Logo image
Towards Cross-Framework Workload Analysis via Flexible Event-Driven Interfaces
Conference proceeding

Towards Cross-Framework Workload Analysis via Flexible Event-Driven Interfaces

Michael Lui, Karthik Sangaiah, Mark Hempstead, Baris Taskin and IEEE
2018 IEEE INTERNATIONAL SYMPOSIUM ON PERFORMANCE ANALYSIS OF SYSTEMS AND SOFTWARE (ISPASS), pp 169-178
01 Jan 2018

Abstract

Computer Science Computer Science, Hardware & Architecture Computer Science, Software Engineering Science & Technology Technology
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.

Metrics

6 Record Views
4 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Hardware & Architecture
Computer Science, Software Engineering
Logo image