Adrian Silasi – SiSoftware

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

By Adrian Silasi | 25th April 2019 - 12:30 pm |25th April 2019 Releases, Updates

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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 in recent Windows 10 / Server 2019 updates
- Kernel Address Table Import Optimisation status (as above)
- L1TF – L1 data terminal fault 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 but KATI enabled**
	Kernel Retpoline Speculation Control – no Kernel Address Table Import Optimisation – Enabled *(Note CPU does not require RDCL mitigation thus no KVA, L1TF required)*
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 KVA (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.
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 KVA 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

By Adrian Silasi | 11th April 2019 - 10:58 pm |12th April 2019 Reviews

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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

	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.
	Mandel FP32/Single (Mpix/s)	476	478	865	1350 [+56%]	Vega rules FP32 and is over 50% faster than GT3.
	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.
	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.

	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.
	Crypto AES-128 (GB/s)	1	1.08	1.52	3.3 [+2.17x]	Nothing changes here, Vega is over 2.2x faster.

	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.
	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.
	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.

	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.
	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.
	Black-Scholes double/FP64 (MOPT/s)	142	154	281	185 [-33%]	Switching to FP64 precision, Vega is 33% slower than GT3.
	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.
	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.
	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.
	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.
	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.
	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.

	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.
	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.
	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%.
	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.
	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.
	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).
	HNBODY (GFLOPS) half/FP16	270	284	449	623 [+39%]	16-bit precision still benefits N-Body and here Vega is 40% faster than GT3.
	SNBODY (GFLOPS) float/FP32	162	181	291	537 [+85%]	Back to FP32 and Vega has a pretty large 85% lead – almost 2x GT3.
	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.

	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.
	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.
	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.
	Sharpen (5×5) Filter single/FP32 (MPix/s)	100	107	144	157 [+9%]	Same algorithm but more shared data reduces the gap to 9%.
	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.
	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%.
	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.
	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.
	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.
	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.
	Oil Painting Quantise Filter half/FP16 (MPix/s)	8.55	9.32	9.22	<BSOD>	This test would cause BSOD; we are investigating.
	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.
	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.
	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.
	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.
	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

	Internal Memory Bandwidth (GB/s)	12.17	21.2	24	27.32 [+14%]	With higher speed DDR4 memory, Vega has 14% more 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.
	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).

	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.
	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.
	Global (Sequential Access) Latency (ns)	156	158	213	201 [-6%]	Sequential access latencies are less than competition by 6%.
	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.
	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.
	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.
	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.
	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

By Adrian Silasi | 11th April 2019 - 6:13 pm |12th April 2019 Reviews

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What is “Ryzen2” ZEN+ Mobile?

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

	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.
	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.

	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.
	Inter-Unit Latency – Same Compute Unit (ns)	46 [-23%]	61	54	56	Within the same compute unit (shareL3) Ryzen2 again yields 23% lower latencies.
	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

By Adrian Silasi | 9th April 2019 - 3:54 pm |12th April 2019 Reviews

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What is “Ryzen2” ZEN+ Mobile?

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?

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

	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.
	Native Dhrystone Long (GIPS)	102 [-4%]	51	74	106	With a 64-bit integer workload – the difference drops to 4%.
	Native FP32 (Float) Whetstone (GFLOPS)	79 [+18%]	39	45	67	Somewhat surprisingly, Ryzen2 is almost 20% faster than CFL-U here.
	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.

	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.
	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.
	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.
	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%.
	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.
	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.

	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.
	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.
	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.
	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.
	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.

	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.
	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.
	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.
	Binomial double/FP64 (kOPT/s)	19.5 [-7%]	10.1	11.4	21	With FP64 code Ryzen2 drops back from its previous win.
	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.
	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.

	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.
	DGEMM (GFLOPS) double/FP64	47.2 [-6%]	44.2	31.7	50.5	With FP64 vectorised code, Ryzen2 drops down to 6% slower.
	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.
	DFFT (GFLOPS) double/FP64	4 [-7%]	3.23	3.95	4.3	With FP64 code, Ryzen2 does better and is just 7% slower.
	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.
	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.

