bit) SIMD units and greatly benefits from AVX2/FMA workloads. But Ryzen3 with double-width SIMD units should be much faster and thus greatly beating Intel designs.

Software VM (.Net/Java) Performance

We are testing arithmetic and vectorised performance of software virtual machines (SVM), i.e. Java and .Net. With operating systems – like Windows 10 – favouring SVM applications over “legacy” native, the performance of .Net CLR (and Java JVM) has become far more important.

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64, latest drivers. .Net 4.7.x (RyuJit), Java 1.9.x. Turbo / Boost was enabled on all configurations.

VM Benchmarks AMD Ryzen2 2500U Bristol Ridge Intel i7 6500U (Skylake ULV) Intel i7 7500U (Kabylake ULV) Intel i5 8250U (Coffeelake ULV) Comments
BenchDotNetAA .Net Dhrystone Integer (GIPS) 22.7 [+39%] 9.58 12.1 16.36 .Net CLR integer starerts great – Ryzen2 is 40% faster than CFL-U.
BenchDotNetAA .Net Dhrystone Long (GIPS) 22 [+34%] 9.24 12.1 16.4 64-bit integer workloads also favour Ryzen2, still 35% faster.
BenchDotNetAA .Net Whetstone float/FP32 (GFLOPS) 40.5 [+9%] 18.7 22.5 37.1 Floating-Point CLR performance is also good but just about 10% faster than CFL-U.
BenchDotNetAA .Net Whetstone double/FP64 (GFLOPS) 49.6 [+6%] 23.7 28.8 46.8 FP64 performance is also great (CLR seems to promote FP32 to FP64 anyway) with Ryzen2 faster by 6%.
.Net CLR performance was always incredible on Ryzen1 and 2 (desktop/workstation) and here is no exception – all Intel designs are left in the dust with even CFL-U soundly beated by anything between 10-40%.
BenchDotNetMM .Net Integer Vectorised/Multi-Media (MPix/s) 43.23 [+20%] 21.32 25 35 Just as we saw with Dhrystone, this integer workload sees a big 20% improvement for Ryzen2.
BenchDotNetMM .Net Long Vectorised/Multi-Media (MPix/s) 44.71 [+21%] 21.27 26 37 With 64-bit integer workload we see a similar story – 21% better.
BenchDotNetMM .Net Float/FP32 Vectorised/Multi-Media (MPix/s) 137 [+46%] 78.17 94 56 Here we make use of RyuJit’s support for SIMD vectors thus running AVX2/FMA code – Ryzen2 does even better here 50% faster than CFL-U.
BenchDotNetMM .Net Double/FP64 Vectorised/Multi-Media (MPix/s) 75.2 [+45%] 43.59 52 35 Switching to FP64 SIMD vector code – still running AVX2/FMA – we see a similar gain
As before Ryzen2 dominates .Net CLR performance – even when using RyuJit’s SIMD instructions we see big gains of 20-45% over CFL-U.
Java Arithmetic Java Dhrystone Integer (GIPS) 222 [+13%] 119 150 196 We start JVM integer performance with a 13% lead over CFL-U.
Java Arithmetic Java Dhrystone Long (GIPS) 208 [+12%] 101 131 185 Nothing much changes with 64-bit integer workload – Ryzen2 still faster.
Java Arithmetic Java Whetstone float/FP32 (GFLOPS) 50.9 [+9%] 23.13 27.8 46.6 With a floating-point workload Ryzen2 performance improvement drops a bit.
Java Arithmetic Java Whetstone double/FP64 (GFLOPS) 54 [+13%] 23.74 28.7 47.7 With FP64 workload Ryzen2 gets back to 13% faster.
Java JVM performance delta is not as high as .Net but still decent just over 10% over CFL-U similar to what we’ve seen on desktop.
Java Multi-Media Java Integer Vectorised/Multi-Media (MPix/s) 48.74 [+15%] 20.5 24 42.5 Oracle’s JVM does not yet support native vector to SIMD translation like .Net’s CLR but Ryzen2 is still 15% faster.
Java Multi-Media Java Long Vectorised/Multi-Media (MPix/s) 46.75 [+4%] 20.3 24.8 44.8 With 64-bit vectorised workload Ryzen2’s lead drops to 4%.
Java Multi-Media Java Float/FP32 Vectorised/Multi-Media (MPix/s) 38.2 [+9%] 14.59 17.6 35 Switching to floating-point we return to a somewhat expected 9% improvement.
Java Multi-Media Java Double/FP64 Vectorised/Multi-Media (MPix/s) 35.7 [+2%] 14.59 17.4 35 With FP64 workload Ryzen2’s lead somewhat unexplicably drops to 2%.
Java’s lack of vectorised primitives to allow the JVM to use SIMD instruction sets allow Ryzen2 to do well and overtake CFL-U between 2-15%.

