FP16 GPGPU Image Processing Performance & Quality

GPGPU Image Processing

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) while CPU support is still limited to SIMD conversion only (FP16C). It has been added to allow mobile devices (phones, tablets) to provide increased performance (and thus save power for fixed workloads) for a small drop in quality for normal 8-bbc (24-bbp) image and video.

However, normal laptops and tablets with integrated graphics can also benefit from FP16 support in same way due to relatively low graphics compute power and the need to save power due to limited battery in thin and light formats.

In this article we’re investigating the performance differences vs. standard FP32 (aka “single”) and the resulting quality difference (if any) for mobile GPGPUs (Intel’s EV9/9.5 SKL/KBL). See the previous articles for general performance comparison:

Image Processing Performance & Quality

We are testing GPGPU performance of the GPUs in OpenCL, DirectX/OpenGL ComputeShader .

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

Environment: Windows 10 x64, latest Intel drivers (April 2017). Turbo / Dynamic Overclocking was enabled on all configurations.

Image Filter
FP32/Single FP16/Half Comments
GPGPU Image Processing Blur (3×3) Filter OpenCL (MPix/s)  481  967 [+2x] We see a a text-book 2x performance increase for no visible drop in quality.
GPGPU Image Processing Sharpen (5×5) Filter OpenCL (MPix/s)  107  331 [+3.1x] Using FP16 yields over 3x performance increase but we do see a few more changed pixels though no visible difference.
GPGPU Image Processing Motion-Blur (7×7) Filter OpenCL (MPix/s)  112  325 [+2.9x] Again almost 3x performance increase but no visible quality difference. Result!
GPGPU Image Processing Edge Detection (2*5×5) Sobel OpenCL (MPix/s)  107  323 [+3.1x] Again just over 3x performance increase but no visible quality difference.
GPGPU Image Processing Noise Removal (5×5) Median OpenCL (MPix/s) 5.41  5.67 [+4%] No image difference at all but also almost no performance increase – a measly 4%.
GPGPU Image Processing Oil Painting Quantise OpenCL (MPix/s)  4.7  13.48 [+2.86x] We’re back with a 2.8x times performance increase but few more differences than we’ve seen though quality seems acceptable.
GPGPU Image Processing Diffusion Randomise OpenCL (MPix/s)  1188  1210 [+2%] Due to random no generation using 64-bit integer processing the performance difference is minimal but the picture quality is not acceptable.
GPGPU Image Processing Marbling Perlin Noise 2D OpenCL (MPix/s) 470  508 [+8%] Again due to Perlin noise generation we see almost no performance gain but big drop in image quality – not worth it.

Other Image Processing relating Algorithms

Image Filter
FP16/Half FP32/Single FP64/Double Comments
GPGPU Science Benchmark GEMM OpenCL (GFLOPS)  178 [+50%]  118  35 Dropping to FP16 gives us 50% more performance, not as good as 2x but still a significant increase.
GPGPU Science Benchmark FFT OpenCL (GFLOPS)  34 [+70%]  20  5.4 With FFT we are now 70% faster, closer to the 100% promised.
GPGPU Science Benchmark N-Body OpenCL (GFLOPS)  297 [+49%]  199  35 Again we drop to “just” 50% faster with FP16 but still a great performance improvement.

Final Thoughts / Conclusions

For many image processing filters (Blur, Sharpen, Sobel/Edge-Detection, Median/De-Noise, etc.) we see a huge 2-3x performance increase – more than we’ve hoped for (2x) – with little or no image quality degradation. Thus FP16 support is very much useful and should be used when supported.

However for complex filters (Diffusion, Marble/Perlin Noise) the drop in quality is not acceptable for minor performance increase (2-8%); increasing the precision of more data items to improve quality (from FP16 to FP32) would further drop performance making the whole endeavour pointless.

For those algorithms that do benefit from FP16 the performance improvement with FP16 is very much worth it – so FP16 support is very useful indeed.

