MemSpy: analyzing memory system bottlenecks in programs

  • Authors:

  • Margaret Martonosi;Anoop Gupta;Thomas Anderson

  • Affiliations:

  • Computer Systems Laboratory, Stanford University, CA;Computer Systems Laboratory, Stanford University, CA;Computer Science Division, Univ. of California, Berkeley, CA

  • Venue:
  • SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
  • Year:
  • 1992

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Abstract

To cope with the increasing difference between processor and main memory speeds, modern computer systems use deep memory hierarchies. In the presence of such hierarchies, the performance attained by an application is largely determined by its memory reference behavior—if most references hit in the cache, the performance is significantly higher than if most references have to go to main memory. Frequently, it is possible for the programmer to restructure the data or code to achieve better memory reference behavior. Unfortunately, most existing performance debugging tools do not assist the programmer in this component of the overall performance tuning task.This paper describes MemSpy, a prototype tool that helps programmers identify and fix memory bottlenecks in both sequential and parallel programs. A key aspect of MemSpy is that it introduces the notion of data oriented, in addition to code oriented, performance tuning. Thus, for both source level code objects and data objects, MemSpy provides information such as cache miss rates, causes of cache misses, and in multiprocessors, information on cache invalidations and local versus remote memory misses. MemSpy also introduces a concise matrix presentation to allow programmers to view both code and data oriented statistics at the same time. This paper presents design and implementation issues for MemSpy, and gives a detailed case study using MemSpy to tune a parallel sparse matrix application. It shows how MemSpy helps pinpoint memory system bottlenecks, such as poor spatial locality and interference among data structures, and suggests paths for improvement.