为dpc++矩阵支持扩展LLVM IR:使用Intel®高级矩阵扩展(Intel®AMX)的案例研究

Dounia Khaldi, Yuanke Luo, Bing Yu, A. Sotkin, B. Morais, M. Girkar
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引用次数: 1

摘要

本文介绍了一种用于统一不同张量硬件的dpc++矩阵扩展:Intel®Advanced matrix Extensions (Intel®AMX)到cpu、NVIDIA®tpu、IBM®POWER®MMA等。这些张量硬件单元通常由低级的本征函数或汇编来访问,以执行矩阵操作。如果没有我们在这里介绍的这种高级抽象和高效实现,科学家很难对这些特定领域的设备进行编程。我们还扩展了现有的LLVM矩阵特性来表示这个dpc++扩展,并产生了高效的Intel AMX代码生成。基于我们在Intel AMX硬件上实现该接口的案例研究,我们讨论了现有LLVM中间表示(IR)的一些局限性,以及如何克服它们以利用张量硬件。
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Extending LLVM IR for DPC++ Matrix Support: A Case Study with Intel® Advanced Matrix Extensions (Intel® AMX)
In this paper, we introduce a DPC++ matrix extension to unify different tensor hardware: Intel® Advanced Matrix Extensions (Intel® AMX) to CPUs, NVIDIA® TPUs, IBM® POWER® MMA, etc. These tensor hardware units are usually accessed by low-level intrinsics or assembly to perform matrix operations. It is hard for scientists to program these domain- specific devices without the kind of high-level abstractions and efficient implementations we introduce here.We also extend the existing LLVM matrix intrinsics to represent this DPC++ extension and yield efficient Intel AMX code generation. Based on our case study of implementing this interface on Intel AMX hardware, we discuss some of the limitations of existing LLVM Intermediate Representation (IR) and how they can be overcome to exploit tensor hardware.
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[Title page] [Copyright notice] Facilitating CoDesign with Automatic Code Similarity Learning OpenMP aware MHP Analysis for Improved Static Data-Race Detection Extending LLVM IR for DPC++ Matrix Support: A Case Study with Intel® Advanced Matrix Extensions (Intel® AMX)
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