A. Abdelfattah, Timothy B. Costa, J. Dongarra, M. Gates, A. Haidar, S. Hammarling, N. Higham, J. Kurzak, P. Luszczek, S. Tomov, M. Zounon
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引用次数: 18
摘要
本文描述了一组Batched Basic Linear Algebra子程序(Batched BLAS或BBLAS)的标准API。重点是对小矩阵进行许多独立的BLAS操作,这些操作被分组在一起并由单个例程(称为批处理BLAS例程)处理。矩阵被分组成大小一致的组,如果所有矩阵的大小相等,则只有一个组。其目的是在高性能多核平台上提供更高效但可移植的算法实现。这些包括多核和多核CPU处理器、gpu和协处理器,以及其他具有浮点计算功能的硬件加速器。除了单精度和双精度的标准类型外,我们还包括半精度和四倍精度的标准类型。特别是,半精度在许多非常大规模的应用中使用,例如与机器学习相关的应用。
A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines
This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a Batched BLAS routine. The matrices are grouped together in uniformly sized groups, with just one group if all the matrices are of equal size. The aim is to provide more efficient, but portable, implementations of algorithms on high-performance many-core platforms. These include multicore and many-core CPU processors, GPUs and coprocessors, and other hardware accelerators with floating-point compute facility. As well as the standard types of single and double precision, we also include half and quadruple precision in the standard. In particular, half precision is used in many very large scale applications, such as those associated with machine learning.