Ryzen2 on desktop dominated the .Net and Java benchmarks – and Ryzen2 mobile does not disappoint – it is consistently faster than CFL-U which does not bode well for Intel. If you mainly run .Net and Java apps on your laptop then Ryzen2 is the one to get.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

Ryzen2 was a worthy update on the desktop and Ryzen2 mobile does not disappoint; it instantly obsoleted all older Intel designs (SKL/KBL-U) with only the very latest 4-core ULV (CFL/WHL-U) being able to match it. You can see from the results how AMD forced Intel’s hand to double cores in order to stay competitive.

Even then Ryzen2 manages to beat CFL-U in non-SIMD workloads and remains competitive in SIMD AVX2/FMA workloads (only 20% or so slower) while soundly beating SKL/KBL-U with their 2-cores and wide SIMD units. With soon-to-be-released Ryzen3 with wide SIMD units (256-bit as CFL/WHL-U) – Intel will need AVX512 to stay competitive – however it has its own issues which may be problematic in mobile/ULV space.

Both Ryzen2 mobile and CFL/WHL-U have increased TDP (~25W) in order to manage the increased number of cores (instead of 15W with older 2-core designs) and turbo short-term power as much as 35W. This means while larger 14/15″ designs with good cooling are able to extract top performance – smaller 12/13″ designs are forced to use lower cTDP of 15W (20-25W turbo) thus with lower multi-threaded performance.

Also consider than Ryzen2 is not affected by most “Spectre” vulnerabilities and not by “Meltdown” either thus does not need KVA (kernel pages virtualisation) that greatly impacts I/O workloads. Only the very latest Whiskey-Lake ULV (WHL-U gen 8-refresh) has hardware “Meltdown” fixes – thus there is little point buying CFL-U (gen 8 original) and even less point buying older SKL/KBL-U.

In light of the above – Ryzen2 mobile is a compelling choice especially as it comes at a (much) lower price-point: its competition is really only the very latest WHL-U i5/i7 which do not come cheap – with most vendors still selling CFL-U and even KBL-U inventory. The only issue is the small choice of laptops available with it – hopefully the vendors (Dell, HP, etc.) will continue to release more versions especially with Ryzen 3 mobile.

In a word: Highly Recommended!

Please see our other articles on:

Intel Core i9 9900K CofeeLake-R Review & Benchmarks – 2-channel DDR4 Cache & Memory Performance

What is “CofeeLake-R” CFL-R?

It is the “refresh” (updated) version of the 8th generation Intel Core architecture (CFL) – itself a minor stepping of the previous 7th generation “KabyLake” (KBL), itself a minor update of the 6th generation “SkyLake” (SKL). While ordinarily this would not be much of an event – this time we do have more significant changes:

  • Patched vulnerabilities in hardware: this can help restore I/O workload performance degradation due to OS mitigations
    • Kernel Page Table Isolation (KPTI) aka “Meltdown” – Patched in hardware
    • L1TF/Foreshadow – Patched in hardware
    • (IBPB/IBRS) “Spectre 2” – OS mitigation needed
    • Speculative Store Bypass disabling (SSBD) “Spectre 4” – OS mitigation needed
  • Increased core counts yet again: CFL-R top-end now has 8 cores, not 6.

Intel CPUs bore the brunt of the vulnerabilities disclosed at the start of 2018 with “Meltdown” operating system mitigations (KVA) likely having the biggest performance impact in I/O workloads. While modern features (e.g. PCID (process context id) acceleration) could help reduce performance impact somewhat on recent architectures (4th gen and newer) the impact can still be significant. The CFL-R hardware fixes (thus not needing KVA) may thus prove very important.

On the desktop we also see increased cores (again!) now up to 8 (thus 16 threads with HyperThreading) – double what KBL and SKL brought and matching AMD.

We also see increased clocks, mainly Turbo, but this still allows 1 or 2 cores to boost clocks higher than CFL could and thus help workloads not massively threaded. This can improve responsiveness as single tasks can be run at top speed when there is little thread utilization.

While rated TDP has not changed, in practice we are likely to see increased “real” power consumption especially due to higher clocks – with Turbo pushing power consumption even higher – close to SKL/KBL-X.

