SiSoftware Sandra 20/20 (2020) SP1a Released

Update November 25th: Released patch (version 30.24) to add further hardware and software support.

Update October 24th: Released patch (version 30.21) to corrrect Windows 7 / Server 2008/R2 run-time issues.

We are pleased to release SP1a (version 30.24) update for 20/20 (2020) with the following updates:

Sandra 20/20 (2020) Press Release

  • Hardware Support:
    • AMD Ryzen2 (series 3000 Matisse), Stoney Ridge updated support
    • Intel Cascade Lake (CSL), Comet Lake (CML), Cannon Lake (CNL), Ice Lake (ICL) updated support
  • CPU Benchmarks:
    • Tools (Visual C++ compiler 2019) Update
  • GPGPU Benchmarks:
    • CUDA: Updated SDK 10.2/10.1
    • OpenCL: Updated SDK support

Reviews using Sandra 20/20:

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

Intel Core i9 10980X (Cascade Lake) Review & Benchmarks – CPU 18-core/36-thread AVX512 Performance

Intel Skylake-X Core i9

What is “Cascade Lake (CSL-X)”?

It is one of the 10th generation Core X-Series  arch (CSL-X) from Intel – the latest revision of the “Skylake-X” (SKL-X) arch; it succeeds the older 9900 and 7900 X-Series for HEDT platform. Again, as on the desktop/mobile – it is not the “real” 10th generation Core-X arch – but unlike those platforms – it does actually bring a few more features thus it may be thought as “gen 9.5”:

  • Up to 18C/36T (matching older 7/9-X series)
  • Increased Turbo ratios (e.g. 3.0/4.6GHz for 10980X vs. 2.6/4.2 for 7980X)
  • 4-channel DDR4-2933 (up from 2667) and 256GB (up from 128)
  • AVX512-VNNI, aka “Deep Learning Boost” (DLB) for AI/ML neural networks
  • Hardware fixes/mitigations for vulnerabilities (“Meltdown”, “MDS”, various “Spectre” types)
  • Reduced cost – by 50% ($999 for 10980X vs. $1999 for 7980X)

Unfortunately there are no core-count increases here as the CPUs are still power limited especially with AVX512 loads, but we do have some base and turbo ratio increases that should come in useful. We also get a good increase in (official) memory data-speed support and double memory size support (256GB!) for those big servers.

New instruction sets are always appreciated, though “VNNI” is just an acceleration for twin 8/16-bit integer multiply/accumulate for faster sumation for low-precision quantised (thus integer not floating-point) neural networks. Thus it is not something most algorithms can benefit from: if all you’re going to be using your CPU is AI/ML then great – otherwise it may not be much use.

Dropping the price by a *huge* 50% instantly doubles performance/cost ratio making the CSL-X far more competitive against the new Ryzen 3 / ThreadRipper 3 that have brought big performance gains. Alternatively, it also allows almost doubling the no. of cores/cost – which is a nice upgrade for lower-end users but will not help top-end (12C+) users.

Why review it now?

Until “IceLake” (ICL-X) makes its public debut, “Cascade Lake” is the latest X-Series CPU from Intel you can buy today; despite being just a revision of “Skylake-X” due to its reduced price they may still prove worthy competitors not just in cost but also performance.

As they contain hardware fixes/mitigations for vulnerabilities discovered since original “Skylake-X” has launched (especially “Meltdown” but also various “Spectre” variants), the operating system & applications do not need to deploy slower mitigations that can affect performance (especially I/O, virtualisation) on the older designs. For some algorithms, this may be enough to warrant an upgrade alone!

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

Other articles using Sandra around the Internet:

Hardware Specifications

We are comparing the top-of-the-range Intel ULV with competing architectures (gen 8, 7, 6) as well as competiors (AMD) with a view to upgrading to a mid-range but high performance design.

CPU Specifications Intel Core i9-10980X (CSL-X)
Intel Core i9-9900K (CFL-R)
Intel Core i9-7900X (SKL-X)
AMD Ryzen 9 3950X (R3)
Cores (CU) / Threads (SP) 18C / 36T 8C / 16T 10C / 20T 16C / 32T CSL-X has the most cores thus a big advantage.
Speed (Min / Max / Turbo) 3.0 – 4.6GHz 3.6 – 5.0GHz 3.3-4.3GHz 3.8-4.6GHz CSL-X improves Turbo clock over SKL-X
Power (TDP) 165 – 250W 95 – 135W 140 – 250W 105 – 135W TDP has increased over SKL-X
L1D / L1I Caches 18x 32kB / 18x 32kB 8x 32kB / 8x 32kB 10x 32kB / 10x 32kB 16x 32kB / 16x 32kB No L1 change
L2 Caches 18x 1MB (18MB) 8x 256kB (2MB) 10x 1MB (10MB) 16x 512kB (8MB) No L2 change and good size vs. Ryzen3
L3 Caches 24.75MB 16MB 13.75MB 4x 16MB (64MB) L3/Core stays the same – too little vs. Ryzen3
Microcode (Firmware) MU065507-29 MU069E0C-9E MU065504-49 MU8F7100-11 Just a stepping change of the same core

Native Performance

We are testing native arithmetic, SIMD and cryptography performance using the highest performing instruction sets (AVX2, AVX, etc.). “CometLake” (CML) supports all modern instruction sets including AVX2, FMA3 but not AVX512 (like “IceLake”) or SHA HWA (like Atom, Ryzen).

