Memory Feasibility Analysis of Parallel Tasks Running on Scratchpad-Based Architectures

Abstract

This work proposes solutions for bounding the worst-case memory space requirement for parallel tasks running on multicore platforms with scratchpad memories. It introduces a feasibility test that verifies whether memories are large enough to contain the maximum memory backlog that may be generated by the system. Both closed-form bounds and more accurate algorithmic techniques are proposed. It is shown how one can use max-plus algebra and solutions to the max-flow cut problem to efficiently solve the memory feasibility problem. Experimental results are presented to evaluate the efficiency of the proposed feasibility analysis techniques on synthetic workload and state-of-the-art benchmarks.