There is a Mixed-precision training mode which uses both single- and half-precision representations. This also makes the double precision (FP64) not useful, because additional precision gives nothing, while being slower. There is a trend towards using FP16 (half precision) instead of FP32 (single precision) because lower precision calculations seem to be not critical for neural networks. It seems that Intel do not like to participate in these comparisons. If the article mentioned FP64 performance, and the AVX base frequency is lower that 2.60 GHz, then 1 TFLOPS FP64 could be understandable.įor a 6-core i7-6850K (Broadwell) with no AVX, working at 3.60 GHz base frequency the estimate is 6*3.6*32 = 690 GFLOPS FP32.Ĭorrect me if I made mistakes somewhere, pls.īTW, if you know a reliable source of Intel/AMD peak/real performance metrics (in FLOPS, not a special scores), let me know. For workloads heavy in AVX-512 the CPU reduces the clock frequency.
The base frequency is applicable only to non-AVX workloads. Maybe it’s because the frequency behaviour is complex, especially in the case of AVX modes. It’s 3x times larger than “teraflop-speed”. So, for a single 18-core 7980XE (Skylake-X) working at base frequency of 2.60 GHz (in Turbo mode it can be up to 4.20 GHz) the Peak Performance in GFLOPS is 18*2.6*64 = 2995, so near 3 TFLOPS FP32.
#TESLA P100 FP64 SERIES#
Intel Haswell/Broadwell/Skylake performs 32 SP FLOPs/cycle, Skylake-X performs 64 SP FLOPs/cycle (thanks to AVX-512, see the CPU post of the series on more details on AVX-512). Here is a more popular version with a bit of history.Ĭalculating FLOPS for modern processors is complicated due to features such as vectorization, fused multiply-add, hyperthreading, “turbo” mode and so on. Half the price GTX 1080 Ti delivers 10x more TFLOPS.
#TESLA P100 FP64 PC#
You can find the tables with the data and comparisons in my Google Doc here.įor comparison, the new 18-core Intel Core i9 Extreme Edition ( i9–7980XE) with 160W TDP and $1999 recommended price is called the ‘First teraflop-speed’ consumer PC chip (but I’m not sure exactly which TFLOPS are mentioned, I suppose FP64). You’ll see examples of real performance comparing to the peak performance soon. But anyway, peak performance is a proxy for the real-world performance, so treat it wisely. Maybe even once we’ll have a special AI to solve this optimization problems (like Google did it in its papers ).
#TESLA P100 FP64 SOFTWARE#
So here is a niche for special-purpose software to optimize you DL-related calculations, and NVIDIA TensorRT is a one example of such class of software, dedicated specifically to inference (but I think it generally works on the higher levels than I described), others could be implemented into DL frameworks (like we have optimization options in compilers) and special libraries. Moreover it requires a completely different skill set and expertise, with the low level understanding of GPU architecture (or several architectures). Maybe it is achievable, but I had not seen any DL developer wanting to spend time on such hardcore optimizations instead of working with the neural networks themselves. It’s because to achieve the peak performance you have to heavily optimize your calculations, keeping all parts of the processing pipeline optimally loaded, avoiding bottlenecks and so on. More correctly to say, the real performance can be far behind the peak performance (and you’ll see it below). Important: Peak performance can be very far from the performance on the real tasks. But anyway, FP32 is a good common ground, because you’ll see that there are many caveats with others. So, you may see other charts with larger numbers. This is not the only option to measure, you’ll learn about FP16/FP64/INT8 soon. Important: This is FP32, a single-precision float, performance. There are some activities and I’ll return to AMD at the end of the post.
For the inference they are good as well, but here may play other factors (like size, power consumption, price, etc) depending on the target system you are developing a neural network (NN) for.Īmong GPUs the NVIDIA ones are beyond comparison, because almost every DL framework supports NVIDIA GPUs while have no support of AMD GPUs. The training is much more calculation intensive process than the inference, and GPUs are especially important for the training mode. The most modern DL systems are a mix of CPU and GPU, where the GPU does the heavy lifting, and CPU is responsible for loading the data into/from the memory of a graphics card and orchestrating the calculations. Matrix multiplications, the core of DL right now, are among these. Modern GPUs contain a lot of simple processors (cores) and are highly parallel, which makes them very effective in running some algorithms. GPUs, Graphics Processing Units, are specialized processors originally created for computer graphics tasks.
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