Intel Graphics GPGPU Performance

Intel Logo

Why test GPGPU performance Intel Core Graphics?

Laptops (and tablets) are still in fashion with desktops largely left to PC game enthusiasts and workstations for big compute workloads; most laptops (and all tablets) make due with integrated graphics with few dedicated graphics options mainly for mobile PC gamers.

As a result integrated graphics on Intel’s mobile platform is what the vast majority of users will experience – thus its importance is not to be underestimated. While in the past integrated graphics options were dire – the introduction of Core v3 (Ivy Bridge) series brought us a GPGPU-capable graphics processor as well an updated internal media transcoder of Core v2 (Sandy Bridge).

With each generation Intel has progressively improved the graphics core, perhaps far more than its CPU cores – and added more variants (GT3) and embedded cache (eDRAM) which greatly increased performance – all within the same power limit.

New Features enabled by the latest 21.45 graphics driver

With Intel graphics drivers supporting just 2 generations of graphics – unlike unified drivers of AMD and nVidia – old graphics quickly become obsolete with few updates; but Windows 10 “free update” forced Intel’s hand somewhat – with its driver (20.40) supporting 3 generations of graphics (Haswell, Broadwell and latest at the time Skylake).

However, the latest 21.45 driver for newly released Kabylake and older Skylake does bring new features that can make a big difference in performance:

  • Native FP64 (64-bit aka “double” floating-point support) in OpenCL – thus allowing high precision compute on integrated graphics.
  • Native FP16 (16-bit aka “half” floating-point support) in OpenCL, ComputeShader – thus allowing lower precision but faster compute.
  • Vulkan graphics interface support – OpenGL’s successor and DirectX 12’s competitor – for faster graphics and compute.

Will these new features make upgrading your laptop to a brand-new KBL laptop more compelling?

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

Hardware Specifications

We are comparing the internal GPUs of the new Intel ULV APUs with the old versions.

Graphics Unit Haswell HD4000 Haswell HD5000 Broadwell HD6100 Skylake HD520 Skylake HD540 Kabylake HD620 Comment
Graphics Core EV7.5 HSW GT2U EV7.5 HSW GT3U EV8 BRW GT3U EV9 SKL GT2U EV9 SKL GT3eU EV9.5 KBL GT2U Despite 4 CPU generations we really have 2 GPU generations.
APU / Processor Core i5-4210U Core i7-4650U Core i7-5557U Core i7-6500U Core i5-6260U Core i3-7100U The naming convention has changed between generations.
Cores (CU) / Shaders (SP) / Type 20C / 160SP 40C / 320SP 48C / 384SP 24C / 192SP 48C / 384SP 23C / 184SP BRW increased CUs to 24/48 and i3 misses 1 core.
Speed (Min / Max / Turbo) MHz 200-1000 200-1100 300-1100 300-1000 300-950 300-1000 The turbo clocks have hardly changed between generations.
Power (TDP) W 15 15 28 15 15 15 Except GT3 BRW, all ULVs are 15W rated.
DirectX CS Support 11.1 11.1 11.1 11.2 / 12.1 11.2 / 12.1 11.2 / 12.1 SKL/KBL enjoy v11.2 and 12.1 support.
OpenGL CS Support 4.3 4.3 4.3 4.4 4.4 4.4 SKL/KBL provide v4.4 vs. verision 4.3 for older devices.
OpenCL CS Support 1.2 1.2 1.2 2.0 2.0 2.1 SKL provides v2 support with KBL 2.1 vs 1.2 for older devices.
FP16 / FP64 Support No / No No / No No / No Yes / Yes Yes / Yes Yes / Yes SKL/KBL support both FP64 and FP16.
Byte / Integer Width 8 / 32-bit 8 / 32-bit 8 / 32-bit 128 / 128-bit 128 / 128-bit 128 / 128-bit SKL/KBL prefer vectorised integer workloads, 128-bit wide.
Float/ Double Width 32 / X-bit 32 / X-bit 32 / X-bit 32 / 64-bit 32 / 64-bit 32 / 64-bit Strangely neither arch prefers vectorised floating-point loads – driver bug?
Threads per CU 512 512 256 256 256 256 Strangely BRW and later reduced the threads/CU to 256.