	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.
	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.
	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.
	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.
	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.
	Oil Painting Quantise Filter (MPix/s)	9.38 [-38%]	7.21	7.63	15.09	Again we see Ryzen2 fall behind CFL-U.
	Diffusion Randomise (XorShift) Filter (MPix/s)	660 [-53%]	730	764	1394	With integer AVX2 workload, Ryzen2 falls behind even SKL/KBL-U.
	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

	.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.
	.Net Dhrystone Long (GIPS)	22 [+34%]	9.24	12.1	16.4	64-bit integer workloads also favour Ryzen2, still 35% faster.
	.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.
	.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%.

	.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.
	.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.
	.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.
	.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 Dhrystone Integer (GIPS)	222 [+13%]	119	150	196	We start JVM integer performance with a 13% lead over CFL-U.
	Java Dhrystone Long (GIPS)	208 [+12%]	101	131	185	Nothing much changes with 64-bit integer workload – Ryzen2 still faster.
	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 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 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 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 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 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

By Adrian Silasi | 18th March 2019 - 2:37 pm |18th March 2019 Reviews

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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

	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.
	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

	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.
	Inter-Unit Latency – Same Compute Unit (ns)	43.7 [-3%]	45	40	75	Within the same unit, Ryzen2 is again faster than CFL/R.
	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.

SiSoftware Sandra Titanium (2018) SP3a/b Update: Pushing the Limits

By Adrian Silasi | 8th February 2019 - 1:54 am |8th April 2019 Releases, Updates

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Note: This article originally announced SP3a (28.45); it has since been updated to SP3b (28.49).

We are pleased to announce SP3b (version 28.49 for Sandra Titanium (2018) with updated hardware and software support:

Sandra Titanium (2018) Press Release

Sandra has always pushed the limits of hardware, optimising the workload based on the capablities of the device (compute performance, memory/storage size, etc.) ensuring that both low-end devices and high-end devices are used to their best of their capability.

This new version pushes the workload even higher with better scaling across all GPGPU benchmarks allowing low-end devices to work (e.g. integrated graphics, emulation on CPU) and high-end professional GPGPU accelerators with very fast, very large memory.

GPGPU Benchmarks

All Benchmarks: increased workload size on all benchmarks to up to maximum device capacity.
FP16/half optimisations for AMD Vega and Radeon VII (vectorisation)*
FP16/half optimisations for nVidia Volta/Turing (half2) [CUDA 10]
Workgroup and workload optimisations for AMD Vega and Radeon VII*
Updated DirectX and OpenGL compute to match CUDA and OpenCL
Resolved “out of memory” issues on low-end hardware**
Resolved long running time of CPU test-paths (that the GPGPU test paths are checked against) by enabling SIMD implementation (FFT/GEMM)**

Note: At this time we have not personally tested Radeon VII to confirm improvements.

Note2: Applies to SP3b update.

Hardware Support

Intel Core v8/9 Mobile WhiskyLake (WHL), AmberLake (AML) support (based on public information)

Reviews using Sandra 2018 SP3a/b

Ranker Hardware Results

nVIDIA GeForce GTX 1660 Ti Max-Q Design

Note: FP64 rate on Radeon VII is 1/4 not 1/8 as stated in some places. FP16 rate is 2x with vectorisation, 1x with scalar.

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

Download Sandra Lite

nVidia Titan V/X: FP16 and Tensor CUDA Performance

By Adrian Silasi | 25th January 2019 - 12:53 am |11th March 2019 Reviews

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What is FP16 (“half”)?

FP16 (aka “half” floating-point) is the IEEE lower-precision floating-point representation that has recently begun to be supported by GPGPUs for compute (e.g. Intel EV9+ Skylake GPU, nVidia Pascal/Turing) and soon by CPUs (BFloat16). While originally meant for mobile devices in order to reduce memory and compute requirements – it also allows workstations/servers to handle deep neural-network workloads that have exploded in both size and compute power.

