论文标题
高性能计算的矩阵发动机：性能还是抓住稻草的典范？
Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws?
论文作者
论文摘要
矩阵发动机或单位，不同形式和亲和力，正在现代处理器中成为现实。 CPU等。深度学习的当前和主导算法方法值得这些单位的商业投资，并从超级计算中的第一基准（即高性能Linpack）中得出，人们也希望HPC社区唤醒了热情。 因此，我们的目标是通过访问矩阵引擎来确定HPC和机器学习应用程序的实际额外好处。为此，我们对软件堆栈，代理应用程序和基准和历史批处理记录进行了深入的调查。我们对矩阵发动机进行成本效益分析，无论是渐近还是与最先进的处理器结合使用。尽管我们的经验数据将缓解热情，但我们还概述了如果它们免费出现这些密集的矩阵 - 刺激引擎。
Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep Learning merits the commercial investments in these units, and deduced from the No.1 benchmark in supercomputing, namely High Performance Linpack, one would expect an awakened enthusiasm by the HPC community, too. Hence, our goal is to identify the practical added benefits for HPC and machine learning applications by having access to matrix engines. For this purpose, we perform an in-depth survey of software stacks, proxy applications and benchmarks, and historical batch job records. We provide a cost-benefit analysis of matrix engines, both asymptotically and in conjunction with state-of-the-art processors. While our empirical data will temper the enthusiasm, we also outline opportunities to misuse these dense matrix-multiplication engines if they come for free.
