Michael Lui, Drexel University
Karthik Sangaiah, Drexel University
Mark Hempstead, Tufts University
Baris Taskin, Drexel University
Proceedings of the 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2018). April 2018.
[PDF]
Abstract
Hardware/software co-design and software profiling rest on the ability to perform a range of workload analyses. State-of-the-art tools and methods used in such analyses utilize either custom solutions or complex frameworks. There are two problems with this approach: 1) duplicated development work when moving to new and unsupported frameworks or platforms, and 2) the additional burden of in-depth knowledge required to develop the analysis tools. This work presents a methodology to solve these inefficiencies by decoupling workload analysis from the underlying techniques used to observe the workload. The interface is designed to be cross-platform and presents workloads as a set of configurable events with scalable levels-of-detail. An implementation of the methodology, PRISM, is presented which leverages two popular dynamic binary instrumentation tools, Valgrind and DynamoRIO, and additionally Intel PT via Linux perf. The goals of the methodology are three-fold: modularity, flexibility, and productivity. Three analyses are conducted using PRISM to demonstrate these properties: 1) discrepancies are assessed between workloads generated with Valgrind, DynamoRIO, and perf, 2) scalability of a complex Valgrind trace generation tool is improved, and 3) prototyping of a new dynamic loop detection and data-dependence tool is demonstrated. The average overhead of PRISM compared to in-framework analysis is 33% in the worse case and under 1% during typical analysis.