Opportunities for concurrent dynamic analysis with explicit inter-core communication

  • Authors:

  • Jungwoo Ha;Stephen P. Crago

  • Affiliations:

  • University of Southern California, Los Angeles, CA and Information Sciences Institute East, Arlington, VA, USA;University of Southern California, Los Angeles, CA and Information Sciences Institute East, Arlington, VA, USA

  • Venue:
  • Proceedings of the 9th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
  • Year:
  • 2010

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Abstract

Multicore is now the dominant processor trend, and the number of cores is rapidly increasing. The paradigm shift to multicore forces the redesign of the software stack, which includes dynamic analysis. Dynamic analyses provide rich features to software in various areas, such as debugging, testing, optimization, and security. However, these techniques often suffer from excessive overhead, which make it less practical. Previously, this overhead has been overcome by improved processor performance as each generation gets faster, but the performance requirements of dynamic analyses in the multicore era cannot be fulfilled without redesigning for parallelism. Scalable design of dynamic analysis is a challenging problem. Not only must the analysis itself must be parallel, but the analysis must also be decoupled from the application and run concurrently. A typical method of decoupling the analysis from the application is to send the analysis data from the application to the core that runs the analysis thread via buffering. However, buffering can perturb application cache performance, and the cache coherence protocol may not be efficient, or even implemented, with large numbers of cores in the future. This paper presents our initial effort to explore the hardware design space and software approach that will alleviate the scalability problem for dynamic analysis on multicore. We choose to make use of explicit inter-core communication that is already available in a real processor, the TILE64 processor and evaluate the opportunity for scalable dynamic analyses. We provide our model and implement concurrent call graph profiling as a case study. Our evaluation shows that pure communication overhead from the application point of view is as low as 1%. We expect that our work will help design scalable dynamic analyses and will influence the design of future many-core processors.