While not all algorithms can use such low precision and thus may require parts to use normal precision, nevertheless FP16 can still be used in many instances and thus needs to be implemented and benchmarked.

In addition we see the introduction of specialised compute engines that specifically support FP16 (and not higher precision like FP32/FP64) like “Tensor Engines”.

What are “Tensors”?

A tensor engine (hardware accelerator) is a specialised processing unit that accelerates matrix multiplication in hardware, in this case the latest nVidia GPGPU architectures (Pascal/Turing). While the former was targeted to workstations (Titan), the latter powers all consumer (series 2000) graphics cards – thus it has entered the mainstream. In addition, the speed restrictions (e.g. Maxwell FP16 processing speed was limited to 1/64 FP32 speed) have been lifted.

While it is used in other algorithms, it is intended to be used to accelerate neural networks (so called “AI”) that are now being used in mainstream local workloads like image/video processing (scaling, de-noising, etc.), games (anti-aliasing, de-noising when using with ray-tracing, bots/NPCs, procedural world-building, etc.).

In this article we are investigating both FP16/half performance vs. standard FP32 as well as the performance improvement when using tensors.

nVidia Titan X : Pascal GPGPU performance in CUDA and OpenCL

FP16/half Performance

We are testing GPGPU performance of the GPUs in CUDA as it supports both FP16/half operations and tensors; hopefully both OpenCL and DirectX will also be updated to support both FP16/half (compute) and tensors.

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

Environment: Windows 10 x64, latest nVidia drivers (Jan 2019). Turbo / Dynamic Overclocking was enabled on all configurations.

For image processing, Titan V brings big performance increases from 50% to 4x (times) faster than Titan X a big upgrade. If you are willing to drop to FP16 precision, then it is an extra 50% to 2x faster again – while naturally FP16 is not really usable on the X. With potential 8x times better performance Titan V powers through image processing tasks.

Processing Benchmarks		nVidia Titan X FP32	nVidia Titan X FP16/half	nVidia Titan V FP32	nVidia Titan V FP16/half	Comments

	Mandel (Mpix/s)	17.82	0.244	22.68	33.86 [+49%]	In a purely compute-heavy algorithm FP16 can bring 50% improvement.
When F16 precision is sufficient, compute heavy algorithms improve greatly on the unlocked Titan V; we see that on the previous Titan X FP16 is not worth using as its performance is way too low.

	Black-Scholes (MOPT/s)	11.32	0.726	18.57	37 [+99%]	B/S benefits greatly from FP16 both through decreased memory storage and low precision compute.
	Binomial (kOPT/s)	2.38	2.34	4.2	4.22 [+1%]	Binomial requires higher precision for the results to make sense thus sees almost no benefit.
	Monte-Carlo (kOPT/s)	5.82	0.137	11.92	12.61 [+6%]	M/C also uses thread shared data but read-only but still requires higher precision.
For financial workloads, FP16 is generally too low and most parts of the algorithm do need to be performed in FP32; we can still use FP16 as data storage but the heavy compute sees little benefit. When FP16 can be deployed as in B/S then we see far higher performance benefits.

	GEMM (GFLOPS)	6	0.191	11	15.8 [+44%] / 42 Tensor [+4x]	Here we see the power of the tensor cores – in FP16 Titan V is 4 times faster! Normal compute is still almost 50% faster a good result.
	FFT (GFLOPS)	0.227	0.078	0.617	0.962 [+56%]	FFT also benefits from FP16 due to reduced memory pressure.
	NBODY (GFLOPS)	5.72	0.061	7.79	8.25 [+6%]	N-Body simulation needs some parts in FP32 thus does not benefit as much.
The new Tensor cores show their power in FP16 GEMM – we see 4x (times) higher performance than in FP32 which can go a long way in making neural network processing much faster not to mention 1/2 memory size requirement of FP32.

