Each Volta Sub-Core has 16 FP16 cores, 16 INT32 cores, 8 FP64 cores, 2 Tensor Cores, L0 instruction cache, 1 Warp scheduler, 1 Dispatch unit, and 64 KB register files as shown in Figure SubCoreMicroarchitecture. Though Volta's SM has twice warp scheduler compared to Pascal SM which keeps the same throughput. Thus, the warp scheduler in no way can exploit the Instruction level parallelism(ILP). The absence of a second dispatch unit associated with the scheduler implies that each warp scheduler can dispatch only one instruction per clock and is not able to dispatch a second independent instruction. Volta 's SM is partitioned into four processing blocks (Sub-Cores), each with 1 warp scheduler and 1 dispatch unit per scheduler whereas Pascal's SM is partitioned into two blocks, each with 1 warp scheduler and 2 dispatch units per scheduler. Open-source CUDA C++ templates library providing customizable GEMM templates Our Tensor Core timing model, implemented in GPGPU-Sim,Īchieves 99.6% IPC correlation versus a physical V100 GPU.īuilding upon this we also enable GPGPU-Sim to run NVIDIA’s CUTLASS, an In this paper, weĬomprehensively investigate NVIDIA’s Tensor Core implementation found in Volta and Turing architecturesĪnd propose an architectural model for it. Micro-architectural simulators model Tensor Cores.
WMMA 3 RENDERS FULL
To exploit the full capability of current NVIDIA GPUs machine learning researchersįor example 5 out of 6, 2018 Gordon Bell Award Finalists used Tensor Cores in their work. The NVIDIA Tesla V100 GPU introduced a specializedįunctional unit called the Tensor Core to meet growing demand for higher performance on this workload. The efficacy of deep learning has resulted in it becoming one of the most important applications run in data centers today.