Conference Paper

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

Dept. of Comput. & Inf. Sci., Nagasaki Univ., Nagasaki, Japan
DOI: 10.1109/ASAP.2010.5540796 Conference: 21st IEEE International Conference on Application-specific Systems Architectures and Processors, ASAP 2010, Rennes, France, 7-9 July 2010
Source: DBLP

ABSTRACT

This paper describes a multi-threaded parallel design and implementation of the Smith-Waterman (SW) algorithm on graphic processing units (GPUs) with NVIDIA corporation's Compute Unified Device Architecture (CUDA). Central to this is a divide and conquer approach which divides the computation of a whole pairwise sequence alignment matrix into multiple sub-matrices (or parallelograms) each running efficiently on the available hardware resources of the GPU in hand, with temporary intermediate data stored in global memory. Moreover, we use thread warps and padding techniques in order to decrease the cost of thread synchronization, as well as loop unrolling in order to reduce the cost of conditional branches. While intermediate data is stored in global memory for large queries, the most inner loop in our implementation will only access shared memory and registers. As a result of these optimizations, our implementation of the SW algorithm achieves a throughput ranging between 9.09 GCUPS (Giga Cell Update per Second) and 12.71 GCUPS on a single-GPU version, and a throughput between 29.46 GCUPS and 43.05 GCUPS on a quad-GPU platform. Compared with the best GPU implementation of the SW algorithm reported to date, our implementation achieves up to 46 % improvement in speed. The source code of our implementation is available in the public domain for Bioinformaticians to benefit from its performance.

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    • "In either case, aligning sequences in a realistic time becomes an issue due to the exponential growth of database sequences. Advancement in computing technologies has seen the use of parallel architectures such as FPGAs in [1], [2], [3], [4],[5] and GPUs in [6], [7], [8], [9] to accelerate the time consuming DP-based algorithm. In hardware, the algorithm is usually accelerated using a linear systolic array. "
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    ABSTRACT: This paper presents a novel substitution matrix loader architecture for pairwise sequence alignment. The search for sequence homology using DP-based alignment matrix computation is an important tool in molecular biology. It can be implemented either by optimal or sub-optimal approaches. Both of these methods require frequent and rapid access to the amino acids probability scores for PE (Processing Element) configuration especially in a folded systolic array. Typical FPGA implementations configure look-up tables in the pipeline PEs either by using a serial configuration chain with different look-up tables or by run time reconfiguration of the same look-up table. In the former case, configuration time increases proportionally to the number of look-up tables, while the latter case suffers from the limited reconfiguration bandwidth. Therefore, in this paper, we propose a highly efficient parallel loader to optimize both time and space complexities of protein sequence alignment in folded systolic arrays, using only two configuration elements (CEs). In addition, the proposed loader enables PEs to be updated with substitution matrix scores concurrently, with the worst case configuration time of 2 × the depth of the PE's look-up table (in clock cycles). This allows for further optimization of the most time consuming alignment matrix computation through efficient scheduling of alignment matrix computation and PE configuration. Implementation results show that the proposed architecture achieves k.NPE speed-up in configuration time (where k is the folding factor and NPE is the number of PEs) compared to classical approaches, at virtually no area overhead.
    Full-text · Conference Paper · Dec 2012

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    ABSTRACT: The paper presents parallel computational models of Smith-Waterman algorithm for CPU and GPU. An investigation is made of the performance parameters of computing similarity indexes between query sequences and a reference sequence using the suggested parallel programming models. Implementations for GPU based sequence alignment using nVIDIA CUDA and OpenCL as well as CPU based sequence alignment using OpenMP multithreaded implementation are presented. The experimental analyses are aimed at searching for similarities of the human gamma interferon protein and influenza virus.
    No preview · Conference Paper · Jan 2011
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