MASA: a multi-platform architecture for sequence aligners with block pruning
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 and porting them to other s...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/99739 |
| Acceso en línea: | https://hdl.handle.net/2117/99739 https://dx.doi.org/10.1145/2858656 |
| Access Level: | acceso abierto |
| Palabra clave: | Parallel programming (Computer science) Biological Sequence Alignment Parallel Algorithms GPU multicores Intel Phi Programació en paral·lel (Informàtica) Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures paral·leles |
| Sumario: | 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 and porting them to other systems requires considerable programming effort. This paper 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 semi-global) 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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