Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science
Cloud technologies for bioinformatics applications
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
Highly scalable genome assembly on campus grids
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
Defining future platform requirements for e-Science clouds
Proceedings of the 1st ACM symposium on Cloud computing
Design patterns for efficient graph algorithms in MapReduce
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
MRAP: a novel MapReduce-based framework to support HPC analytics applications with access patterns
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
AzureBlast: a case study of developing science applications on the cloud
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Cloud computing paradigms for pleasingly parallel biomedical applications
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
A MapReduce workflow system for architecting scientific data intensive applications
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Proceedings of the second international workshop on Emerging computational methods for the life sciences
Rapid parallel genome indexing with MapReduce
Proceedings of the second international workshop on MapReduce and its applications
MapReducing a genomic sequencing workflow
Proceedings of the second international workshop on MapReduce and its applications
The case for being lazy: how to leverage lazy evaluation in MapReduce
Proceedings of the 2nd international workshop on Scientific cloud computing
Magellan: experiences from a science cloud
Proceedings of the 2nd international workshop on Scientific cloud computing
Sedic: privacy-aware data intensive computing on hybrid clouds
Proceedings of the 18th ACM conference on Computer and communications security
Journal of Parallel and Distributed Computing
Parallel data processing with MapReduce: a survey
ACM SIGMOD Record
Distributed workflow-driven analysis of large-scale biological data using biokepler
Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities
Riding the elephant: managing ensembles with hadoop
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Mapping short sequencing reads to distant relatives
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
DVM: towards a datacenter-scale virtual machine
VEE '12 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments
The HybrEx model for confidentiality and privacy in cloud computing
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
SkewTune: mitigating skew in mapreduce applications
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Scalable sequence similarity search and join in main memory on multi-cores
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
Large-Scale DNA sequence analysis in the cloud: a stream-based approach
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
Halt or continue: estimating progress of queries in the cloud
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Improving the diagnosis of mild hypertrophic cardiomyopathy with MapReduce
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Scalable regression tree learning on Hadoop using OpenPlanet
Proceedings of third international workshop on MapReduce and its Applications Date
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Component-based approach for programming and running scientific applications on grids and clouds
International Journal of High Performance Computing Applications
Integrating GPU-accelerated sequence alignment and SNP detection for genome resequencing analysis
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Incremental DNA sequence analysis in the cloud
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Themis: an I/O-efficient MapReduce
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Designing good algorithms for MapReduce and beyond
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Acceleration of the long read mapping on a PC-FPGA architecture (abstract only)
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
CloudSVM: training an SVM classifier in cloud computing systems
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Proceedings of the 3rd international workshop on Emerging computational methods for the life sciences
Don't match twice: redundancy-free similarity computation with MapReduce
Proceedings of the Second Workshop on Data Analytics in the Cloud
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Proceedings of the 20th European MPI Users' Group Meeting
Performance comparison under failures of MPI and MapReduce: An analytical approach
Future Generation Computer Systems
HAT: history-based auto-tuning MapReduce in heterogeneous environments
The Journal of Supercomputing
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The Journal of Supercomputing
Methodological Review: 'Big data', Hadoop and cloud computing in genomics
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Workload characteristics of DNA sequence analysis: from storage systems' perspective
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Cluster Computing
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Motivation: Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor read-mapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics. It is modeled after the short read-mapping program RMAP, and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences. This level of sensitivity could be prohibitively time consuming, but CloudBurst uses the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. Results: CloudBurst's running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a 24-processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core, while computing an identical set of alignments. Using a larger remote compute cloud with 96 cores, CloudBurst improved performance by 100-fold, reducing the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome. Availability: CloudBurst is available open-source as a model for parallelizing algorithms with MapReduce at http://cloudburst-bio.sourceforge.net/. Contact: mschatz@umiacs.umd.edu