Dynamic percolation: a case of study on the shortcomings of traditional optimization in many-core architectures

Abstract

This paper provides a discussion on the shortcomings of traditional static optimization techniques when used in the context of many-core architectures. We argue that these shortcomings are a result of the significantly different environment found in many-cores. We analyze previous attempts at optimization of Dense Matrix Multiplication (DMM) that failed to achieve high performance despite extensive efforts towards optimization. We have found that percolation (prefetching data) and scheduling play a central role in the performance of applications. To overcome those difficulties, we have (1) fused dynamic scheduling and percolation into a dynamic percolation approach and (2) we have added additional percolation operations. Our new techniques enabled us to increase the performance of the application in our study from 44 GFLOPS (out of 80 GFLOPS possible) to 70.0 GFLOPS (operands in SRAM) or 65.6 GFLOPS (operands in DRAM).