AMD Radeon VII: Vega2 GPGPU Performance in OpenCL

What is “Vega2”?

It is the code-name of the updated “Vega” GPU arch(itecture), the last of the GCN (graphics core next) arch (version 5.1) shrinked to 7nm before being replaced by the forthcoming “Navi”. Originally for the professional/workstation high-end market, “Vega2″/”big Vega” designed for compute (scientific, machine learning, etc.) workloads was pressed into service to battle the latest 2000-series “Turing”/RTX competition.

As a result it contains many high-end features not normally found on consumer cards:

  • 1/4 FP64 rate (instead of 1/16 or worse)
  • 16GB HBM2 memory (instead of 8-12)
  • 4096-bit HBM2 memory 1TB/s bandwidth (instead of 400-500)
  • Int8/Int4 support for AI/ML workloads
  • PCIe 4.0 capable but not enabled at this time

See these other articles on GPGPU performance:

Hardware Specifications

We are comparing the middle-range Radeon with previous generation cards and competing architectures with a view to upgrading to a mid-range high performance design.

GPGPU Specifications AMD Radeon VII (Vega2) nVidia Titan V (Volta) nVidia Titan X (Pascal) AMD Vega 56 (Vega1) Comments
Arch Chipset Vega 20 / GCN 5.1 GV100 / 7.0 GP102 / 6.1 Vega 10 / GCN 5.0 A minor revision of Vega1.
Cores (CU) / Threads (SP) 60 / 3840 80 / 5120 28 / 3584 56 / 3584 More CUs than normal Vega but not 64.
SIMD per CU / Width 4 / 16 n/a n/a 4 / 16 Naturally same SIMD count and width
Wave/Warp Size 64 32 32 75 Wave size has always been 2x nVidia.
Speed (Min-Turbo) 1.4 – 1.750 [+21%] (135-1455) 1.531 (139-1910) 1.156 – 1.471 Base clock is ~20% higher and turbo
Power (TDP) 300W [+42%] 300W 250W 210W TDP has gone up by 40%.
ROP / TMU 64 / 256 96 / 320 96 / 224 64 / ROPs and TMUs unchanged
Shared Memory
32kB 48 / 96 kB 48 / 96kB 32kB No shared memory change.
Constant Memory
8GB 64kB 64kB 4GB No dedicated constant memory but large.
Global Memory 16GB HBM2 2Gbps 4096-bit 12GB HBM2 2x850Mbps 3072-bit 12GB GDDR5X 10Gbps 384-bit 8GB HBM2 1.89Gbps 2048-bit 2x as big and 2x as wide HBM a huge improvement.
Memory Bandwidth (GB/s)
1000 [+2.4x] 652 512 410 Still bandwidth is 9% higher.
L1 Caches 16kB x 60 96kB x 80 48kB x 28 16kB x 56 L1 has not changed.
L2 Cache 4MB 4.5MB 3MB 4MB L2 has not changed.
Maximum Work-group Size
256 / 1024 1024 / 2048 1024 / 2048 256 / 1024 Same work-group sizes.
FP64/double ratio
1/4x 1/2x 1/32x 1/16x Ratio is 4x better than Vega1.
FP16/half ratio
2x 2x 1/64x 2x Ratio is the same throughout.

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 AMD and nVidia drivers. Turbo / Boost was enabled on all configurations.

