A work stealing based approach for enabling scalable optimal sequence homology detection

Daily, Jeff; Kalyanaraman, Ananth; Krishnamoorthy, Sriram; Vishnu, Abhinav

doi:10.1016/j.jpdc.2014.08.009

Title: A work stealing based approach for enabling scalable optimal sequence homology detection

Full Record
Other Related Research

Authors:: Daily, Jeff; Kalyanaraman, Ananth; Krishnamoorthy, Sriram; Vishnu, Abhinav

Publication Date:: Fri May 01 00:00:00 EDT 2015

Sponsoring Org.:: USDOE

OSTI Identifier:: 1197975

Grant/Contract Number:: SC-0006516; AC02-05CH11231

Resource Type:: Published Article

Journal Name:: Journal of Parallel and Distributed Computing

Additional Journal Information:: Journal Name: Journal of Parallel and Distributed Computing Journal Volume: 79-80 Journal Issue: C; Journal ID: ISSN 0743-7315

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Citation Formats


                    Daily, Jeff, Kalyanaraman, Ananth, Krishnamoorthy, Sriram, and Vishnu, Abhinav. A work stealing based approach for enabling scalable optimal sequence homology detection.  United States: N. p., 2015. 
Web.  doi:10.1016/j.jpdc.2014.08.009.

Copy to clipboard


                    Daily, Jeff, Kalyanaraman, Ananth, Krishnamoorthy, Sriram, & Vishnu, Abhinav. A work stealing based approach for enabling scalable optimal sequence homology detection.  United States.  https://doi.org/10.1016/j.jpdc.2014.08.009

Copy to clipboard


                    Daily, Jeff, Kalyanaraman, Ananth, Krishnamoorthy, Sriram, and Vishnu, Abhinav. Fri .  
"A work stealing based approach for enabling scalable optimal sequence homology detection".  United States.  https://doi.org/10.1016/j.jpdc.2014.08.009.

Copy to clipboard


                    
@article{osti_1197975,

  title        = {A work stealing based approach for enabling scalable optimal sequence homology detection},

  author       = {Daily, Jeff and Kalyanaraman, Ananth and Krishnamoorthy, Sriram and Vishnu, Abhinav},

  abstractNote = {},

  doi          = {10.1016/j.jpdc.2014.08.009},

  journal      = {Journal of Parallel and Distributed Computing},

  number       = C,

  volume       = 79-80,

  place        = {United States},

  year         = {Fri May 01 00:00:00 EDT 2015},

  month        = {Fri May 01 00:00:00 EDT 2015}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Publisher's Version of Record
https://doi.org/10.1016/j.jpdc.2014.08.009

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 9 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

A work stealing based approach for enabling scalable optimal sequence homology detection

Journal Article Daily, Jeffrey A. ; Kalyanaraman, Anantharaman ; Krishnamoorthy, Sriram ; ... - Journal of Parallel and Distributed Computing

Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or “homologous”) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. Here, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronousmore »« less
https://doi.org/10.1016/j.jpdc.2014.08.009
Towards Scalable Optimal Sequence Homology Detection

Conference Daily, Jeffrey A. ; Krishnamoorthy, Sriram ; Kalyanaraman, Anantharaman

Abstract—The field of bioinformatics and computational biol- ogy is experiencing a data revolution — experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. This has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a funda- mental problem in large-scale sequence analysis with numerousmore »« less
https://doi.org/10.1109/HiPC.2012.6507523
A Scalable Parallel Algorithm for Large-Scale Protein Sequence Homology Detection

Conference Wu, Changjun ; Kalyanaraman, Anantharaman ; Cannon, William R

Protein sequence homology detection is a fundamental problem in computational molecular biology, with a pervasive application in nearly all analyses that aim to structurally and functionally characterize protein molecules. While detecting homology between two protein sequences is computationally inexpensive, detecting pairwise homology at a large-scale becomes prohibitive, requiring millions of CPU hours. Yet, there is currently no efficient method available to parallelize this kernel. In this paper, we present the key characteristics that make this problem particularly hard to parallelize, and then propose a new parallel algorithm that is suited for large-scale protein sequence data. Our method, called pGraph, ismore »« less
https://doi.org/10.1109/ICPP.2010.41
Scalable Work Stealing

Conference Dinan, James S ; Larkins, D B ; Sadayappan, Ponnuswamy ; ...

Irregular and dynamic parallel applications pose significant challenges to achieving scalable performance on large-scale multicore clusters. These applications often require ongoing, dynamic load balancing in order to maintain efficiency. While effective at small scale, centralized load balancing schemes quickly become a bottleneck on large-scale clusters. Work stealing is a popular approach to distributed dynamic load balancing; however its performance on large-scale clusters is not well understood. Prior work on work stealing has largely focused on shared memory machines. In this work we investigate the design and scalability of work stealing on modern distributed memory systems. We demonstrate high efficiency andmore »« less
https://doi.org/10.1145/1654059.1654113
FastEtch : A Fast Sketch-Based Assembler for Genomes

Journal Article Ghosh, Priyanka ; Kalyanaraman, Ananth - IEEE/ACM Transactions on Computational Biology and Bioinformatics
Cited by 2
https://doi.org/10.1109/TCBB.2017.2737999

Similar Records