基于PPR模型的稀疏矩阵向量乘及卷积性能优化研究

稀疏矩阵向量乘和卷积作为高性能计算的两大计算核心,是非规则和规则访存的典型代表.目前已经做了许多针对性的优化工作,但是对于大量运行着不同指令集和拥有不同计算和访存性能的机器,仍然无法判定在特定的体系结构下导致性能效率无法被完全释放的主要原因及性能瓶颈,同时也很难准确预测出程序在特定机器上可达到的最佳性能.通过使用性能模...

Research on Optimal Performance of Sparse Matrix-Vector Multiplication and Convoulution Using the Probability-Process-Ram Model

Performance models provide insightful perspectives to allow us to predict performance and propose optimization guidance.Although there has been much research,pinpointing bottlenecks of various memory access patterns and reaching high performance of both regular and irregular programs on various hardware configurations are still not trivial.In this work,we propose a novel model called probability-process-ram(PPR)to quantify the amount of compute and data transfer time on general-purpose multicore processors.The PPR model predicts the number of instruction for single-core and probability of memory access between each memory hierarchy through a newly designed cache simulator.By using the automatically extracted best optimization method and expectation,we use PPR model for analyzing and optimizing sparse matrix-vector multiplication and 1D convolution as case study for typical irregular and regular computational kernels.Then we obtain best block sizes for sparse matrices with various sparsity structures,as well as optimal optimization guidance for 1D convolution with different instruction sets support and data sizes.Comparison with Roofline model and ECM model,the proposed PPR model greatly improves prediction accuracy by the newly designed cache simulator and achieves comprehensive feedback ability.