Hotspot Analysis Based Partial CUDA Acceleration of HMMER 3.0 on GPGPUs
Fahian Ahmed1, Saddam Quirem2, Gak Min3, Byeong Kil Lee4
1Fahian Ahmed, Department of Electrical and Computer Engineering, University of Texas at San Antonio, USA .
2Saddam Quirem, Department of Electrical and Computer Engineering, University of Texas at San Antonio, USA.
3Gak Min, Department of Engineering, Korea Broadcasting System, Korea.
4Byeong Kil Lee, Department of Electrical and Computer Engineering, University of Texas at San Antonio, USA .
Manuscript received on September 01, 2012. | Revised Manuscript received on September 02, 2012. | Manuscript published on September 05, 2012. | PP: 1-10 | Volume-2 Issue-4, September 2012. | Retrieval Number: D0894072412/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the introduction of many-core GPUs, there is widespread interest in using GPUs to accelerate non-graphics applications such as bioinformatics, energy, finance and several research areas. Even though the GPUs provide highly parallel processing capability, the communication interface between CPU and GPU could be a performance bottleneck due to heavy data transfer. If data transfer time is overwhelming the computation time on GPU, it would be better keep the computation on CPU instead of using GPUs. In this paper, we characterize the HMMER 3.0 and investigate performance hotspot functions. The HMMER is a bioinformatics application which is used in searching sequence databases for protein sequences. For our experiment, we use Nvidia CUDA that abstracts the GPU hardware. Based on the hotspot analysis of HMMER 3.0, we consider two factors for partial CUDA acceleration: one is the performance impact of major hotspot functions and the other one is data transfer overhead. Also, we verified that hotspot analysis based partial CUDA acceleration could provide better performance than full CUDA implementation.
Keywords: CUDA acceleration, GPGPU, HMMER, Many-core processors