In this article we test CPU Core performance; please see our other articles on:

Hardware Specifications

We are comparing the top-of-the-range Gen 8 Core i7 (8700K) with previous generation (6700K) and competing architectures with a view to upgrading to a mid-range high performance design.

CPU Specifications Intel i9-9900K CofeeLake-R Intel i7-8700K CofeeLake AMD Ryzen2 2700X Pinnacle Ridge Intel i9-7900X SkyLake-X Comments
L1D / L1I Caches 8x 32kB 8-way / 8x 32kB 8-way 6x 32kB 8-way / 6x 32kB 8-way 8x 32kB 8-way / 8x 64kB 8-way 10x 32kB 8-way / 10x 32kB 8-way No L1D/I changes, Ryzen’s L1I is twice as big.
L2 Caches 8x 256kB 4-way 6x 256kB 4-way 8x 512kB 8-way 10x 1MB 16-way No L2 changes, Ryzen’s L2 is twice as big again.
L3 Caches 16MB 16-way 12MB 16-way 2x 8MB 16-way 2x 8MB 16-way L3 has also increased with no of cores, and now matches Ryzen.
TLB 4kB pages
64 4-way / 64 8-way / 1536 6-way 64 4-way / 64 8-way/ 1536 6-way 64 full-way 1536 8-way 64 4-way / 64 8-way / 1536 6-way No TLB changes.
TLB 2MB pages
8 full-way / 1536 6-way 8 full-way / 1536 6-way 64 full-way 1536 2-way 8 full-way / 1536 6-way No TLB changes.
Memory Controller Speed (MHz) 1200-5000 1200-4400 1333-2667 1200-2700 The uncore (memory controller) runs at faster clock due to higher rated clock but not a lot in it.
Memory Data Speed (MHz)
3200 3200 2667 3200 CFL/R can easily run at 3200Mt/s while KBL/SKL were not as reliable. We could not get Ryzen past 2667 while it does support 2933.
Memory Channels / Width
2 / 128-bit 2 / 128-bit 2 / 128-bit 2 / 128-bit All have 128-bit total channel width.
Memory Bandwidth (GB/s)
50 50 42 100 Bandwidth has naturally increased with memory clock speed but latencies are higher.
Uncore / Memory Controller Firmware
2.6.2 2.6.2 We’re on firmware 2.6.x on both.
Memory Timing (clocks)
16-16-16-36 6-52-25-12 2T 16-16-16-36 6-52-25-12 2T 16-17-17-35 7-60-20-10 2T Timings are very much BIOS dependent and vary a lot.

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, AVX, etc.). CFL-R supports most modern instruction sets (AVX2, FMA3) but not the latest SKL/KBL-X AVX512 nor a few others like SHA HWA (Atom, Ryzen).

Results Interpretation: Higher values (GOPS, MB/s, etc.) mean better performance.

Environment: Windows 10 x64 (1807), latest drivers. 2MB “large pages” were enabled and in use. Turbo / Boost was enabled on all configurations.

Spectre / Meltdown Windows Mitigations: all were enabled as per default (BTI enabled, RDCL/KVA enabled, PCID enabled).