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 Intel Core i9-10980X (18C/36T) Intel Core i9-9900K (8C/16T) Intel Core i9-7900X (10C/20T) AMD Ryzen 9 3950X (16C/32T) Comments
CPU Arithmetic Benchmark Native Dhrystone Integer (GIPS) 779 [+3%] 400 455 753 CSL-X is just 3% faster than Ryzen3.
CPU Arithmetic Benchmark Native Dhrystone Long (GIPS) 835 [+11%] 393 448 750 With a 64-bit integer workload – the gain is 11%.
CPU Arithmetic Benchmark Native FP32 (Float) Whetstone (GFLOPS) 459 [-1%] 236 262 464 With floating-point workload we have a tie.
CPU Arithmetic Benchmark Native FP64 (Double) Whetstone (GFLOPS) 379 [-4%] 196 223 393 With FP64 it is 4% slower than R3.
Despite its extra 2 cores (18 vs. 16 Ryzen 3), CSL-X pretty much ties with Ryzen3 across both legacy workloads (integer and floating-point). The performance increase vs. the older SKL-X is pretty much inline with the no cores (18 vs. 10) thus no discernible core improvement.
BenchCpuMM Native Integer (Int32) Multi-Media (Mpix/s) 2,341 [+25%] 985 1,430 1,873 In this vectorised integer test, AVX512 allows CSL-X a 25% win.
BenchCpuMM Native Long (Int64) Multi-Media (Mpix/s) 913 [+23%] 414 550 744 With a 64-bit AVX2 integer workload the gain is 23%.
BenchCpuMM Native Quad-Int (Int128) Multi-Media (Mpix/s) 12.92 [=] 6.75 9.58 12.98 This is a tough test using Long integers to emulate Int128 without SIMD it’s a tie.
BenchCpuMM Native Float/FP32 Multi-Media (Mpix/s) 2,676 [+36%] 914 1,740 1,970 In this floating-point vectorised test, CSL-X is 36% faster.
BenchCpuMM Native Double/FP64 Multi-Media (Mpix/s) 1,738 [+45%] 535 1,140 1,200 Switching to FP64 SIMD code, the gain is 45%.
BenchCpuMM Native Quad-Float/FP128 Multi-Media (Mpix/s) 56.4 [+21%] 23 38.7 46.5 In this heavy algorithm using FP64 to mantissa extend FP128 CSL-X is 21% faster.
Thanks to AVX512 support, CSL-X still manages to beat the new Ryzen3 (with its double-width SIMD units) by ~25% on vectorised integer and ~40% on floating-point workloads. With older AVX2/FMA we have a tie despite the extra 2 cores. Again, no appreciable delta vs. the old SKL-X thus without VNNI support there is nothing to see here.
BenchCrypt Crypto AES-256 (GB/s) 33.9 [2.6x] 17.6 34 13 With AES/HWA support CSL-X wins due to 4-channels.
BenchCrypt Crypto AES-128 (GB/s) 33.9 [2.6x] 17.6 34 13 No change with AES128.
BenchCrypt Crypto SHA2-256 (GB/s) 33.5 [+17%] 12 26 28.6 Without SHA/HWA CSL-X still wins.
BenchCrypt Crypto SHA1 (GB/s) 22.9 38 Less compute intensive SHA1 .
BenchCrypt Crypto SHA2-512 (GB/s) 9 21 SHA2-512 is not accelerated by SHA/HWA.
The memory sub-system is crucial here, and with 4-channel DDR4 CSL-X easily wins against Ryzen3 even lacking SHA/HWA support. But again, nothing special vs. old SKL-X at 3200Mt/s that ties with it despite less cores. But if you were to use only “official/non-XMP” clocks then CSL-X would win.
BenchFinance Black-Scholes float/FP32 (MOPT/s) 276 344 With non vectorised workload.
BenchFinance Black-Scholes double/FP64 (MOPT/s) 497 [+61%] 238 277 308 Using FP64 CSL-X is 60% faster than Ryzen3.
BenchFinance Binomial float/FP32 (kOPT/s) 59.9 68.3 Binomial uses thread shared data thus stresses the cache & memory system.
BenchFinance Binomial double/FP64 (kOPT/s) 128 [+3%] 61.6 68 124 With FP64 code CSL-X is just 3% faster.
BenchFinance Monte-Carlo float/FP32 (kOPT/s) 56.5 257 Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure on the caches.
BenchFinance Monte-Carlo double/FP64 (kOPT/s) 178 [-16%] 44.5 103 212 Switching to FP64 CSL-X is 16% slower.
With non-SIMD financial workloads, CSL-X does not always win outright over Ryzen3, sometimes it ties, sometimes it is slower and sometimes faster. It is a big improvement over SKL-X only due to having more cores at the same price.
BenchScience SGEMM (GFLOPS) float/FP32 375 413 In this tough vectorised AVX2/FMA algorithm.
BenchScience DGEMM (GFLOPS) double/FP64 240 [+45%] 209 212 165 With FP64 vectorised code, CSL-X is 45% faster.
BenchScience SFFT (GFLOPS) float/FP32 22.3 28.6 FFT is also heavily vectorised but stresses the memory sub-system more.
BenchScience DFFT (GFLOPS) double/FP64 22.07 [+2.6x] 11.21 14.6 8.56 With FP64 code, CSL-X is over 2x faster.
BenchScience SNBODY (GFLOPS) float/FP32 557 638 N-Body simulation is vectorised but with more memory accesses.
BenchScience DNBODY (GFLOPS) double/FP64 292 [-25%] 171 195 388 With FP64 code CSL-X is 25% slower.
With highly vectorised SIMD code (scientific workloads) using AVX512 – CSL-X easily wins again (up to 50% faster) over Ryzen3 but again nothing special over older SKL-X save its more cores. You will need AVX512 optimised algorithms though to realise these gains, otherwise it is again pretty much a tie vs. Ryzen3.
CPU Image Processing Blur (3×3) Filter (MPix/s) 7,295 [+2.53x] 2,560 4,880 2,883 In this vectorised integer workload CSL-X is 2.5x faster.
CPU Image Processing Sharpen (5×5) Filter (MPix/s) 2,868 [+54%] 1,000 1,880 1,857 Same algorithm but more shared data still 54%.
CPU Image Processing Motion-Blur (7×7) Filter (MPix/s) 1,724 [+80%] 519 1,000 959 Same algorithm but even more data shared 80% faster.
CPU Image Processing Edge Detection (2*5×5) Sobel Filter (MPix/s) 2,285 [+44%] 827 1,500 1,589 Different algorithm but still vectorised workload 44% faster.
CPU Image Processing Noise Removal (5×5) Median Filter (MPix/s) 332 [+91%] 78 221 174 Still vectorised code again almost 2x faster.
CPU Image Processing Oil Painting Quantise Filter (MPix/s) 112 [+2.25x] 42.2 66.7 49.7 Even better improvement here of 2.25x
CPU Image Processing Diffusion Randomise (XorShift) Filter (MPix/s) 3,573 [2.37x] 4,000 3,084 1,505 With integer workload 2.5x faster.
CPU Image Processing Marbling Perlin Noise 2D Filter (MPix/s) 1,162 [+85%] 596 776 627 In this final test again with integer workload CSL-X is 85% faster.