GPGPU Performance

We are testing vectorised, crypto (including hash), financial and scientific GPGPU performance of the GPUs in OpenCL, DirectX/OpenGL ComputeShader .

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

Environment: Windows 10 x64, latest Intel drivers (April 2017). Turbo / Dynamic Overclocking was enabled on all configurations.

Graphics Processors HD4000 (EV7.5 HSW-GT2U) HD5000 (EV7.5 HSW-GT3U) HD6100 (EV8 BRW-GT3U) HD520 (EV9 SKL-GT2U) HD540 (EV9 SKL-GT3eU) HD620 (EV9.5 KBL-GT2U) Comments
GPGPU Arithmetic Half/Float/FP16 Vectorised OpenCL (Mpix/s) 288 399 597 875 [+3x] 1500 840 [+2.8x] If FP16 is enough, KBL and SKL have 2x performance of FP32.
GPGPU Arithmetic Single/Float/FP32 Vectorised OpenCL (Mpix/s) 299 375 614 468 [+56%] 817 452 [+50%] SKL GT3e rules the roost but KBL hardly improves on SKL.
GPGPU Arithmetic Double/FP64 Vectorised OpenCL (Mpix/s) 18.54 (eml) 24.4 (eml) 38.9 (eml) 112 [+6x] 193 104 [+5.6x] SKL GT2 with native Fp64 is almost 4x emulated BRW GT3!
GPGPU Arithmetic Quad/FP128 Vectorised OpenCL (Mpix/s) 1.8 (eml) 2.36 (eml) 4.4 (eml) 6.34 (eml) [+3.5x] 10.92 (eml) 6.1 (eml) [+3.4x] Emulating Fp128 though Fp64 is ~2.5x faster than through FP32.
As expected native FP16 runs about 2x faster than FP32 and thus provides a huge performance upgrade if precision is sufficient. Native FP64 is about 8x emulated FP64 and even emulated FP128 improves by about 2.5x! Otherwise KBL GT2 matches SKL GT2 and is about 50% faster than HSW GT2 in FP32 and 6x faster in FP64.
GPGPU Crypto Benchmark AES256 Crypto OpenCL (MB/s) 1.37 1.85 2.7 2.19 [+60%] 3.36  2.21 [+60%] Since BRW integer performance is similar.
GPGPU Crypto Benchmark AES128 Crypto OpenCL (MB/s) 1.87 2.45 3.45 2.79 [+50%] 4.3 2.83 [+50%] Not a lot changes here.
SKL/KBL GT2 with integer workloads (with extensive memory accesses) are 50-60% faster than HSW similar to what we saw with floating-point performance. But the changed happened with BRW which improved the most over HSW with SKL and KBL not improving further.
GPGPU Crypto Benchmark SHA2-256 (int32) Hash OpenCL (MB/s)  1.2 1.62 4.35  3 [+2.5x] 5.12 2.92 In this tough compute test SKL/KBL are 2.5x faster.
GPGPU Crypto Benchmark SHA1 (int32) Hash OpenCL (MB/s) 2.86  3.93  9.82  6.7 [+2.34x]  11.26  6.49 With a lighter algorithm SKL/KBL are still ~2.4x faster.
GPGPU Crypto Benchmark SHA2-512 (int64) Hash OpenCL (MB/s)  0.828  1.08 1.68 1.08 [+30%] 1.85  1 64-integer performance does not improve much.
In pure integer compute tests SKL/KBL greatly improve over HSW being no less than 2.5x faster a huge improvement; but 64-bit integer performance hardly improves (30% faster with 20% more CUs 24 vs 20). Again BRW is where the improvements were added with SKL GT3e hardly improving over BRW GT3.
GPGPU Finance Benchmark Black-Scholes FP32 OpenCL (MOPT/s) 461 495 493 656 [+42%]  772 618 [+40%] Pure FP32 compute SKL/KBL are 40% faster.
GPGPU Finance Benchmark Black-Scholes FP64 OpenCL (MOPT/s) 137  238 135 SKL GT3 is 73% faster than GT2 variants
GPGPU Finance Benchmark Binomial FP32 OpenCL (kOPT/s) 62.45 85.76 123 86.32 [+38%]  145.6 82.8 [+35%] In this tough algorithm SKL/KBL are still amost 40% faster.
GPGPU Finance Benchmark Binomial FP64 OpenCL (kOPT/s) 18.65 31.46 19 SKL GT3 is over 65% faster than GT2 KBL.
