支持深度学习加速器的GPU建模
Modeling Deep Learning Accelerator Enabled GPUs
摘要：
深度学习的有效性导致其在越来越多的应用中的使用。NVIDIA的Volta图形处理器单元（GPU）架构引入了一个专门的功能单元“张量核心”，有助于满足深度学习对更高性能日益增长的需求。在本文中，我们研究了NVIDIA的Volta和Turing架构中的张量核的设计。我们进一步提出了一个架构模型的张量核心伏。在GPU模拟器GPGPU-Sim上实现时，我们的张量核模型在运行支持张量核的GEMM工作负载时，与NVIDIA Titan~V GPU在每个周期的平均指令方面实现了99.6\%的相关性。我们还描述了为使GPGPU-Sim能够运行CUTLASS而添加的支持，CUTLASS是一个开源CUDA C++模板库，提供利用张量核心的可定制GEMM模板。
英文摘要：
The efficacy of deep learning has resulted in its use in a growing number of applications. The Volta graphics processor unit (GPU) architecture from NVIDIA introduced a specialized functional unit, the "tensor core", that helps meet the growing demand for higher performance for deep learning. In this paper we study the design of the tensor cores in NVIDIA's Volta and Turing architectures. We further propose an architectural model for the tensor cores in Volta. When implemented a GPU simulator, GPGPU-Sim, our tensor core model achieves 99.6\% correlation versus an NVIDIA Titan~V GPU in terms of average instructions per cycle when running tensor core enabled GEMM workloads. We also describe support added to enable GPGPU-Sim to run CUTLASS, an open-source CUDA C++ template library providing customizable GEMM templates that utilize tensor cores.
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