Reducing overheads for acquiring dynamic memory traces

Abstract

Tools for acquiring dynamic memory address information for large scale applications are important for performance modeling, optimization, and for trace-driven simulation. However, straightforward use of binary instrumentation tools for such a fine-grained task as address tracing can cause astonishing slowdown in application run time. For example, in a large scale FY05 collaboration with the Department of Defense High Performance Computing Modernization Office (HPCMO), over 1 million processor hours were expended in order to gather address information on 7 parallel applications. In this paper, we discuss in detail the issues surrounding the performance of memory address acquisition using low-level binary instrumentation tracing. We present three techniques and optimizations to improve performance: 1) SimPoint-guided sampling, 2) instrumentation tool routine optimization, and 3) reduction of instrumentation points through static application analysis. The use of these three techniques together reduces instrumented application slowdown by an order of magnitude. The techniques are generally applicable and have been deployed in the MetaSim tracer thereby enabling memory address acquisition for real-sized applications. We expect the optimizations reported here reduce the HPCMO effort by approximately 80% in FY06.