Cloud technologies for bioinformatics applications
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
Biomedical Case Studies in Data Intensive Computing
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
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
Twister: a runtime for iterative MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Design patterns for scientific applications in DryadLINQ CTP
Proceedings of the second international workshop on Data intensive computing in the clouds
Performance comparison under failures of MPI and MapReduce: An analytical approach
Future Generation Computer Systems
SIDR: structure-aware intelligent data routing in Hadoop
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Applying high level parallel runtimes to data/compute intensive applications is becoming increasingly common. The simplicity of the MapReduce programming model and the availability of open source MapReduce runtimes such as Hadoop, are attracting more users to the MapReduce programming model. Recently, Microsoft has released DryadLINQ for academic use, allowing users to experience a new programming model and a runtime that is capable of performing large scale data/compute intensive analyses. In this paper, we present our experience in applying DryadLINQ for a series of scientific data analysis applications, identify their mapping to the DryadLINQ programming model, and compare their performances with Hadoop implementations of the same applications.