#Wmma 5 database code#
When I run the code you have shown now, on a Tesla V100: $ cat t18.cu Wmma::store_matrix_sync( C, acc_frag, 16, wmma::mem_col_major ) Matrix A is read once, and accumulator is filled once. _half* C = smem + threadIdx.y * 1024 + threadIdx.y * 16 + 512 _half* B = smem + threadIdx.y * 1024 + threadIdx.y * 16 + 256 _half* A = smem + threadIdx.y * 1024 + threadIdx.y * 16 Warps within a block read from 256 byte aligned strided adresses to avoid Here, I only load a single matrix from shared in each iteration but my threads still stall entirely on MIO Throttle and Short Scoreboard. I would still love some insight into why the code below does not give any kind of performance (issue is still at. I have gone through the documentation and code, but fail to see the important difference in their approach. In my case, CUTLASS does not help much, as I actually need to do 16x16 matrix multiplications (not as part of a larger GEMM) within my kernel. Thank you for your reply! I realize I oversimplified my test case a bit, and unintentionally left a global memory write in the end. Contribute to NVIDIA/cutlass development by creating an account on GitHub.
#Wmma 5 database how to#
Not only are there a lot of flexible building blocks there, but it probably has the best documentation on how to do it: GitHub NVIDIA/cutlassĬUDA Templates for Linear Algebra Subroutines. The necessary programming techniques here are pretty involved, and so the recommendation would be to use CUTLASS if possible. Scroll down to “Loop Unrolling” and read that section. You can get a sense of this necessity by reading the CUTLASS documentation This means its necessary to overlap the loading/unloading operations with compute operations, at the kernel level.
#Wmma 5 database full#
In order to get anything approaching full rated performance, it will be necessary to keep these units continuously busy with multiple “back-to-back” operations. For much of the duration of the execution time of your kernel, the tensor core units across the device are idle. It does not matter how much of this you do, it will not be efficient. In particular your paradigm is load-compute-unload. The construction of your kernel represents a fairly naive usage of the wmma functionality.
(This is on a GTX2070 (sm 7.5) mobile card (Razer Blade 15)) What is wrong here? How do I come anywhere near the 100 TFlops (or whatever) that cublas manages? Any tips or thoughts are very welcome! By choosing different combinations of row/col major for the matrices, I can make it change the stall reasons around a bit, but I cannot get issued warps over 0.2. If I switch so that the A matrix is also in shared, it stalls in the same place, but reports “MIO throttle” as the main offender. The largest stall reason by far is “Stall LG throttle” (~18 cycles/intruction), and looking at the source I find that it stalls mainly on the “load_matrix_sync(…d_A…)” instruction. Looking at the “Scheduler Statistics” in nsight, I find that 6 warps are active, but only 0.45 are eligible and 0.18 are issued. However, when profiling the code in Nsight, it takes 145us (very approximately 2TFlops if I didn’t mess up my calculations).
I would have expected this to run very quickly, as the global memory access is always cached (which I have verified in Nsight: 99.98% L1 hitrate). (For simpler timing, no results are written, but I have verified that the SASS code is correct and does not optimize away anything). So the kernel simply multiplies two matrices, one from global memory and the other from shared, and stores the result in shared memory. Wmma::store_matrix_sync( d_C, acc_frag, 16, wmma::mem_row_major ) įor ( auto& a : A ) a = float( rand() ) / RAND_MAX įor ( auto& b : B ) b = float( rand() ) / RAND_MAX ĬudaMalloc( &d_A, 16 * 16 * sizeof( dtype ) ) ĬudaMalloc( &d_B, 16 * 16 * sizeof( dtype ) ) ĬudaMalloc( &d_C, 16 * 16 * sizeof( dtype ) ) ĬudaMemcpy( d_A, A.data(), 16 * 16 * sizeof( dtype ), cudaMemcpyHostToDevice ) ĬudaMemcpy( d_B, B.data(), 16 * 16 * sizeof( dtype ), cudaMemcpyHostToDevice ) ĬudaMemcpy( d_C, C.data(), 16 * 16 * sizeof( dtype ), cudaMemcpyHostToDevice ) Wmma::mma_sync( acc_frag, a_frag, b_frag, acc_frag ) Wmma::load_matrix_sync( a_frag, d_A, 16 ) _global_ void test_wmma( _half* d_A, _half* d_B, _half* d_C )
In the hope that someone here can help me understand what I am doing wrong, I will post a small repro-case here. I am attempting to use the tensor cores efficiently in a custom DL inference kernel, but I get very poor performance.
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