GPGPU Finance Benchmark Monte-Carlo FP32 OpenCL (kOPT/s) 106 160.4 192 174 [+64%] 295 166.4 [+56%] M/C is not as tough so here SKL/KBL are 60% faster.
GPGPU Finance Benchmark Monte-Carlo FP64 OpenCL (kOPT/s) 31.61 56 31 GT3 SKL manages an 80% improvement over GT2.
Intel is pulling our leg here; KBL GPU seems to show no improvement whatsoever over SKL, but both are about 40% faster in FP32 than the much older HSW. GT3 SKL variant shows good gains of 65-80% over the common GT2 and thus is the one to get if available. Obviously the ace card for SKL and KBL is FP64 support.
GPGPU Science Benchmark SGEMM FP32 OpenCL (GFLOPS)  117  130 142 116 [=]  181 113 [=] SKL/GBL have a problem with this algorithm but GT3 does better?
GPGPU Science Benchmark DGEMM FP64 OpenCL (GFLOPS) 34.9 64.7 34.7 GT3 SKL manages a 86% improvement over GT2.
GPGPU Science Benchmark SFFT FP32 OpenCL (GFLOPS) 13.3 13.1 15 20.53 [+54%]  27.3 21.9 [+64%] In a return to form SKL/KBL are 50% faster.
GPGPU Science Benchmark DFFT FP64 OpenCL (GFLOPS) 5.2  4.19  4.69 GT3 stumbles a bit here some optimisations are needed.
GPGPU Science Benchmark N-Body FP32 OpenCL (GFLOPS)  122  157.9 249 201 [+64%]  304 177.6 [+45%] Here SKL/KBL are 50% faster overall.
GPGPU Science Benchmark N-Body FP64 OpenCL (GFLOPS) 19.25 31.9 17.8 GT3 manages only a 65% improvement here.
Again we see no delta between SKL and KBL – the graphics cores perform the same; again both benefit from FP64 support allowing high precision kernels to run. GT3 SKL variant greatly improves over common GT2 variant – except in one test (DFFT) that seems to be an outlier.
GPGPU Image Processing Blur (3×3) Filter OpenCL (MPix/s)  341  432  636 492 [+44%]  641 488 [+43%] We see the GT3s trading blows in this integer test, but SKL/KBL are 40% faster than HSW.
GPGPU Image Processing Sharpen (5×5) Filter OpenCL (MPix/s)  72.7  92.8  147  106 [+45%]  139  106 [+45%] BRW GT3 just wins this with SKL/KBL again 45% faster.
GPGPU Image Processing Motion-Blur (7×7) Filter OpenCL (MPix/s)  75.6  96  152  110 [+45%]  149  111 [+45%] Another win for BRW and 45% improvent for SKL/KBL.
GPGPU Image Processing Edge Detection (2*5×5) Sobel OpenCL (MPix/s)  72.6  90.6  147  105 [+44%]  143  105 [+44%] As above in this test.
GPGPU Image Processing Noise Removal (5×5) Median OpenCL (MPix/s)  2.38  1.53  6.51  5.2 [+2.2x]  7.73  5.32 [+2.23x] SKL’s GT3 manages a win but overall SKl/KBL are over 2x faster than HSW.
GPGPU Image Processing Oil Painting Quantise OpenCL (MPix/s)  1.17  0.719  5.83  4.57 [+3.9x]  4.58  4.5 [+3.84x] Another win for BRW
GPGPU Image Processing Diffusion Randomise OpenCL (MPix/s)  511  688  1150  1100 [+2.1x]  1750  1080 [+2.05x]_ SKL/KBL are over 2x faster than HSW. BRW is beat here.
GPGPU Image Processing Marbling Perlin Noise 2D OpenCL (MPix/s)  378.5  288  424  437 [+15%]  611  443 [+17%] Some wild results here, some optimizations may be needed.
In this integer workloads (with texture access) the 28W GT3 of BRW manages a few wins over 15W GT3e of SKL – but compared to old HSW – both SKL and KBL are between 40 and 300% faster. Again we see no delta between SKL and KBL – there does not seem to be any difference at all!

