CBESW: Sequence Alignment on the Playstation 3

Wirawan, Adrianto; Kwoh, Chee Keong; Hieu, Nim Tri T.; Schmidt, Bertil

doi:10.1186/1471-2105-9-377

Cited by 46 publications

(40 citation statements)

References 17 publications

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“…In particular vector instructions of modern scalar processors were used for improving speed of execution of a single alignment of two sequences [23][24][25][26]. An alternative idea is to execute alignment of several database sequences with a single query [27,28].…”

Section: Introductionmentioning

confidence: 99%

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Ligowski

Rudnicki

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

129

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Abstract-The Smith Waterman algorithm for sequence alignment is one of the main tools of bioinformatics. It is used for sequence similarity searches and alignment of similar sequences. The high end Graphical Processing Unit (GPU), used for processing graphics on desktop computers, deliver computational capabilities exceeding those of CPUs by an order of magnitude. Recently these capabilities became accessible for general purpose computations thanks to CUDA programming environment on Nvidia GPUs and ATI Stream Computing environment on ATI GPUs. Here we present an efficient implementation of the Smith Waterman algorithm on the Nvidia GPU. The algorithm achieves more than 3.5 times higher per core performance than previously published implementation of the Smith Waterman algorithm on GPU, reaching more than 70% of theoretical hardware performance. The differences between current and earlier approaches are described showing the example for writing efficient code on GPU.

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Section: Introductionmentioning

confidence: 99%

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Ligowski

Rudnicki

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

129

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“…(More recent work includes that of Manavski and Valle [63]. While Wirawan et al [64] accelerate the SmithWaterman algorithm by running it on PlayStation games consoles. )…”

Section: Bioinformatics Applicationsmentioning

confidence: 99%

Graphics processing units and genetic programming: an overview

Langdon

2011

Soft Comput

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A top end graphics card (GPU) plus a suitable SIMD interpreter, can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular we survey genetic programming (GP) use with GPU. We give several applications from Bioinformatics and show how the fastest GP is based on an interpreter rather than compilation. Finally using GP to generate GPU CUDA kernel C++ code is sketched.

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“…They demonstrated a reduction by a factor of up to sixteen in the look up times for most proteins. SmithWaterman has also been ported to the Sony PlayStation 3 [54] and the GeForce 8800 (CUDA) [35]. Schatz et al also used CUDA to port another sequence searching tool (MUMmer) to another G80 GPU and obtained speed ups of 3-10 when matching short DNA strands against much longer sequences [49].…”

Section: Gpus In Bioinformatics and Computational Intelligencementioning

confidence: 99%

Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units

Langdon

2010

Studies in Computational Intelligence

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A suitable single instruction multiple data GP interpreter can achieve high (Giga GPop/second) performance on a SIMD GPU graphics card by simultaneously running multiple diverse members of the genetic programming population. SPMD dataflow parallelisation is achieved because the single interpreter treats the different GP programs as data. On a single 128 node parallel nVidia GeForce 8800 GTX GPU, the interpreter can out run a compiled approach, where data parallelisation comes only by running a single program at a time across multiple inputs. The RapidMind GPGPU Linux C++ system has been demonstrated by predicting ten year+ outcome of breast cancer from a dataset containing a million inputs. NCBI GEO GSE3494 contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies. Multiple GP runs each with a population of five million programs winnow useful variables from the chaff at more than 500 million GPops per second. Sources available via FTP.

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CBESW: Sequence Alignment on the Playstation 3

Cited by 46 publications

References 17 publications

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Graphics processing units and genetic programming: an overview

Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units

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