Processing Benchmarks AMD Radeon VII (Vega2) nVidia Titan V (Volta) nVidia Titan X (Pascal) AMD Vega 56 (Vega1) Comments
GPGPU Arithmetic Benchmark Mandel FP16/Half (Mpix/s) 29,057 [+48%] 33,860 245 19,580 Vega2 starts strong with a 48% lead over Vega1 and almost catching Volta.
GPGPU Arithmetic Benchmark Mandel FP32/Single (Mpix/s) 18,340 [+35%] 22,680 17,870 13,550 Good improvement here +35% over Vega1 again close to Volta.
GPGPU Arithmetic Benchmark Mandel FP64/Double (Mpix/s) 5,377 [+4.3x] 11,000 661 1,240 1/4 FP64 rate makes it over four (4x) times faster than Vega1.
GPGPU Arithmetic Benchmark Mandel FP128/Quad (Mpix/s) 234 [+3x] 458 25.77 77 Similar to above, Vega2 is over three (3x) faster.
Vega2 looks about 35-50% faster than Vega1 in FP32/FP16 and 3-4x faster in FP64 due to its 1/4 FP64 rate. It won’t beat real workstation cards with 1/2 FP64 rate through thus that Titan has nothing to worry about.
GPGPU Crypto Benchmark Crypto AES-256 (GB/s) 91 [+36%] 70 42 67 The fast HBM2 memory allows it to beat even Volta not just Vega1.
GPGPU Crypto Benchmark Crypto AES-128 (GB/s) 93 58 88
GPGPU Crypto Benchmark Crypto SHA2-256 (GB/s) 209 [+67%] 245 145 125 Vega2 is a huge 70% faster in integer/crypto workloads.
GPGPU Crypto Benchmark Crypto SHA1 (GB/s) 129 107 162
GPGPU Crypto Benchmark Crypto SHA2-512 (GB/s) 176 76 32
Vega2 increases its lead in integer workloads even streaming ones no doubt due to its very fast HBM2 memory making it the crypto-king of the hill though its cost may be an issue.
GPGPU Finance Benchmark Black-Scholes float/FP32 (MOPT/s) 23,164 [+2.3x] 18,570 11,480 9,500 Vega2 is over 2x faster than Vega1 also beating Volta.
GPGPU Finance Benchmark Black-Scholes double/FP64 (MOPT/s) 7,272 [+3.84x] 8,400 1,370 1,880 In FP64 its almost 4x faster just below Volta!
GPGPU Finance Benchmark Binomial float/FP32 (kOPT/s) 3,501 [+38%] 4,200 2,240 2,530 Binomial uses thread shared data thus stresses the memory system Vega2 is still 40% faster.
GPGPU Finance Benchmark Binomial double/FP64 (kOPT/s) 789 [+4.8x] 2,000 129 164 With FP64 we’re almost 5x faster than Vega1.
GPGPU Finance Benchmark Monte-Carlo float/FP32 (kOPT/s) 6,249 [+62%] 11,920 5,350 3,840 Monte-Carlo also uses thread shared data but read-only thus reducing modify pressure – here Vega2 is 60% faster.
GPGPU Finance Benchmark Monte-Carlo double/FP64 (kOPT/s) 1,676 [+3.55x] 4,440 294 472 With FP64 we’re over 3.5x faster.
For financial FP32 workloads, Vega2 is 40-60% faster than Vega1 a decent improvement; naturally in FP64 it’s 4-5x times faster thus a significant upgrade for algorithms that require such precision.
GPGPU Science Benchmark SGEMM (GFLOPS) float/FP32 6,634 [+30%] 11,000 6,073 5,066 GEMM still brings a 30% improvement over Vega1.
GPGPU Science Benchmark DGEMM (GFLOPS) double/FP64 2,339 [+3.77x] 3,830 340 620 But DGEMM is almost 4x faster.
GPGPU Science Benchmark SFFT (GFLOPS) float/FP32 643 [+74%] 617 235 369 FFT loves HBM thus Vega2 is 75% faster.
GPGPU Science Benchmark DFFT (GFLOPS) double/FP64 365 [+2.1x] 280 207 175 DFFT is tough but Vega2 is still twice as fast.
GPGPU Science Benchmark SNBODY (GFLOPS) float/FP32 6,846 [+41%] 7,790 5,720 4,840 In N-Body physics Vega2 is 40% faster.
GPGPU Science Benchmark DNBODY (GFLOPS) double/FP64 1,752 [+3.9x] 4,270 275 447 And in FP64 physics Vega2 is almost 4x faster.
The scientific scores show a similar improvement, with FP32 30-40% better but FP64 a whopping four (4x) faster than Vega1 and, in some algorithms, matching the hugely expensive Volta.
GPGPU Image Processing Blur (3×3) Filter single/FP32 (MPix/s) 25,418 [+32%] 26,790 18,410 19,130 In this 3×3 convolution algorithm, Vega2 is 32% faster than Vega1
GPGPU Image Processing Sharpen (5×5) Filter single/FP32 (MPix/s) 5,275 [+21%] 9,295 5,000 4,340 Same algorithm but more shared data reduces the lead to 21%.
GPGPU Image Processing Motion Blur (7×7) Filter single/FP32 (MPix/s) 5,510 [+24%] 9,428 5,080 4,450 With even more data the gap remains constant.
GPGPU Image Processing Edge Detection (2*5×5) Sobel Filter single/FP32 (MPix/s) 5,273 [+23%] 9,079 4,800 4,300 Still convolution but with 2 filters – similar 23% faster.
GPGPU Image Processing Noise Removal (5×5) Median Filter single/FP32 (MPix/s) 92 [+91%] 112 37 48 Different algorithm makes Vega2 almost 2x faster than Vega1.
GPGPU Image Processing Oil Painting Quantise Filter single/FP32 (MPix/s) 57 [+50%] 42 12.7 38 Without major processing, this filter is 50% faster on Vega2.
GPGPU Image Processing Diffusion Randomise (XorShift) Filter single/FP32 (MPix/s) 47,349 [+2.3x] 24,370 19,480 20,880 This algorithm is 64-bit integer heavy and Vega2 flies 2x faster than Vega1.
GPGPU Image Processing Marbling Perlin Noise 2D Filter single/FP32 (MPix/s) 7,708 [+28%] 8,460 305 6,000 One of the most complex and largest filters, Vega2 is 28% faster.
For image processing using FP32 precision, Vega goes from 21% to 2x faster, overall a decent improvement if you are processing a large number of images. In many filters it beats the far more expensive Volta competition.

