Smith-Waterman algorithm on heterogeneous systems: A case study

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. However, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many applications. Some heuristics are possible but at the expense of losing sensitivity. Fortunately, previous research have shown that new computing platforms such as GPUs and FPGAs are able to accelerate SW and achieve impressive speedups. In this paper we have explored SW acceleration on a heterogeneous platform equipped with an Intel Xeon Phi coprocessor. Our evaluation, using the well-known Swiss-Prot database as a benchmark, has shown that a hybrid CPU-Phi heterogeneous system is able to achieve competitive performance (62.6 GCUPS), even with moderate low-level optimisations.

[1]  Edward T. Grochowski,et al.  Larrabee: A many-Core x86 architecture for visual computing , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[2]  Yongchao Liu,et al.  CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units , 2009, BMC Research Notes.

[3]  Viktor K. Prasanna,et al.  High Performance Computing — HiPC 2002 , 2002, Lecture Notes in Computer Science.

[4]  Alexandros Stamatakis,et al.  Coupling SIMD and SIMT architectures to boost performance of a phylogeny-aware alignment kernel , 2011, BMC Bioinformatics.

[5]  Luis Piñuel,et al.  A power measurement environment for PCIe accelerators , 2014, Computer Science - Research and Development.

[6]  Yang Liu,et al.  GPU Accelerated Smith-Waterman , 2006, International Conference on Computational Science.

[7]  Enzo Rucci,et al.  Computación eficiente del alineamiento de secuencias de ADN sobre cluster de multicores , 2013 .

[8]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[9]  Francisco Tirado Fernández,et al.  2-D wavelet transform enhancement on general-purpose microprocessors: memory hierarchy and SIMD parallelism exploitation , 2002 .

[10]  Geoffrey C. Fox,et al.  Hybrid cloud and cluster computing paradigms for life science applications , 2010, BMC Bioinformatics.

[11]  Francisco Tirado,et al.  -D Wavelet Transform Enhancement on General-Purpose Microprocessors: Memory Hierarchy and SIMD Parallelism Exploitation , 2002, HiPC.

[12]  Michael Farrar,et al.  Sequence analysis Striped Smith – Waterman speeds database searches six times over other SIMD implementations , 2007 .

[13]  Kevin Truong,et al.  160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA) , 2007, BMC Bioinformatics.

[14]  Siu-Ming Yiu,et al.  SOAP3: ultra-fast GPU-based parallel alignment tool for short reads , 2012, Bioinform..

[15]  Torbjørn Rognes,et al.  Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation , 2011, BMC Bioinformatics.

[16]  Yongchao Liu,et al.  CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions , 2013, BMC Bioinformatics.

[17]  Heng Li,et al.  A survey of sequence alignment algorithms for next-generation sequencing , 2010, Briefings Bioinform..

[18]  Yongchao Liu,et al.  SWAPHI: Smith-waterman protein database search on Xeon Phi coprocessors , 2014, 2014 IEEE 25th International Conference on Application-Specific Systems, Architectures and Processors.