![]() Perhaps a better way to state it would be that for GPUs of this type, the 1:2 ratio reflects the ratio of peak theoretical performance for FP64:FP32 comparison. ![]() M2070, M2090, etc.) and it is applicable to Tesla P100. Volta’s full GV100 GPU sports 84 SMs (each SM features four texture units, 64 FP32 cores, 64 INT32 cores, 32 FP64 cores) fed by 128KB of shared L1 cache per SM that can be configured to. Note that this 1:2 ratio does not apply to all Tesla processors, but it is applicable to Fermi Tesla processors (e.g. Can it be accelerated using Tensor Cores and still get fp64 accuracy Multi-precision numerical methods. ![]() codes where the limiter in each case is this particular metric). Actually, among the floating point cores, the presence of 2 FP64 cores per SM performing double precision floating point operations should be mentioned, although the FP64 cores are not. Yes, that is what it means (for suitably measured compute-bound codes, i.e. Since ratio of cores for Tesla is 1:2 does it mean that double precision performance is at max one half of performance of single precision kernel? You then only need to multiply those numbers by the number of SMs in your cc6.1 GPU (which you can obtain with deviceQueryfor example), to get the total FP32 and the total FP64 cores in the GPU.Īlso followup question. It means that there are 128 FP32 floating-point units, and 4 FP64 floating point units per SM. We can refer to this table in the programming guide to discover relative instruction throughput: 6.1ģ2-bit floating-point add, multiply, multiply-add 128Ħ4-bit floating-point add, multiply, multiply-add 4 I am unable to find any numbers for GTX 1070. I have found information about how much does Tesla P100 contain CUDA cores per one SM.
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