Vapor: Virtual Machine Based Parallel Program Profiling Framework

Abstract

It is hard to execute parallel program efficiently on man-core platform because we could not divide program into appropriate granularity executed simultaneously. Based on virtual machine and binary translation technologies the article proposes the vapor profiling framework that uses SBIRP instruction in-place replacement method to collect program’s run-time control flow and data flow information precisely. Moreover, it explains how to create control flow and data flow dependency graphs. Experiment results prove that vapor has better performance than traditional methods.