NDLI: Characterization and analysis of HMMER and SVM-RFE parallel bioinformatics applications

Content Provider	IEEE Xplore Digital Library
Author	Srinivasan, U. Peng-Sheng Chen Qian Diao Chu-Cheow Lim Li, E. Yongjian Chen Ju, R. Yimin Zhang
Copyright Year	2005
Description	Author affiliation: Microprocessor Technol. Lab, Intel Corp., Santa Clara, CA, USA (Srinivasan, U.; Peng-Sheng Chen)
Abstract	Bioinformatics applications constitute an emerging data-intensive, high-performance computing (HPC) domain. While there is much research on algorithmic improvements, (2004), the actual performance of an application also depends on how well the program maps to the target hardware. This paper presents a performance study of two parallel bioinformatics applications HMMER (sequence alignment) and SVM-RFE (gene expression analysis), on Intel x86 based hyperthread-capable (2002) shared-memory multiprocessor systems. The performance characteristics varied according to the application and target hardware characteristics. For instance, HMMER is compute intensive and showed better scalability on a 3.0 GHz system versus a 2.2 GHz system. However, SVM-RFE is memory intensive and showed better absolute performance on the 2.2 GHz machine which has better memory bandwidth. The performance is also impacted by processor features, e.g. hyperthreading (HT) (2002) and prefetching. With HMMER we could obtain -75% of the performance with HT enabled with respect to doubling the number of CPUs. While load balancing optimizations can provide speedup of -30% for HMMER on a hyperthreading-enabled system, the load balancing has to adapt to the target number of processors and threads. SVM-RFE benefits differently from the same load-balancing and thread scheduling tuning. We conclude that compiler and runtime optimizations play an important role to achieve the best performance for a given bioinformatics algorithm.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Comput. Archit
Starting Page	87
Ending Page	98
File Size	562576
Page Count	12
File Format	PDF
ISBN	0780394615
DOI	10.1109/IISWC.2005.1526004
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Bioinformatics Hardware Load management Yarn Gene expression Performance analysis Multiprocessing systems Scalability Bandwidth
Content Type	Text
Resource Type	Article

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