Extended species for code parallelization through algorithmic classification

Abstract

Multicore systems along with GPUs enabled to increase the parallelism extensively. Few compilers are enhanced to emerging issues with respect to threading and synchronization. Proper classification of algorithms and programs will benefit largely to the community of programmers to get chances for efficient parallelization. In this work we analyzed the existing species for algorithm classification, where we discuss the classification of related work and compare the amount of problems which are difficult for classification. We have selected set of algorithms which resemble in structure for various problems but perform given specific tasks. These algorithms are tested using existing tools such as Bones compiler and A-Darwin, an automatic species extraction tool. The access patterns are produced for various algorithmic kernels by running against A-Darwin and analysis is done for various code segments. We have identified that all the algorithms cannot be classified using only existing patterns and created new set of access patterns.