In 2019, the rapid rate at which GPU manufacturers refresh their designs,
coupled with their reluctance to disclose microarchitectural details, is still
a hurdle for those software designers who want to extract the highest possible
performance. Last year, these very reasons motivated us to dissect the Volta
GPU architecture using microbenchmarks.
The introduction in August 2018 of Turing, NVidia's latest architecture,
pressed us to update our study. In this report, we examine Turing and compare
it quantitatively against previous NVidia GPU generations. Specifically, we
study the T4 GPU: a low-power board aiming at inference applications. We
describe its improvements against its inference-oriented predecessor: the P4
GPU based on the Pascal architecture. Both T4 and P4 GPUs achieve significantly
higher frequency-per-Watt figures than their full-size counterparts.
We study the performance of the T4's TensorCores, finding a much higher
throughput on low-precision operands than on the P4 GPU. We reveal that Turing
introduces new instructions that express matrix math more succinctly. We map
Turing's instruction space, finding the same encoding as Volta, and additional
instructions. We reveal that the Turing TU104 chip has the same memory
hierarchy depth as the Volta GV100; cache levels sizes on the TU104 are
frequently twice as large as those found on the Pascal GP104. We benchmark each
constituent of the T4 memory hierarchy and find substantial overall performance
improvements over its P4 predecessor. We studied how clock throttling affects
compute-intensive workloads that hit power or thermal limits.
Many of our findings are novel, published here for the first time. All of
them can guide high-performance software developers get closer to the GPU's
peak performance.

通过使用微基准测试来研究图形处理器的体系结构，本研究揭示了最新的 Turing 架构相比先前的 Nvidia GPU 生成量的优劣，并研究了针对推理应用的 T4 GPU 的性能。