Native Benchmarks Intel i9-9900K CofeeLake-R Intel i7-8700K CofeeLake AMD Ryzen2 2700X Pinnacle Ridge Intel i9-7900X SkyLake-X Comments
CPU Multi-Core Benchmark Total Inter-Core Bandwidth – Best (GB/s) 70.7 [+28%] 52.5 55.3 86 CFL-R finally overtakes Ryzen2 in inter-core bandwidth with almost 30% more bandwidth.
CPU Multi-Core Benchmark Total Inter-Core Bandwidth – Worst (GB/s) 15.4 [-1%] 15.5 6.35 25.7 In worst-case pairs on Ryzen2 must go across CCXes – unlike Intel’s CPUs – thus CFL can muster over 2x more bandwidth in this case.
CFL-R manages good bandwidth improvement with its 2  extra cores allowing it to dominate Ryzen  2; worst-case bandwidth does not improve as the inter-core connector has remained the same
CPU Multi-Core Benchmark Inter-Unit Latency – Same Core (ns) 13.4 [-7%] 14.4 13.5 15 With its faster clock, CFL-R manages lower inter-core latency with 7% drop.
CPU Multi-Core Benchmark Inter-Unit Latency – Same Compute Unit (ns) 43.7 [-3%] 45 40 75 Within the same unit, Ryzen2 is again faster than CFL/R.
CPU Multi-Core Benchmark Inter-Unit Latency – Different Compute Unit (ns) 115 Obviously going across CCXes is slow, about 3x slower which needs careful thread scheduling.
The multiple CCX designof Ryzen 2 still presents some challenges to programmers requiring threads to be carefully scheduled – thus the unified CFL-R just like CFL before it enjoys lower latencies throughout.
Aggregated L1D Bandwidth (GB/s) 1890 [+39%] 1630
854 2220 Intel’s wide L1D in CFL/R means almost 2x more bandwidth than Ryzen 2.
Aggregated L2 Bandwidth (GB/s) 618 [+8%] 571 720 985 But Ryzen2’s L2 caches are not only twice as big but also very wide – CFL/R surprisingly cannot beat it.
Aggregated L3 Bandwidth (GB/s) 326 [=] 327 339 464 Ryzen’s 2 L3 caches also provide good bandwidth matching CFL’s unified L3 cache.
Aggregated Memory (GB/s) 35.5 [=] 35.6 32.2 70 Running at 3200Mt’s obviously CFL enjoys higher bandwidth than Ryzen2 at 2667Mt’s but somehow the latter has better efficiency.
Nothing much has changed in CFL/R vs. old SKL/KBL thus while L1 caches are wide and thus fast – the L2, L3 are not as impressive and the memory controller while competitive it does not seem as efficient as Ryzen2 but is more stable at high data rates allowing for higher bandwidth.
Data In-Page Random Latency (ns) 17.5 (3-10-21) 17.4 (4-11-20) [-73%] 63.4 (4-12-31) 25.5 (4-13-30) While clock latencies have not changed w.s. old KBL/SKL, CFLR enjoys lower latencies due to higher data rates. Ryzen2 has problems here.
Data Full Random Latency (ns) 54.3 (3-10-36) 53.4 (4-11-42) [-30%] 76.2 (4-12-32) 74 (4-13-62) Out-of-page clock latencies have increased but still overall lower. Ryzen2 has almost caught up here.
Data Sequential Latency (ns) 3.8 (3-10-11) 3.8 (4-11-12) 3.3 (4-6-7) 5.3 (4-12-12) With sequential access, Ryzen2 is now faster as CFL/R’s clock latencies have not changed.
CFL-R does not improve over CFL (same memory controller) is lucky here as even Ryzen2 still has high latencies in random accesses (either in-page or full range) but manages to be faster with sequential access. Intel will need to improve going forward as clock latencies while good have really not improved at all.
Code In-Page Random Latency (ns) 8.6 (2-9-19) 8.7 (2-10-21) 13.8 (4-9-24) 11.8 (4-14-25) Code clock latencies also have not changed and again and while Ryzen2 performs a lot better, CFL/R manage to be ~35% faster.
Code Full Random Latency (ns) 60.1 (2-9-48) 59.8 (2-10-48) 85.7 (4-14-49) 83.6 (4-15-74) Out-of-page clock latencies also have not changed and here CFL/R is 20% faster over Ryzen2.
Code Sequential Latency (ns) 4.3 (2-3-8) 4.5 (2-4-10) 7.4 (4-12-20) 6.8 (4-7-11) Ryzen2 is competitive but again CFL/R manages to be almost 40% faster.
CFL/R does not improve over CFL but still dominates here and enjoys 30-40% less latency over Ryzen2 but the latter has improved a lot in time.
Memory Update Transactional (MTPS) 73.3 [+36%] 54 5 59 Finally all top-end Intel CPUs have HLE enabled and working and thus enjoy huge performance increase.
Memory Update Record Only (MTPS) 53.4 [+41%] 38 4.58 59 Nothing much changes here. CFL-R can do over 40% more transactions.

CFL-R does not really perform any different cache/memory wise vs. old CFL as the caches and memory controller are unchanged.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

CFL-R just adds more cores, thus enjoys higher aggregated L1D/L2 bandiwdths vs CFL but the L3 is still disappointing – especially as now it has to feed 33% more cores/threads (8/16 vs 6/12). Latencies (in clocks) do not change either but as it can clock higher they do decrease in real terms (ns).

The memory controller is the very same (even running same firmware) thus performs the same though now it has to feed 33% more cores/threads (8/16 vs 6/12) thus when all cores/threads are used the aggregated bandwidth falls due to extra contention. In fairness Ryzen2 has the same issue (too many cores/threads for too little bandwidth) thus SKL/KBL-X is where you should be looking for more bandwidth.