Volta Sensor Decoding
Download >> https://shurll.com/2sBNBi
One of the more impressive features of CUDA is the use of raw memory for arithmetic computation, for example, partitioning large arrays in a bank of memory as in Figure 9. Recall, that memory is partitioned into warps of 64 threads, each operating on a 64-bit word in memory while threads within a warp do arithmetic on a 128-bit vector, using 16 floating point operations per 128-bit element. Volta’s memory layout is very similar, but with arrays of 256B aligned data instead of 64B. Volta Sensor Decoding When compiling against the CUDA driver, each loop of CUDA applications is compiled as a single accelerator block. This is because CUDA applications running on Tensor Cores are capable of processing simple depth-first loops in parallel, unlike the classic CUDA drivers. NVIDIA allows a developer to specify multiple loops using kernel launch directives as part of the CUDA application. For example, Figure 12 presents two loops that are each compiled into a single accelerator block. For loops with multiple kernels, the number of arrays to be partitioned and submitted to the GPU are specified in the CUDA application. Since all loop index arrays are used, there is no need to determine how many loop variables each loop performs. In this way, the programmer can exploit the raw memory capability of Tensor Cores and the parallelism available in a kernel’s execution.
Cumulatively, the speed-up of single code paths on the Volta V100 GPU as compared to the Pascal P100 GPU is moderate, for example, on a single SM the speedup for the Tensor Cores in a given dot product is roughly a factor of 3x to 4x. 7211a4ac4a