Software Managed Instruction Scratchpad Memory Optimization in Stream Architecture Based on Hot Code Analysis of Kernels

Abstract

Stream processors, such as Imagine, GPGPUs, FT64 and MASA, typically uses software managed scratchpad instruction memory which improves performance and significantly reduces energy consumption. In this paper, we build a kernel-storage model to analyze the hot spot of kernels in stream programs. Based on the analysis, we define Kernel Hot Code and prove that scratchpad instruction memory should focus on the access efficiency of it. A methodology for finding Kernel Hot Code in the kernels of different structures is presented as well. In accordance with this method, we develop HOIS for Stream Architecture, which adopts a software managed scratchpad memory to store Kernel Hot Code, and uses a small hardware managed victim cache to store the Kernel Cool Code. HOIS is evaluated by measuring the performance of six applications on the MASA_S simulation platform. The results show that HOIS can achieve high efficiency in predictable applications with little performance loss.