If you have a HSW GT2 then an upgrade to SKL GT2 brings massive improvements as well as FP16 and FP64 native support. But HSW GT3 variant is competitive and BRW GT3 even more so. KBL GT2 shows no improvement over SKL GT2 – so it’s not just the CPU core that is unchanged but the graphics core also – it’s no EV9.5 here more like EV9.1!

For integer workloads BRW is where the big improvement came but for 64-integer that improvement is still to come, if ever. At least all drivers support native int64.

Transcoding Performance

We are testing media (video + audio) transcoding performance for common video algorithms: H.264/MP4, AVC1, M.265/HEVC.

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

Environment: Windows 10 x64, latest Intel drivers (April 2017). Turbo / Dynamic Overclocking was enabled on all configurations.

Graphics Processors HD4000 (EV7.5 HSW-GT2U) HD5000 (EV7.5 HSW-GT3U) HD6100 (EV8 BRW-GT3U) HD520 (EV9 SKL-GT2U) HD540 (EV9 SKL-GT3eU) HD620 (EV9.5 KBL-GT2U) Comments
H.264/AVC Decoder/Encoder QuickSync H264 8-bit only QuickSync H264 8-bit only QuickSync H264 8/10-bit QuickSync H264 8/10-bit QuickSync H264 8/10-bit QuickSync H264 8/10-bit HSW supports 8-bit only so 10-bit (high-colour) are out of luck.
H.265/HEVC Decoder/Encoder QuickSync H265 8-bit partial QuickSync H265 8-bit QuickSync H265 8-bit QuickSync H265 8/10-bit SKL has full/hardware H265/HEVC transcoding but for 8-bit only; Main10 (10-bit profile) requires KBL so finally we see a difference.
Transcode Benchmark VC 1 > H264/AVC Transcoding (MB/s)  7.55 8.4  7.42 [-2%]  8.25  8.08 [+6%] With DDR4 KBL is 6% faster.
Transcode Benchmark VC 1 > H265/HEVC Transcoding (MB/s)  0.734  3.14 [+4.2x]  3.67  3.63 [+5x] Hardware support makes SKL/KBL 4-5x faster.

If you want HEVC/H.265 then you want SKL including 4k/UHD. But if you plan on using 10-bit/HDR colour then you need KBL – finally an improvement over SKL. As it uses fixed-point hardware the GT3 performs only slightly faster.

Memory Performance

We are testing memory performance of GPUs using OpenCL, DirectX/OpenGL ComputeShader,  including transfer (up/down) to/from system memory and latency.

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

Environment: Windows 10 x64, latest Intel drivers (Apr 2017). Turbo / Dynamic Overclocking was enabled on all configurations.

