In this paper, we propose a fine-grained cycle sharing (FGCS) system capable of exploiting idle graphics processing units (GPUs) for accelerating sequence homology search in local area network environments. Our system exploits short idle periods on GPUs by running small parts of guest programs such that each part can be completed within hundreds of milliseconds. To detect such short idle periods from the pool of registered resources, our system continuously monitors keyboard and mouse activities via event handlers rather than waiting for a screensaver, as is typically deployed in existing systems. Our system also divides guest tasks into small parts according to a performance model that estimates execution times of the parts. This task division strategy minimizes any disruption to the owners of the GPU resources. Experimental results show that our FGCS system running on two non-dedicated GPUs achieves 111-116% of the throughput achieved by a single dedicated GPU. Furthermore, our system provides over two times the throughput of a screensaver-based system. We also show that the idle periods detected by our system constitute half of the system uptime. We believe that the GPUs hidden and often unused in office environments provide a powerful solution to sequence homology search.

(Fumihiko Ino, Yuma Munekawa, and Kenichi Hagihara, “Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs”, accepted for publication in IEEE Transactions on Parallel and Distributed Systems, Sep. 2011. [DOI])

This paper describes the approach and the speedup obtained in performing Smith-Waterman database searches on heterogeneous platforms comprising of multi core CPU and multi GPU systems. Most of the advanced and optimized Smith-Waterman algorithm versions have demonstrated remarkable speedup over NCBI BLAST versions, viz., SWPS3 based on x86 SSE2 instructions and CUDASW++ v2.0 CUDA implementation on GPU. This work proposes a hybrid Smith-Waterman algorithm that integrates the state-of-the art CPU and GPU solutions for accelerating Smith-Waterman algorithm in which GPU acts as a co-processor and shares the workload with the CPU enabling us to realize remarkable performance of over 70 GCUPS resulting from simultaneous CPU-GPU execution. In this work, both CPU and GPU are graded equally in performance for Smith-Waterman rather than previous approaches of porting the computationally intensive portions onto the GPUs or a naive multi-core CPU approach.

(J. Singh and I. Aruni: “Accelerating Smith-Waterman on Heterogeneous CPU-GPU Systems”, Proceedings of Bioinformatics and Biomedical Engineering (iCBBE), May 2011. [DOI])

MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of the alignment code and nearly 4× faster in total computation time than MUMmerGPU 1.0. We exhaustively examined 128 GPU data layout configurations to improve register footprint and running time and conclude higher occupancy has greater impact than reduced latency. MUMmerGPU is available open-source at http://www.mummergpu.sourceforge.net.

(Trapnell, C, Schatz, MC (2009) Optimizing data intensive GPGPU computations for DNA sequence alignment. Parallel Computing doi:10.1016/j.parco.2009.05.002)