	Blur (3×3) Filter (MPix/s)	18.41	1.65	27	27.53 [+2%]	Surprisingly 3×3 does not seem to benefit much from FP16 processing performance.
	Sharpen (5×5) Filter (MPix/s)	5	0.618	9.29	14.66 [+58%]	Same algorithm but more shared brings over 50% performance improvement.
	Motion-Blur (7×7) (MPix/s)	5.08	0.332	9.48	14.39 [52%]	Again same algorithm but even more data shared also brings around 50% better performance.
	Edge Detection (2*5×5) Sobel Filter (MPix/s)	4.8	0.316	9	13.36 [+48%]	Still convolution but with 2 filters – similar 50% improvement.
	Noise Removal (5×5) Median Filter(MPix/s)	37.14	7.4	112.5	207.37 [+84%]	Median filter benefits greatly from FP16 processing, it’s almost 2x faster.
	Oil Painting Quantise Filter (MPix/s)	12.73	28	42.38	141.8 [+235%]	Without major processing, quantisation benefits even more from FP16, it’s over 3x faster.
	Diffusion Randomise (XorShift) Filter (MPix/s)	21	6.43	24.14	24.58 [+2%]	This algorithm is 64-bit integer heavy thus shows almost no benefit.
	Marbling Perlin Noise 2D Filter (MPix/s)	0.305	0.49	0.8125	2 [+148%]	One of the most complex and largest filters, FP16 makes it over 2.5x faster.
FP16/half brings huge performance improvement in image processing as long as the results are acceptable – again some parts have to use higher precision (FP32) in order to prevent artifacts. Convolution can be implemented through matrix multiplication thus would benefit even more from Tensor core hardware acceleration.

Final Thoughts / Conclusions

FP16/half support when unlocked can greatly benefit many algorithms – if the lower precision is acceptable: in general performance improves by about 50% – though in some cases it can reach 200%.

When using the new Tensor cores – performance improves hugely: in GEMM we see 4x performance improvement (vs. FP32). It thus makes great sense to modify algorithms (like convolution) to use matrix multiplication and thus the Tensor cores – which will greatly accelerate image processing and neural networks. With the new nVidia 2000 series – this kind of performance is available in the mainstream right now and is pretty amazing to see.

Expect to see similar hardware accelerator units from both GPGPUs and soon CPUs with AVX512-VNNI as well as FP16 processing support (BFloat16) that will allow multi-core wide-SIMD CPUs to be competitive.

SiSoftware Sandra Titanium (2018) SP3 Update

By Adrian Silasi | 24th January 2019 - 7:43 pm |8th April 2019 Releases, Updates

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We are pleased to announce SP3 (version 28.40 for Sandra Titanium (2018) with updated hardware and software support:

Sandra Titanium (2018) Press Release

GPGPU Benchmarks:

Enable FP16/half support in CUDA benchmarks*
Enable low-precision FP32 shader support in DirectX Compute benchmarks
Enable Tensor support for CUDA/GEMM/FP16

Note: FP16 is already supported by the OpenCL and DirectX compute benchmarks.

GPGPU Processing, Video Shader Benchmarks:

FP16/half-precision performance* (if supported) is included in the aggregate scores. This better reflects the performance of new GPGPUs that natively support FP16/half processing.
Tensors code-path used by default where applicable.

Note: FP16 is supported by all benchmarks: DirectX, OpenGL, OpenCL and now CUDA.

Reviews using Sandra 2018 SP3

nVidia Titan V/X: FP16 and Tensor CUDA Performance

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

Download Sandra Lite

Intel Core i9 9900K CofeeLake-R Review & Benchmarks – CPU 8-core/16-thread Performance

By Adrian Silasi | 21st November 2018 - 12:00 am |18th March 2019 Reviews

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What is “CofeeLake-R” CFL-R?

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.

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.