Memory Performance

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

Results Interpretation: For bandwidth tests (MB/s, etc.) high values mean better performance, for latency tests (ns, etc.) low values mean better performance.

Environment: Windows 10 x64, latest AMD and nVidia. drivers. Turbo / Boost was enabled on all configurations.

Memory Benchmarks AMD Radeon VII (Vega2) nVidia Titan V (Volta) nVidia Titan X (Pascal) AMD Vega 56 (Vega1) Comments
GPGPU Memory Bandwidth Internal Memory Bandwidth (GB/s) 627 [+88%] 536 356 333 Vega2’s wide HBM2 is almost 2x faster as expected.
GPGPU Memory Bandwidth Upload Bandwidth (GB/s) 12.37 [+2%] 11.47 11.4 12.18 Using PCIe 3.0 similar upload bandwidth.
GPGPU Memory Bandwidth Download Bandwidth (GB/s) 12.95 [+7%] 12.27 12.2 12.08 Again similar bandwidth.
Vega2 benefits greatly from its very wide HBM2 memory (4096-bit) which provides almost 2x real bandwidth as expected. But while PCIe 4.0 capable for now it has to make do with 3.0 and thus same upload/download bandwith. Here’s hoping for a BIOS update once new motherboards come out.
GPGPU Memory Latency Global (In-Page Random Access) Latency (ns) 202 [-19%] 180 201 247 The higher clock allows Vega2 a 20% latency reduction.
GPGPU Memory Latency Global (Full Range Random Access) Latency (ns) 341 [-4%] 311 286 353 Full range is only 4% faster.
GPGPU Memory Latency Global (Sequential Access) Latency (ns) 53.4 89.8 115
GPGPU Memory Latency Constant Memory (In-Page Random Access) Latency (ns) 75.4 117 237
GPGPU Memory Latency Shared Memory (In-Page Random Access) Latency (ns) 18.1 18.7 55
GPGPU Memory Latency Texture (In-Page Random Access) Latency (ns) 212 195 193
GPGPU Memory Latency Texture (Full Range Random Access) Latency (ns) 344 282 301
GPGPU Memory Latency Texture (Sequential Access) Latency (ns) 88.5 87.6 80
Not unexpected, GDDR6′ latencies are higher than HBM2 although not by as much as we were fearing.

SiSoftware Official Ranker Scores

Final Thoughts / Conclusions

Vega2 (“BigVega”) is a big improvement over normal Vega1 and its workstation-class pedigree shows. For FP16/Fp32 workloads though the 30-40% performance improvement may not be worth it considering the much higher price: naturally FP64 performance is almost 4x due to 1/4 FP64 rate though not as good at professional cards with 1/2 rate or Titan competition with similar 1/2 rate.

While the GCN core (rev 5.1) has seen internal updates, there is nothing new that can be supported/optimised for in the compute land thus any code working well on Vega1 should work just as well on Vega2.

The 16GB HBM2 wide memory also helps big workloads with 2x higher bandwidth and also lower latency due to higher clock. For some workloads this alone makes it a definite buy when competition stops at 12GB.

Unfortunately the card has had a limited release at a relatively high price thus value/price ratio depends entirely on your workload – if FP64 with large datasets then it is very much worth it; if FP32/FP16 with datasets that fit in standard 8GB memory then the older Vega1 is much better value and you can even get 2 for the price of the Vega2.

For revolutionary change we need to wait for Navi and its brand new RDNA (Radeon DNA) arch(itecture)…

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