Graphics Processors HD4000 (EV7.5 HSW-GT2U) HD5000 (EV7.5 HSW-GT3U) HD6100 (EV8 BRW-GT3U) HD520 (EV9 SKL-GT2U) HD540 (EV9 SKL-GT3eU) HD620 (EV9.5 KBL-GT2U) Comments
Memory Configuration 8GB DDR3 1.6GHz 128-bit 8GB DDR3 1.6GHz 128-bit 16GB DDR3 1.6GHz 128-bit 8GB DDR3 1.867GHz 128-bit 16GB DDR4 2.133GHz 128-bit 16GB DDR4 2.133GHz 128-bit All use 128-bit memory with SKL/KBL using DDR4.
Constant (kB) / Shared (kB) Memory 64 / 64 64 / 64 64 / 64 2048 / 64 2048 / 64 2048 / 64 Shared memory remains the same; in SKL/KBL constant memory is the same as global.
GPGPU Memory Bandwidth Internal Memory Bandwidth (GB/s) 10.4 10.7 11 15.65 23 [+2.1x] 19.6 DDR4 seems to provide over 2x bandwidth despite low clock.
GPGPU Memory Bandwidth Upload Bandwidth (GB/s) 5.23 5.35 5.54 7.74 11.23 [+2.1x] 9.46 Again over 2x increase in up speed.
GPGPU Memory Bandwidth Download Bandwidth (GB/s) 5.27 5.36 5.29 7.42 11.31 [+2.1x] 9.6 Again over 2x increase in down speed.
SKL/KBL + DDR4 provide over 2x increase in internal, up and down memory bandwidth – despite the relatively modern increase in memory speed (2133 vs 1600); with DDR3 1867MHz memory the improvement drops to 1.5x. So if you were to decide DDR3 or DDR4 the choice has been made!
GPGPU Memory Latency Global Memory (In-Page Random) Latency (ns)  179 192  234 [+30%]  296 235 [+30%] With DDR4 latency has increased by 30% not great.
GPGPU Memory Latency Constant Memory Latency (ns)  92.5  112  234 [+2.53x]  279  235 [+2.53x] Constant memory has effectively been dropped resulting in a disastrous 2.53x higher latencies.
GPGPU Memory Latency Shared Memory Latency (ns)  80  84  –  86.8 [+8%]  102  84.6 [+8%] Shared memory latency has stayed the same.
GPGPU Memory Latency Texture Memory (In-Page Random) Latency (ns)  283  298  56 [1/5x]
 58.1 [1/5x]
Texture access seems to have markedly improved to be 5x faster.
SKL/KBL global memory latencies have increased by 30% with DDR4 – thus wiping out some gains. The “new” constant memory (2GB!) is now really just bog-standard global memory and thus with over 2x increase in latency. Shared memory latency has stayed pretty much the same. Texture memory access is very much faster – 5x faster likely though some driver optimisations.

Again no delta between KBL and SKL; if you want bandwidth (who doesn’t?) DDR4 with modest 2133MHz memory doubles bandwidths – but latencies increase. Constant memory is now the same as global memory and does not seem any faster.

Shader Performance

We are testing shader performance of the GPUs in DirectX and OpenGL as well as memory bandwidth performance.

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

Environment: Windows 10 x64, latest Intel drivers (Apr 2017). Turbo / Dynamic Overclocking was enabled on all configurations.