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 i9 (9900K) with previous generation (8700K) 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
Cores (CU) / Threads (SP)	8C / 16T	6C / 12T	8C / 16T	10C / 20T	We have 33% more cores matching Ryzen and close to mainstream SKL-X!
Speed (Min / Max / Turbo)	0.8-3.6-5GHz (8x-36x-50x)	0.8-3.7-4.7GHz (8x-37x-47x)	2.2-3.7-4.2GHz (22x-37x-42x)	1.2-3.3-4.3 (12x-33x-43x)	Single/Dual core Turbo has now reached 5GHz same as 8086K special edition.
Power (TDP)	95W (135)	95W (131)	105W (135)	140W (308)	TDP is the same but overall power consumption likely far higher.
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 change in L1 caches. Just more of them.
L2 Caches	8x 256kB 8-way	6x 256kB 8-way	8x 512kB 8-way	10x 1MB 8-way	No change in L2 caches. Just more of them.
L3 Caches	16MB 16-way	12MB 16-way	2x 8MB 16-way	13.75MB 11-way	L3 has also increased by 33% in line with cores matching Ryzen.
Microcode/Firmware	MU069E0C-9E	MU069E0A-96	MU8F0802-04	MU065504-49	We have a new stepping and slightly newer microcode.

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, AVX, etc.). CFL 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 (1809), 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

	Native Dhrystone Integer (GIPS)	400 [+20%]	291	334	485	In the old Dhrystone integer workload, CFL-R finally beats Ryzen by 20%.
	Native Dhrystone Long (GIPS)	393 [+17%]	296	335	485	With a 64-bit integer workload – nothing much changes.
	Native FP32 (Float) Whetstone (GFLOPS)	236 [+19%]	170	198	262	Switching to floating-point, CFL-R still beats Ryzen by 20% in old Whetstone.
	Native FP64 (Double) Whetstone (GFLOPS)	196 [+16%]	143	169	223	With FP64 nothing much changes.
From integer workloads in Dhrystone to floating-point workloads in Whetstone, CFL-R handily beats Ryzen by about 20% and is also 33-39% faster than old CFL. It’s “king of the hill”.

	Native Integer (Int32) Multi-Media (Mpix/s)	1000 [+74%]	741	574	1590 (AVX512)	In this vectorised AVX2 integer test CFL-R is almost 2x faster than Ryzen
	Native Long (Int64) Multi-Media (Mpix/s)	416 [+122%]	305	187	581 (AVX512)	With a 64-bit AVX2 integer vectorised workload, CFL-R is now over 2.2x faster than Ryzen.
	Native Quad-Int (Int128) Multi-Media (Mpix/s)	6.75 [+16%]	4.9	5.8	7.6	This is a tough test using Long integers to emulate Int128 without SIMD: still CFL-R is fastest.
	Native Float/FP32 Multi-Media (Mpix/s)	927 [+56%]	678	596	1760 (AVX512)	In this floating-point AVX/FMA vectorised test, CFL-R is 56% faster than Ryzen.
	Native Double/FP64 Multi-Media (Mpix/s)	544 [+62%]	402	335	533 (AVX512)	Switching to FP64 SIMD code, CFL-R increases its lead to 612.
	Native Quad-Float/FP128 Multi-Media (Mpix/s)	23.3 [+49%]	16.7	15.6	40.3 (AVX512)	In this heavy algorithm using FP64 to mantissa extend FP128 but not vectorised – CFL-R is 50% faster.
In vectorised SIMD code we know Intel’s SIMD units (that can execute 256-bit instructions in one go) are much more powerful than AMD’s and it shows: Ryzen is soundly beaten by a big 50-100% margin. Naturally it cannot beat its “big brother” SKL-X with AVX512 – which is likely why Intel has not enabled them.

