SPM-Sieve: A framework for assisting data partitioning in scratch pad memory based systems
The memory hierarchy continues to have a substantial effect on application performance. This paper explores the potential of high-level application understanding in improving the performance of modern memory hierarchies, decomposing the often-chaotic address stream of an application into multiple more regular streams. We present two orthogonal methodologies. The first is a system called DTrack that decomposes the dynamic reference stream of a C program by tagging each reference with its global variable or heap call-site name. The second is a technique to determine the correct granularity at which to study the global phase behavior of applications. Applying these twin analysis methods to twelve CSPEC2000 benchmarks, we demonstrate that they reveal data structure interactions that remain obscured with traditional aggregation-based analysis methods. Such a characterization creates a rich profile of an application's memory behavior that highlights the most memory-intensive data structures and program phases, and we illustrate how this profile can lead system and application designers to a deeper understanding of the applications they study.