Or it's simply a 2x4x2 in one clock and I'm reading too much into a diagram. Basically you need an Nvidia Tesla or AMD FirePro for proper FP64 support. It might actually be doing a 2x4x4 over two clocks, in which case you might be able to alternate vector and tensor instructions? But then I suspect nvidia would be advertising it as 30 TFLOPS FP64. Anandtech does a few benchmarks for FP64 on specific cards, but I couldn't really find a large scale comparison.
We can refer to this table in the programming guide to discover relative instruction throughput: 6.1 32-bit floating-point add, multiply, multiply-add 128 64-bit floating-point add, multiply, multiply-add 4. We don’t have base clock speeds, but we know the GPU Boost clock speed on the GV100 was 1.53 GHz when it was announced three years ago and it is 1.41 GHz with the GA100 today. I am unable to find any numbers for GTX 1070. The results calculated for Radeon Instinct MI50 GPU at 1,725 MHz peak engine clock. Which I normally wouldn't really nitpick but all the other precisions are depicted doing valid multiplications. The clock speeds on the Ampere chips are actually lower than on the Volta chips, even with a sizeable process shrink from 12 nanometers to 7 nanometers. peak theoretical double precision (FP64) floating-point performance. In the white paper nvidia depicts fp64 multiplying two 2x4 matrices which isn't a valid matrix multiplication (for AxB the # of columns of A must match the number of rows of B). half types are encoded as ushorts hardware accelerated conversion (single instruction) Need to get data into fp16 format Copy to 32-bit data to device, do setup kernel before actual computation Create fp16 on host (e.g. A 1:2 ratio of FP64 cores yields a double-precision rate of 5.3. With fp64 tensors only executing at double the rate, though, it might only be doing one instruction per 2 clocks. The largest and most expensive GP100 processor offers a peak FP32 rate of 10.6 TFLOPS (if you use the peak GPU Boost frequency).
Most tensor core operations block everything else as they hog all available instruction and register file bandwidth. Overall, AMD GPUs hold a reputation for good double precision performance ratios compared to their NVIDIA counterparts. The FirePro W9100, W8100 and S9150 will give you an incredible FP64 1:2 FP32 performance. Why include CUDA cores FP64 at all if tensors are way faster?īecause you might have a use case for fp64 that isn't matrix multiplication?Ĭan they be used simultaneously, as in added together to give us 29TFLOPs of FP64 performance? Newer Hawaii architecture consumer grade GPUs are expected to provide 1:8 performance.
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