METRIC: tracking down inefficiencies in the memory hierarchy via binary rewriting

  • Authors:

  • Jaydeep Marathe;Frank Mueller;Tushar Mohan;Bronis R. de Supinski;Sally A. McKee;Andy Yoo

  • Affiliations:

  • North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;University of Utah, Salt Lake City, UT;Lawrence Livermore National Lab, Livermore, CA;Cornell University, Ithaca, NY;Lawrence Livermore National Lab, Livermore, CA

  • Venue:
  • Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we present METRIC, an environment for determining memory inefficiencies by examining data traces. METRIC is designed to alter the performance behavior of applications that are mostly constrained by their latency to resolve memory references. We make four primary contributions in this paper. First, we present methods to extract partial data traces from running applications by observing their memory behavior via dynamic binary rewriting. Second, we present a methodology to represent partial data traces in constant space for regular references through a novel technique for online compression of reference streams. Third, we employ offline cache simulation to derive indications about memory performance bottlenecks from partial data traces. By exploiting summarized memory metrics, by-reference metrics as well as cache evictor information, we can pin-point the sources of performance problems. Fourth, we demonstrate the ability to derive opportunities for optimizations and assess their benefits in several experiments resulting in up to 40% lower miss ratios.