Author Archives: Adrian Silasi

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:

• AMD Ryzen2 3900X Review & Benchmarks – CPU 12-core/24-thread Performance
• AMD Ryzen2 3700X Review & Benchmarks – Cache and Memory Performance

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.

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
	Native Dhrystone Integer (GIPS)	336 [=]	334	400	485	We start with no improvement over ZEN+
	Native Dhrystone Long (GIPS)	339 [=]	335	393	485	With a 64-bit integer workload nothing much changes.
	Native FP32 (Float) Whetstone (GFLOPS)	202 [+2%]	198	236	262	Floating-point performance does not change delta either – only 2% faster
	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?
	Native Integer (Int32) Multi-Media (Mpix/s)	1023 [+78%]	574	985	1590	ZEN2 is ~80% faster than ZEN+ despite what we’ve seen before.
	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.
	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.
	Native Float/FP32 Multi-Media (Mpix/s)	100 [+68%]	596	914	1760	In this floating-point AVX/FMA vectorised test, ZEN2 is ~70% faster.
	Native Double/FP64 Multi-Media (Mpix/s)	618 [+84%]	335	535	533	Switching to FP64 SIMD code, ZEN2 is now ~90% faster than ZEN+
	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.
	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.
	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.
	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+
	Crypto SHA1 (GB/s)	20.41 [+6%]	19.3	22.9	38	The less compute-intensive SHA1 does not change things due to acceleration.
	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.
	Black-Scholes float/FP32 (MOPT/s)	–	257	276	309	–
	Black-Scholes double/FP64 (MOPT/s)	229 [+5%]	219	238	277	Switching to FP64 code, ZEN2 is just 5% faster.
	Binomial float/FP32 (kOPT/s)	–	107	59.9	70.5	Binomial uses thread shared data thus stresses the cache & memory system;
	Binomial double/FP64 (kOPT/s)	57.98 [-4%]	60.6	61.6	68	With FP64 code ZEN2 is 4% slower.
	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;
	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.
	SGEMM (GFLOPS) float/FP32	263 [-12%]	300	375	413	In this tough vectorised algorithm ZEN2 is strangely slower.
	DGEMM (GFLOPS) double/FP64	193 [+63%]	119	209	212	With FP64 vectorised code, ZEN2 comes back to be over 60% faster.
	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.
	DFFT (GFLOPS) double/FP64	11.16 [+41%]	7.92	11.21	14.6	With FP64 code, ZEN2 is ~40% faster.
	SNBODY (GFLOPS) float/FP32	612 [+2.2x]	280	557	638	N-Body simulation is vectorised but fewer memory accesses; ZEN2 is over 2x faster.
	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.
	Blur (3×3) Filter (MPix/s)	2049 [+42%]	1440	2560	4880	In this vectorised integer workload ZEN2 starts over 40% faster than ZEN+.
	Sharpen (5×5) Filter (MPix/s)	950 [+52%]	627	1000	1920	Same algorithm but more shared data makes ZEN2 over 50% faster.
	Motion-Blur (7×7) Filter (MPix/s)	495 [+52%]	325	519	1000	Again same algorithm but even more data shared still 50% faster
	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.
	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.
	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.
	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.
	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

• AMD Ryzen 9 3900X 12C/24T
• AMD Ryzen 7 3700X 8C/16T
• AMD Ryzen 5 3600X 6C/12T
• AMD Ryzen 5 3600 6C/12T

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…

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. 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

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:

• AMD Ryzen2 3900X Review & Benchmarks – Cache and Memory Performance
• AMD Ryzen2 3700X Review & Benchmarks – CPU 8-core/16-thread Performance

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.

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
	Native Dhrystone Integer (GIPS)	551 [+38%]	334	400	485	Right off Ryzen2 demolishes all CPUs, it is 40% faster than CFL-R!
	Native Dhrystone Long (GIPS)	556 [+41%]	335	393	485	With a 64-bit integer workload nothing much changes.
	Native FP32 (Float) Whetstone (GFLOPS)	331 [+40%]	198	236	262	Floating-point performance does not change delta either – still 40% faster!
	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!
	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!
	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!
	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!
	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!
	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!!!
	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.
	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.
	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%.
	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!
	Crypto SHA1 (GB/s)		19.3	22.9	38	–
	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.
	Black-Scholes float/FP32 (MOPT/s)		257	276	309	–
	Black-Scholes double/FP64 (MOPT/s)	379 [+55%]	219	238	277	Switching to FP64 code, nothing much changes, Ryzen2 55% faster than CFL-R.
	Binomial float/FP32 (kOPT/s)		107	59.9	70.5	Binomial uses thread shared data thus stresses the cache & memory system;
	Binomial double/FP64 (kOPT/s)	95.73 [+55%]	60.6	61.6	68	With FP64 code Ryzen2 is still 55% faster!
	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;
	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!
	SGEMM (GFLOPS) float/FP32		300	375	413	In this tough vectorised algorithm Ryzen2.
	DGEMM (GFLOPS) double/FP64	212 [+1%]	119	209	212	With FP64 vectorised code, Ryzen2 matches CFL-R and SKL-X.
	SFFT (GFLOPS) float/FP32		9	22.33	28.6	FFT is also heavily vectorised but stresses the memory sub-system more;
	DFFT (GFLOPS) double/FP64	12.69 [+13%]	7.92	11.21	14.6	With FP64 code, Ryzen2 is 13% faster than CFL-R.
	SNBODY (GFLOPS) float/FP32		280	557	638	N-Body simulation is vectorised but fewer memory accesses;
	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.
	Blur (3×3) Filter (MPix/s)	3056 [+20%]	1440	2560	4880	In this vectorised integer workload Ryzen2 is 20% faster than CFL-R.
	Sharpen (5×5) Filter (MPix/s)	1499 [+50%]	627	1000	1920	Same algorithm but more shared data makes Ryzen2 50% faster!
	Motion-Blur (7×7) Filter (MPix/s)	767 [+48%]	325	519	1000	Again same algorithm but even more data shared still 50% faster
	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.
	Noise Removal (5×5) Median Filter (MPix/s)	136 [+74%]	72.1	78	221	Still vectorised code now Ryzen2 is 70% faster.
	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.
	Diffusion Randomise (XorShift) Filter (MPix/s)	1384 [-65%]	1000	4000	4070	With integer workload, Intel CPUs seem to do much better.
	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

• AMD Ryzen 9 3900X 12C/24T
• AMD Ryzen 7 3700X 8C/16T
• AMD Ryzen 5 3600X 6C/12T
• AMD Ryzen 5 3600 6C/12T

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…