High Performance Protein Sequence Database Scanning on the Cell Broadband Engine

Wirawan, Adrianto; Schmidt, Bertil; Zhang, Huiliang; Kwoh, Chee Keong

doi:10.1155/2009/615038

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Considering the price of the quad-core AMD processor (about 600 Euro) included in a basic workstation (about 1000 Euro), the PS3 will meet their reputation as a low-cost high-performance computing platform. Therefore the applicability of the Cell Broadband Engine for common problems in bioinformatics is of current interest and several approaches have been presented [ 28 - 30 ]. We believe that this platform is an interesting alternative for fast multimodal alignments of 2D and 3D datasets and is able to speedup other tasks in image processing.…”

Section: Discussionmentioning

confidence: 99%

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

2010

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BackgroundSolving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.ResultsWe evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de.ConclusionsThe results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.

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Section: Discussionmentioning

confidence: 99%

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

2010

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“…In the last years, many parallel variants of SW have been proposed for CPUs with Single Instruction Multiple Data (SIMD) vector instructions [Farrar 2007], clusters [Rajko and Aluru 2004] [Chen and Schmidt 2003], Field Programmable Gate Arrays (FPGAs) [Yamagutchi et al 2011] [T. F. Oliver 2005, Cell Broadband Engines (CellBEs) [Sanchez et al 2010] [Sarje and Aluru 2009] [Wirawan et al 2009], Graphics Processing Units (GPUs) [Sandes and Melo 2013a] [Liu et al 2013] [Manavski andValle 2008] [Xiao et al 2009] [Korpar and Sikic 2013] and, more recently, for the Intel Phi [Liu and Schmidt 2014b]. Even though these SW proposals commonly share some similar features or code blocks that could be reused, they usually have a design which is highly platform-dependent.…”

Section: Introductionmentioning

confidence: 99%

Masa

Sandes

Miranda

Martorell

et al. 2016

ACM Trans. Parallel Comput.

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Biological sequence alignment is a very popular application in Bioinformatics, used routinely worldwide. Many implementations of biological sequence alignment algorithms have been proposed for multicores, GPUs, FPGAs and CellBEs. These implementations are platform-specific; porting them to other systems requires considerable programming effort. This article proposes and evaluates MASA, a flexible and customizable software architecture that enables the execution of biological sequence alignment applications with three variants (local, global, and semiglobal) in multiple hardware/software platforms with block pruning, which is able to reduce significantly the amount of data processed. To attain our flexibility goals, we also propose a generic version of block pruning and developed multiple parallelization strategies as building blocks, including a new asynchronous dataflow-based parallelization, which may be combined to implement efficient aligners in different platforms. We provide four MASA aligner implementations for multicores (OmpSs and OpenMP), GPU (CUDA), and Intel Phi (OpenMP), showing that MASA is very flexible. The evaluation of our generic block pruning strategy shows that it significantly outperforms the previously proposed block pruning, being able to prune up to 66.5% of the cells when using the new dataflow-based parallelization strategy.

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“…Indeed, many proposals do exist to execute SW on clusters [5] [17] [7] and grids [23]. More recently, accelerators such as the CellBE and GPUs have been explored to execute SW [26] [2] [19] [13]. Nevertheless, most of the proposals in the literature were developed for a particular target architecture, using specific optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Parallel Biological Sequence Comparison on Heterogeneous High Performance Computing Platforms with BSP++

Hamidouche

Mendonca

Falcou

et al. 2011

2011 23rd International Symposium on Computer Architecture and High Performance Computing

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Biological Sequence Comparison is an important operation inBioinformatics that is often used to relate organisms. Smith and Waterman proposed an exact algorithm (SW) that compares two sequences in quadratic time and space. Due to high computing and memory requirements, SW is usually executed on HPC platforms such as multicore clusters and CellBEs. Since HPC architectures exhibit very different hardware characteristics, porting an application between them is an error-prone time-consuming task. BSP++ is an implementation of BSP that aims to reduce the effort to write parallel code. In this paper, we propose and evaluate a parallel BSP++ strategy to execute SW in multiple platforms like MPI, OpenMP, MPI/OpenMP, CellBE and MPI/CellBE. The results obtained with real DNA sequences show that the performance of our versions is comparable to the ones in the literature, evidencing the appropriateness and flexibility of our approach.

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High Performance Protein Sequence Database Scanning on the Cell Broadband Engine

Cited by 5 publications

References 23 publications

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

Masa

Parallel Biological Sequence Comparison on Heterogeneous High Performance Computing Platforms with BSP++

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