Optimizing inter-processor data locality on embedded chip multiprocessors

  • Authors:

  • G. Chen;M. Kandemir

  • Affiliations:

  • Pennsylvania State University, University Park, PA;Pennsylvania State University, University Park, PA

  • Venue:
  • Proceedings of the 5th ACM international conference on Embedded software
  • Year:
  • 2005

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Abstract

Recent research in embedded computing indicates that packing multiple processor cores on the same die is an effective way of utilizing the ever-increasing number of transistors. The advantage of placing multiple cores into a single die is that it reduces on-chip communication costs (in terms of both execution cycles and power consumption) between the processor cores that are traditionally very high in conventional high-performance parallel architectures (such as SMPs). However, on the negative side, this tighter integration exerts an even higher pressure on off-chip accesses to the memory system. This makes minimizing the number of off-chip accesses a critical optimization goal.This paper discusses a compiler-based solution to this problem for the embedded applications that perform stencil computations. An important characteristic of this solution is that it distinguishes between the intra-processor data reuse and inter-processor data reuse. The first of these captures the data reuse that occurs across loop iterations assigned to the same processor, whereas the second one represents the data reuse that takes place across the loop iterations assigned to different processors. The proposed approach then optimizes inter-processor reuse by re-organizing the loop iterations of each processor carefully, considering how data elements are shared across processors. The goal is to ensure that the different processors access the shared data within a short period of time, so that the data can be captured in the on-chip memory space at the time of the reuse. This paper also presents an evaluation of the proposed optimization and compares it to an alternate scheme that optimizes data locality for each processor in isolation. The results obtained by applying our implementation to eight loop-intensive benchmark codes from the embedded computing domain show that our approach improves over the mentioned alternate scheme by 15.6% on average.