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.
翻译:深层次学习的功效导致它在越来越多的应用中被使用。来自荷兰荷兰盾的Volta图形处理器(GPU)结构引入了一个专门的功能单位,即“电荷核心”,它有助于满足对更高深层学习性能日益增长的需求。在本文中,我们研究了荷兰盾伏特和图灵结构中高温核心的设计。我们进一步提议了伏尔塔高温核心的建筑模型。当我们实施GPU模拟器(GPGPPU-Sim)时,我们的高温核心模型与NVIDIA Titan~V GPU在运行高温核心使GEMM工作量增强的周期平均指示方面,实现了99.6 ⁇ 与NVIDIA Titan~V GPU的关联。我们还介绍了为使GPGPU-Sim能够运行CUTLASS所增加的支持,这是一个开放源的CUDA C++模板库,提供可定制的GEMM模板,使用高压核心。