Performance and scalability prediction is key to designing future High-Performance Computing (HPC) systems. System designers aim to find the proper balance between computation/network performance and power. An adequate multi-scale simulation methodology is needed for a fast and accurate design space exploration. In this regard, the Mont-Blanc project has focused on developing a complete simulation methodology at different abstraction levels that allows architectural parameter exploration and scalability analysis.
Nowadays, a popular approach for architectural performance/scalability prediction is to use a trace-oriented simulation. It relies on performing a reference simulation and collecting traces of the most relevant phenomena observed during execution. The traces are then re-used as an abstraction for some of the simulation elements (e.g., cores behaviors, memories accesses). In this way, they enable refocusing the simulation effort on other performance-critical system sub-components such as caches, communication architecture and memory sub-system. The ElasticSimMATE tool, developed in the Mont-Blanc project, operates on those foundations. It allows the capture of traces on several cores and their subsequent replay on architectures with different configurations and an arbitrary core count, ranging up to hundreds of cores.
Alejandro Nocua, Florent Bruguier, and Gilles Sassatelli from CNRS describe the methodology of ElasticSimMATE. Read their description of ElasticSimMATE and find links to papers dedicated to this tool in our Performance Prediction Tools section >