A gem5 trace-driven simulator for fast architecture exploration of OpenMP workloads

• Published in: Microprocessors and microsystems Vol. 67; pp. 42 - 55
• Main Authors: Nocua, Alejandro, Bruguier, Florent, Sassatelli, Gilles, Gamatié, Abdoulaye
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01-06-2019; Elsevier BV; Elsevier
• Subjects: Architecture; Computer Science; Embedded Systems; Exploration; Hardware Architecture; Multi-threaded programs; OpenMP; Parameter exploration; Parameters; Performance measurement; Scalability exploration; Scaling; Simulation; Software Engineering; Synchronism; Trace-driven simulation; Trace-driven simulation; Parameter exploration; OpenMP; Multi-threaded programs; Scalability exploration
• ISSN: 0141-9331; 1872-9436
• DOI: 10.1016/j.micpro.2019.01.008

Architecture parameter exploration is one of the main analysis that needs to be performed in order to ensure that a multicore system has an optimal set of parameters. The main drawback of current simulation approaches is the long simulation times in order to extract performance metrics while varying a system parameter. Trace-driven simulation approaches allow to abstract selected components of the system under analysis by creating traces during the execution time of an application. This technique reduces the simulation time while keeping the accuracy levels. Even tough trace-driven techniques have proven to be useful, most of them are focused on mono-core systems, and some do not completely capture the behavior of multi-threaded programs. In this regard, we developed a trace-driven simulation approach based on the gem5 framework. This approach is based on a collection phase of the instructions and dependencies of a given application and two extra traces depending on the selected analysis. It allows weak and strong scaling analysis along with the possibility to perform extensive parameter exploration analysis. For weak scaling analysis simulations, we collect synchronization traces for OpenMP applications, and for strong scaling analysis, we collect task traces for OmpSs applications.