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DOE OSTI · 986273

Accelerating DNA analysis applications on GPU clusters

Tumeo, Antonino·Villa, Oreste
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Abstract

DNA analysis is an emerging application of high performance bioinformatic. Modern sequencing machinery are able to provide, in few hours, large input streams of data which needs to be matched against exponentially growing databases known fragments. The ability to recognize these patterns effectively and fastly may allow extending the scale and the reach of the investigations performed by biology scientists. Aho-Corasick is an exact, multiple pattern matching algorithm often at the base of this application. High performance systems are a promising platform to accelerate this algorithm, which is computationally intensive but also inherently parallel. Nowadays, high performance systems also include heterogeneous processing elements, such as Graphic Processing Units (GPUs), to further accelerate parallel algorithms. Unfortunately, the Aho-Corasick algorithm exhibits large performance variabilities, depending on the size of the input streams, on the number of patterns to search and on the number of matches, and poses significant challenges on current high performance software and hardware implementations. An adequate mapping of the algorithm on the target architecture, coping with the limit of the underlining hardware, is required to reach the desired high throughputs. Load balancing also plays a crucial role when considering the limited bandwidth among the nodes of these systems. In this paper we present an efficient implementation of the Aho-Corasick algorithm for high performance clusters accelerated with GPUs. We discuss how we partitioned and adapted the algorithm to fit the Tesla C1060 GPU and then present a MPI based implementation for a heterogeneous high performance cluster. We compare this implementation to MPI and MPI with pthreads based implementations for a homogeneous cluster of x86 processors, discussing the stability vs. the performance and the scaling of the solutions, taking into consideration aspects such as the bandwidth among the different nodes.

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59 BASIC BIOLOGICAL SCIENCES99 GENERAL AND MISCELLANEOUSALGORITHMSARCHITECTUREBIOLOGYDNAexascaleGPUhigh perfromance computingIMPLEMENTATIONMACHINERYPERFORMANCEPROCESSINGSTABILITYTARGETS

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Tumeo, Antonino, Villa, Oreste. 2010-06-13. Accelerating DNA analysis applications on GPU clusters. https://doi.org/10.1109/sasp.2010.5521145

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