Graphics Processors HD4000 (EV7.5 HSW-GT2U) HD5000 (EV7.5 HSW-GT3U) HD6100 (EV8 BRW-GT3U) HD520 (EV9 SKL-GT2U) HD540 (EV9 SKL-GT3eU) HD620 (EV9.5 KBL-GT2U) Comments
Video Shader Benchmark Half/Float/FP16 Vectorised DirectX (Mpix/s) 250  119 602 [+2.4x] 1000 537 [+2.1x] Fp16 support in DirectX doubles performance.
Video Shader Benchmark Half/Float/FP16 Vectorised OpenGL (Mpix/s) 235  109 338 [+43%]  496 289 [+23%] Fp16 does not yet work in OpenGL.
Video Shader Benchmark Single/Float/FP32 Vectorised DirectX (Mpix/s)  238  120 276 [+16%]  485 248 [4%] We only see a measly 4-16% better performance here.
Video Shader Benchmark Single/Float/FP32 Vectorised OpenGL (Mpix/s) 228  108 338 [+48%] 498 289 [+26%] SKL does better here – it’s 50% faster than HSW.
Video Shader Benchmark Double/FP64 Vectorised DirectX (Mpix/s) 52.4  78 76.7 [+46%] 133 69 [+30%] With FP64 SKL is still 45% faster.
Video Shader Benchmark Double/FP64 Vectorised OpenGL (Mpix/s) 63.2  67.2 105 [+60%] 177 96 [+50%] Similar result here 50-60% faster.
Video Shader Benchmark Quad/FP128 Vectorised DirectX (Mpix/s) 5.2  7 18.2 [+3.5x] 31.3 16.7 [+3.2x] Driver optimisation makes SKL/KBL over 3.5x faster.
Video Shader Benchmark Quad/FP128 Vectorised OpenGL (Mpix/s) 5.55  7.5 57.5 [+10x]  97.7 52.3 [+9.4x] Here we see SKL/KBL over 10x faster!
We see similar results to OpenCL GPGPU here – with FP16 doubling performance in DirectX – but with FP64 already supported in both DirectX and OpenGL even with HSW, KBL and SKL have less of a lead – of around 50%.
Video Memory Benchmark Internal Memory Bandwidth (GB/s)  15  14.8 27.6 [+84%]
26.9 25 [+67%] DDR4 brings almost 50% more bandwidth.
Video Memory Benchmark Upload Bandwidth (GB/s)  7  7.8 10.1 [+44%] 12.34 10.54 [+50%] Upload bandwidth has also increased ~50%.
Video Memory Benchmark Download Bandwidth (GB/s)  3.63  3.3 3.53 [-2%] 5.66 3.51 [-3%] No change in download bandwidth though.

Final Thoughts / Conclusions

SKL and KBL with the 21.45 driver yields significant gains in OpenCL making an upgrade from HSW and even BRW quite compelling despite the relatively modern 20.40 driver Intel was forced to provide for Windows 10. The GT3 version provides good gains over the standard GT2 version and should always be selected if available.

Native FP64 support is a huge addition which provides support for high-precision kernels – unheard of for integrated graphics. Native FP16 support provides an additional 2x performance in cases where 16-bit floating-point processing is sufficient.

However KBL’s EV9.5 graphics core shows no improvement at all over SKL’s EV9 core – thus it’s not just the CPU core that has not been changed but the GPU core too! Except for the updated transcoder supporting Main10 HEVC/H.265 (thus HDR / 10-bit+ colour) which is still quite useful for UHD/4K HDR media.

This is very much a surprise – as while the CPU core has not improved markedly since SNB (Core v2), the GPU core has always provided significant improvements – and now we have hit the same road-block. As dedicated GPUs have continued to improve significantly in performance and power efficiency this is quite a surprise. This marks the smallest ever generation to generation – SKL to KBL – ever, effectively KBL is a SKL refresh.

It seems the rumour that Intel may change to ATI/AMD graphics cores may not be such a crazy idea after all!

SiSoftware OpenCL Support Released

GPGPU Arithmetic Benchmark

FOR IMMEDIATE RELEASE

Contact: Press Office

SiSoftware OpenCL Support Released

London, UK, 30th November 2009 – SiSoftware releases its suite of OpenCL GPGPU (General Purpose Graphics Processor Unit) benchmarks as part of SiSoftware Sandra 2010, the latest version of our award-winning utility, which includes remote analysis, benchmarking and diagnostic features for PCs, servers, and networks.

