论文标题
稀疏线性代数的协同CPU-FPGA加速
Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra
论文作者
论文摘要
本文介绍了REAP，这是一种软件硬件方法，可在合作CPU-FPGA平台上进行高性能稀疏线性代数计算。收获仔细地将组织矩阵元素与计算阶段组织的任务分开。它使用CPU提供了对矩阵元素的首次重组，从而使FPGA专注于计算。我们介绍了一种新的中间表示形式，允许CPU将稀疏数据和调度决策传达给FPGA。该计算在FPGA上进行了优化，可用于使用管道的有效资源利用。与CPU上广泛使用的稀疏库库相比，REAP将稀疏一般矩阵乘法（SPGEMM）和稀疏的Cholesky分解的性能提高了3.2倍和1.85倍。
This paper describes REAP, a software-hardware approach that enables high performance sparse linear algebra computations on a cooperative CPU-FPGA platform. REAP carefully separates the task of organizing the matrix elements from the computation phase. It uses the CPU to provide a first-pass re-organization of the matrix elements, allowing the FPGA to focus on the computation. We introduce a new intermediate representation that allows the CPU to communicate the sparse data and the scheduling decisions to the FPGA. The computation is optimized on the FPGA for effective resource utilization with pipelining. REAP improves the performance of Sparse General Matrix Multiplication (SpGEMM) and Sparse Cholesky Factorization by 3.2X and 1.85X compared to widely used sparse libraries for them on the CPU, respectively.
