Speedup bioinformatics applications on multicore-based processor using vectorizing and multithreading strategies - Kridsadakorn Chaichoompu; Surin Kittitornkun; Sissades Tongsima (My University, 2007)
Many computational intensive bioinformatics software, such as multiple sequence alignment, population structure analysis, etc., written in C/C++ are not multicore-aware.A multicore processor is an emerging CPU technology that combines two or more independent processors into a single package.The Single Instruction Multiple Data-stream (SIMD) paradigm is heavily utilized in this class of processors.Nevertheless, most popular compilers including Microsoft Visual C/C++ 6.0, x86 gnu C-compiler gcc do not automatically create SIMD code which can fully utilize the advancement of these processors.To harness the power of the new multicore architecture certain compiler techniques must be considered.This paper presents a generic compiling strategy to assist the compiler in improving the performance of bioinformatics applications written in C/C++.The proposed framework contains 2 main steps: multithreading and vectorizing strategies.After following the strategies, the application can achieve higher speedup by taking the advantage of multicore architecture technology.Due to the extremely fast interconnection networking among multiple cores, it is suggested that the proposed optimization could be more appropriate than making use of parallelization on a small cluster computer which has larger network latency and lower bandwidth.
Keywords: Multi-core processor; SIMD; Speedup; x86; Simultaneous multithreading; Machine Learning in Bioinformatics