	Crypto AES-256 (GB/s)	17.6 [+9%]	17.8	16.1	23	With AES HWA support all CPUs are memory bandwidth bound; core contention (8 vs 6) means CFL-R scores slightly worse than CFL.
	Crypto AES-128 (GB/s)	17.6 [+9%]	17.8	16.1	23	What we saw with AES-256 just repeats with AES-128.
	Crypto SHA2-256 (GB/s)	12.2 [-34%]	9	18.6	26 (AVX512)	With SHA HWA Ryzen2 powers through but CFL-R is only 34% slower.
	Crypto SHA1 (GB/s)	23 [+19%]	17.3	19.3	38 (AVX512)	Ryzen also accelerates the soon-to-be-defunct SHA1 but the algorithm is less compute heavy allowing CFL-R to beat it.
	Crypto SHA2-512 (GB/s)	9 [+139%]	6.65	3.77	21 (AVX512)	SHA2-512 is not accelerated by SHA HWA, allowing CFL-R to use its SIMD units and be 139% faster.
AES HWA is memory bound and here and CFL-R also enjoys the 3200Mt/s memory – but now feeding 8C / 16T which all contend for the bandwidth. Thus CFL-R does score slighly less than CFL and obviously gets left in the dust by SKL-X with 4 memory channels. Ryzen2 SHA HWA does manage a lonely win but anything SIMD accelerated belongs to Intel.

	Black-Scholes float/FP32 (MOPT/s)	276 [+7%]	207	257	309	In this non-vectorised test CFL-R is just a bit faster than Ryzen2.
	Black-Scholes double/FP64 (MOPT/s)	240 [+10%]	180	219	277	Switching to FP64 code, nothing much changes, CFL-R is 10% faster
	Binomial float/FP32 (kOPT/s)	59.9 [-44%]	47	107	70.5	Binomial uses thread shared data thus stresses the cache & memory system; CFL-R strangely loses by 44%.
	Binomial double/FP64 (kOPT/s)	61.9 [+2%]	44.2	60.6	68	With FP64 code Ryzen2’s lead diminishes, CFL is pretty much tied with it.
	Monte-Carlo float/FP32 (kOPT/s)	56.5 [+4%]	41.6	54.2	63	Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure on the caches; CFL-R is just 4% faster.
	Monte-Carlo double/FP64 (kOPT/s)	44.3 [+8%]	32.9	41	50.5	Switching to FP64 CFL-R increases its lead to 8%.
Without SIMD support, CFL-R relies on its thread count increase (thus matching Ryzen2) to beat Ryzen2 by a small amount and lose in one test. But in a test that AMD used to always win (with Ryzen 1/2) Intel now has the lead.

	SGEMM (GFLOPS) float/FP32	403 [+34%]	385	300	413 (AVX512)	In this tough vectorised AVX2/FMA algorithm CFL-R is 35% faster than Ryzen2
	DGEMM (GFLOPS) double/FP64	269 [+126%]	135	119	212 (AVX512)	With FP64 vectorised code, CFL-R is over 2x faster.
	SFFT (GFLOPS) float/FP32	23.4 [+160%]	24	9	28.6 (AVX512)	FFT is also heavily vectorised (x4 AVX/FMA) but stresses the memory sub-system more CFL-R is over 2x faster.
	DFFT (GFLOPS) double/FP64	11.2 [+41%]	11.9	7.92	14.6 (AVX512)	With FP64 code, CFL-R’s lead reduces to 41%.
	SNBODY (GFLOPS) float/FP32	550 [+96%]	411	280	638 (AVX512)	N-Body simulation is vectorised but many memory accesses to shared data but CFL-R is 2x faster than Ryzen2.
	DNBODY (GFLOPS) double/FP64	172 [+52%]	127	113	195 (AVX512)	With FP64 code CFL’s lead reduces to 50% over Ryzen 2.
With highly vectorised SIMD code CFL-R performs very well – dominating Ryzen2: in some tests it is over 2x faster! Then again CFL did not have any issues here either, Intel is just extending their lead…

