Boosting the FM-index on the GPU
The recent advent of high-throughput sequencing machines producing big amounts of short reads has boosted the interest in efficient string searching techniques. As of today, many mainstream sequence alignment software tools rely on a special data structure, called the FM-index, which allows for fast...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:131873 |
| Acceso en línea: | https://ddd.uab.cat/record/131873 https://dx.doi.org/urn:doi:10.1109/TCBB.2014.2377716 |
| Access Level: | acceso abierto |
| Palabra clave: | GPGPU Bioinformatics Short read mapping FM-index Fine-grain parallelism Memory-level parallelism |
| Sumario: | The recent advent of high-throughput sequencing machines producing big amounts of short reads has boosted the interest in efficient string searching techniques. As of today, many mainstream sequence alignment software tools rely on a special data structure, called the FM-index, which allows for fast exact searches in large genomic references. However, such searches translate into a pseudo-random memory access pattern, thus making memory access the limiting factor of all computation-efficient implementations, both on CPUs and GPUs. Here we show that several strategies can be put in place to remove the memory bottleneck on the GPU: more compact indexes can be implemented by having more threads work cooperatively on larger memory blocks, and a k-step FM-index can be used to further reduce the number of memory accesses. The combination of those and other optimisations yields an implementation that is able to process about 2 Gbases of queries per second on our test platform, being about 8× faster than a comparable multi-core CPU version, and about 3× to 5× faster than the FM-index implementation on the GPU provided by the recently announced Nvidia NVBIO bioinformatics library. |
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