A Memory Access Pattern-Based Program Profiling System for Dynamic Parallelism Prediction

Parallel computing is the simultaneous use of multiple computer resources to solve a computational problem. In order to automatically execute a sequential program so that inherent parallelism could utilize the underlying multi-core platforms, program profiling is necessary to learn the control and data flow dependency which is critical for predicting the parallelization feasibility and finding parallelism solutions. In literature, algorithms and tools have been designed for either dynamic or static program analysis in order to increase the accuracy and performance in discovering all aspects of the target program. However, memory and runtime overhead remain two challenge problems for program profiling. In this work, based on the PIN framework, we have designed and implemented a memory access pattern-based program profiling system for sequential program parallelization prediction. Validation results on seven SPEC 2006 benchmark programs show the effectiveness of proposed system on both memory usage and cpu time for target program dynamic profiling.