	Blur (3×3) Filter (MPix/s)	2270 [+86%]	1700	1220	4540 (AVX512)	In this vectorised integer AVX2 workload CFL-R is amost 2x faster than Ryzen2.
	Sharpen (5×5) Filter (MPix/s)	903 [+67%]	675	542	1790 (AVX512)	Same algorithm but more shared data reduces the lead to 67% still significant.
	Motion-Blur (7×7) Filter (MPix/s)	488 [+61%]	362	303	940 (AVX512)	Again same algorithm but even more data shared reduces the lead to 61%.
	Edge Detection (2*5×5) Sobel Filter (MPix/s)	784 [+73%]	589	453	1520 (AVX512)	Different algorithm but still AVX2 vectorised workload means CFL-R is 73% faster than Ryzen 2.
	Noise Removal (5×5) Median Filter (MPix/s)	78.6 [+13%]	57.8	69.7	223 (AVX512)	Still AVX2 vectorised code but CFL-R stumbles a bit here – but it’s still 13% faster.
	Oil Painting Quantise Filter (MPix/s)	43.4 [+76%]	31.8	24.6	70.8 (AVX512)	CFL-R recovers its dominance over Ryzen2.
	Diffusion Randomise (XorShift) Filter (MPix/s)	4470 [+208%]	3480	1450	3570 (AVX512)	CFL-R (like all Intel CPUs) does very well here – it’s a huge 3x faster.
	Marbling Perlin Noise 2D Filter (MPix/s)	614 [+153%]	448	243	909 (AVX512)	In this final test, CFL-R is over 2x faster than Ryzen 2.

Adding 2 more cores brings additional performance gains (not to mention the higher Turbo clock) over CFL which showed big gains over the old SKL/KBL again within the same (rated) TDP. Intel never had any problem with SIMD code (AVX/AVX2/FMA3) beating Ryzen2 by a large margin (now over 2x faster) but now also wins pretty much all tests.

It is consistently 33-40% faster than CFL (8700K) in line with core/speed increases which bodes well for compute-heavy code; streaming performance can be lower due to increase core contention for bandwidth and here faster (though more expensive) memory would help.

No – it cannot beat its “older big brother” SKL-X with AVX512 – not to mention increased core/thread count as well as memory channels, but in some tests it is competitive.

Software VM (.Net/Java) Performance

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

Environment: Windows 10 x64 (1809), 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).

VM Benchmarks		Intel i9-9900K CofeeLake-R	Intel i7-8700K CofeeLake	AMD Ryzen2 2700X Pinnacle Ridge	Intel i9-7900X SkyLake-X	Comments

	.Net Dhrystone Integer (GIPS)	54.3 [-11%]	41	61	52	.Net CLR integer performance starts off well but CFL-R is 10% slower.
	.Net Dhrystone Long (GIPS)	55.8 [-7%]	41	60	54	With 64-bit integers the gap lowers to 7%.
	.Net Whetstone float/FP32 (GFLOPS)	95.5 [-6%]	78	102	107	Floating-Point CLR performance does not change much, CFL-R is still 6% slower than Ryzen2 despite big gain over old CFL/KBL/SKL.
	.Net Whetstone double/FP64 (GFLOPS)	126 [+8%]	95	117	137	FP64 performance allows CFL-R to win in the end.
Ryzen 2 performs exceedingly well in .Net workloads – but CFL-R can hold its own and overall it is not significantly slower (under 10%) and even wins 1 test out of 4.

	.Net Integer Vectorised/Multi-Media (MPix/s)	144 [+30%]	93.5	111	144	Unlike CFL, here CFL-R is 30% faster than Ryzen2.
	.Net Long Vectorised/Multi-Media (MPix/s)	139 [+28%]	93.1	109	143	With 64-bit integer workload nothing much changes.
	.Net Float/FP32 Vectorised/Multi-Media (MPix/s)	499 [+27%]	361	392	585	Here we make use of RyuJit’s support for SIMD vectors thus running AVX/FMA code and CFL-R is 27% faster than Ryzen2.
	.Net Double/FP64 Vectorised/Multi-Media (MPix/s)	274 [+26%]	198	217	314	Switching to FP64 SIMD vector code – still running AVX/FMA – CFL-R is still faster.
We see a big improvement in CFL-R even against old CFL: this allows it to soundly beat Ryzen 2 (which used to win this test) by about 30%, a significant margin. It is possible the hardware fixes (“Meltdown”) are having an effect here.