At SiSoftware we are constantly looking out for new technologies with the aim to understand how those technologies can best be benchmarked and analysed. We believe that the industry is seeing a shift from the model where heavy computational workload is processed on a traditional CPU to a model that uses the GPGPU or a combination of GPU and CPU; in a wide range of applications developers are using the power of GPGPU to aid business analysis, games, graphics and scientific applications.

As certain tasks or workloads may still perform better on traditional CPU, we see both CPU and GPGPU benchmarks to be an important part of performance analysis. Having launched the GPGPU Benchmarks with SiSoftware Sandra 2009 with support for AMD CTM/STREAM and nVidia CUDA, we have now ported the benchmark suite to OpenCL.

OpenCL is an open standard for running parallel tasks on GPUs, CPUs and hardware accelerators using the same code – unlike proprietary solutions. We believe OpenCL will become “the standard” for programming parallel workloads in the future, thus we have ported all our GPGPUs benchmarks to OpenCL.

Below is a quote we would like to share with you:

“AMD believes OpenCL is what the industry has been waiting for: an industry-standard, cross-platform development platform designed to allow developers to harness the immense computational power available in today’s GPUs and multi-core CPUs. We’ve been a staunch supporter of and contributor to OpenCL since its inception,” said Patricia Harrell, director of Stream Computing, AMD. “SiSoftware has made significant contributions to the OpenCL ecosystem with the release of its GPGPU benchmark suite with OpenCL support. This benchmark suite enables customers, partners and OpenCL developers to easily measure application performance on heterogeneous platforms, and provides the information required to help optimize this performance.”

The SiSoftware OpenCL Benchmarks look at the two major performance aspects:

  • Computational performance: in simple terms how fast it can crunch numbers. It follows the same style as the CPU Multi-Media benchmark using fractal generation as its workload. This allows the user to see the power of the GPGPU in solving a workload thus far exclusively performed on a CPU.
  • Memory performance: this analyses how fast data can be transferred to and from the GPGPU. No matter how fast the processing, ultimately the end result will be affected by memory performance.

Key features

  • 4 architectures natively supported (x86, x64/AMD64/EM64T, IA64/Itanium2, ARM)
  • 6 languages supported (English, French3, German3, Italian3, Japanese3, Russian3)
  • AMD OpenCL 1.01
  • nVidia OpenCL 1.0
  • GPU + CPU parallel execution supported, up to 8 devices in total.
  • Different models of GPUs supported, including integrated GPU + dedicated GPUs.
  • Multi-GPUs supported, up to 8 in parallel.

With each release, we continue to add support and compatibility for the latest technologies. SiSoftware works with hardware vendors to ensure the best support for new emerging hardware.

Notes:

1 Available as Beta at this time, performance cannot be guaranteed.

2 By special arrangement; Enterprise versions only.

3 Not all languages available at publication, will be released later.

Relevant Press Releases

Relevant Articles

For more details, please see the following articles comparing current devices on the market:

Purchasing

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

Updating or Upgrading

To update your existing commercial version, please contact your distributor (sales support).

Downloading

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

Reviewers and Editors

For your free review copies, please contact us.

About SiSoftware

SiSoftware, founded in 1995, is one of the leading providers of computer analysis, diagnostic and benchmarking software. The flagship product, known as “SANDRA”, was launched in 1997 and has become one of the most widely used products in its field. Nearly 700 worldwide IT publications, magazines and review sites use SANDRA to analyse the performance of today’s computers. Over 9,000 on-line reviews of computer hardware that use SANDRA are catalogued on our website alone.

Since launch, SiSoftware has always been at the forefront of the technology arena, being among the first providers of benchmarks that show the power of emerging new technologies such as multi-core, GPGPU, OpenCL, DirectCompute, x64, ARM, MIPS, NUMA, SMT (Hyper-Threading), SMP (multi-threading), AVX3, AVX2, AVX, FMA4, FMA, NEON, SSE4.2, SSE4.1, SSSE3, SSE3, SSE2, SSE, Java and .NET.

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