Thanks to AVX512 CSL-X manages to easily beat Ryzen3 in heavily vectorised algorithms (up to 50% faster) and also in memory-bandwidth heavy algorithms (due to its 4-channels memory sub-system). But, despite having 2 extra cores, on older AVX2/FMA we pretty much have a tie – not something we are used to see from Intel.

Also, the improvement over older SKL-X is exactly in-line with the increase in cores (18 vs. 10 here) – thus there are no appreciable core improvements to boost performance. Without specific VNNI-accelerated algorithms, there is no point for SKL-X users to upgrade: you do get more cores for a lot less money but your hardware is also worth a lot less.

It shows how much Ryzen3 has improved (especially due to 256-bit width AVX2/FMA units) and ThreadRipper with 4-channels and even more cores (up to 32!) and threads (up to 64!) should nullify Intel’s AVX512 benefit.

SiSoftware Official Ranker Scores


Final Thoughts / Conclusions

For many, it may be disappointing that we do not have the brand-new “IceLake-X” (ICL-X) now rather than a 3-rd revision “Skylake-X” – and indeed “Cascade Lake-X” (CSL-X) does struggle against newer (and older) competition to make its mark. Without core-count increases and with minor clock increases while still very limited by power at the high end it just does not bring enough improvement. The only exception are workloads (low-precision quantised neural networks) that can use AVX512-VNNI.

Indeed, its “ace card” is the 1/2 price reduction vs. old 7/9-X series and that just about makes it competitive; thankfully existing X299 mainboards can use it through a BIOS/ME update – although boards are still expensive.

But the competition (AMD Ryzen 3, ThreadRipper 3) has much higher performance these days while older CPUs (Ryzen 2 / ThreadRipper 2) have also been greatly reduced in price. They also can use older boards, although to use new features (PCIe 4.0, better power management) new boards are required.

All in all, Intel has done all it can – fix vulnerabilities, greatly reduce the price – to keep the X-Series competitive with current designs – and that it has pretty much achieved. The next X-Series arch better deliver otherwise it will be dead and buried.

In a word: Recommended due to 1/2 price drop

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