	Java Dhrystone Integer (GIPS)	614 [+7%]	557	573	877	Java JVM performance starts well with a 7% lead over Ryzen2.
	Java Dhrystone Long (GIPS)	644 [+16%]	488	553	772	With 64-bit integers, CFL-R doubles its lead to 16%.
	Java Whetstone float/FP32 (GFLOPS)	143 [+9%]	101	131	156	Floating-point JVM performance is similar – 9% faster.
	Java Whetstone double/FP64 (GFLOPS)	147 [+6%]	103	139	160	With 64-bit precision the lead drops to 6%.
While CFL-R improves by a good amount over CFL (which itself improved greatly over KBL/SKL) and now beats Ryzen2 by a good margin 7-16%.

	Java Integer Vectorised/Multi-Media (MPix/s)	147 [+30%]	100	113	140	Without SIMD acceleration we still see CFL-R 30% than Ryzen2 in this integer workload.
	Java Long Vectorised/Multi-Media (MPix/s)	142 [+41%]	89	101	152	Changing to 64-bit integer increases the lead to 41%.
	Java Float/FP32 Vectorised/Multi-Media (MPix/s)	91 [-6%]	64	97	98	With floating-point non-SIMD accelerated Ryzen2 is faster.
	Java Double/FP64 Vectorised/Multi-Media (MPix/s)	93 [+3%]	64	90	99	With 64-bit floatint-point precision CFL-R is back on top by just 3%.
With compute heavy vectorised code but not SIMD accelerated, CFL-R is still faster or at least ties with Ryzen2.

CFL-R now beats or at least matches Ryzen2 in VM tests the latter used to easily win. It may not have the lead native SIMD vectorised code allows it – but has no trouble keeping up – unlike its older SKL/KBL “brothers”.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

While Intel had finally increased core counts with CFL (after many years) in light of new competition from AMD with Ryzen/2 – it still relied on fewer but more powerful (at least in SIMD) cores to compete. CFL-R finally brings core (8 vs 8) and thread (16 vs 16) parity with the competition to ensure domination.

And with few exceptions – that’s what it has achieved – 9900K is the fastest desktop CPU at this time (November 2018) and if you can afford it you can upgrade on the same, old, 200-series mainboards (though naturally 300-series is recommended). The performance improvement (33-40% over 8700K) is pretty significant to upgrade – again if you can afford it – considering it is a “mere refresh”.

The “Meltdown” fixes in hardware are also likely to bring big improvement in I/O workloads – or to be precise – restore the performance loss of OS mitigations (KVA) that have been deployed this year (2018). Still, in very rough terms, now you don’t have to decide between “speed” and “security” – though perhaps KVA should be used by default just in case any CPU (not just Intel) leaks information between user/kernel spaces by a yet undiscovered side-channel vulnerability.

But despite the “i9” moniker – don’t think you’re getting a workstation-class CPU on the cheap: SKL-X not only (still) has more cores/threads and 2x memory channels but also supports AVX512 beating it soundly. It will also be refreshed soon – sporting the same “Meltdown” in-hardware fixes. But again considering the costs (almost 2x) CFL-R is likely the performance/price winner on most workloads.

For now, on the desktop, the 9900K is “king of the hill”!

SiSoftware Sandra Titanium (2018) SP2c Update

By Adrian Silasi | 14th November 2018 - 9:29 pm |8th April 2019 Releases, Updates

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We are pleased to announce SP2c (version 28.34 for Sandra Titanium (2018) with updated hardware and software support:

Sandra Titanium (2018) Press Release

Hardware Information
- Ryzen 1/2 & Threadripper: fix crash when Hyper-V / “Windows Core Isolation – Memory Integrity” (VBS) – are enabled
- Ryzen 2 & Threadripper: enabled display of Core temperature, voltage, current, power usage

Reviews using Sandra 2018 SP2c

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

Download Sandra Lite

Black Friday 2018 Deal