I heard about Tensor Cores on NVIDIA GPUs. Or it's simply a 2x4x2 in one clock and I'm reading too much into a diagram. jl to speed up double-precision simulations with large matrix computations and FFT (e.g. 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. By including full FP64 on it, it would lose performance in its core markets: gaming and professional graphics. NVIDIA GeForce RTX 3050 Laptop GPU remove from comparison Die Nvidia GeForce RTX 3050 Laptop GPU (oder Mobile, NVIDIADEV.2583, GN20-P0) ist die kleinste Variante der RTX 3000 Serie und basiert. The A40 is based on the GA102 GPU which is the top-of-the-line graphics/gaming oriented GPU from NVIDIA. Which I normally wouldn't really nitpick but all the other precisions are depicted doing valid multiplications. Most GPU domains do not require FP64 operations. 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). Giles, Monte Carlo evaluation of sensitivities in computational finance, HERCMA Conference, Athens, Sep. To study the performance, the number of Monte-Carlo paths is varied between 128K-2,048K. With fp64 tensors only executing at double the rate, though, it might only be doing one instruction per 2 clocks. This benchmark uses a portfolio of 15 swaptions with maturities between 4 and 40 years and 80 forward rates (and hence 80 delta Greeks). The exceptions to this are the GTX Titan cards which blur the lines between the consumer GTX series and the professional Tesla/Quadro cards. The performance generally ranges between 1:24 (Kepler) and 1:32 (Maxwell). Most tensor core operations block everything else as they hog all available instruction and register file bandwidth. NVIDIA’s GTX series are known for their great FP32 performance but are very poor in their FP64 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?
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |