Cloud computing paradigms for pleasingly parallel biomedical applications

Cloud computing offers new approaches for scientific computing that leverage the major commercial hardware and software investment in this area. Closely coupled applications are still unclear in clouds as synchronization costs are still higher than on optimized MPI machines. However loosely coupled problems are very important in many fields and can achieve good cloud performance even when pleasingly parallel steps are followed by reduction operations as supported by MapReduce. However we can use clouds in several ways and here we compare four different approaches using two biomedical applications. We look at the cloud infrastructure service based virtual machine utility computing models of Amazon AWS and Microsoft Windows Azure; Map Reduce based computing frameworks Apache Hadoop (deployed on raw hardware as well as on virtual machines) and Micrsoft DryadLINQ. We compare performance showing strong variations in cost between different EC2 machine choices and comparable performance betw...

• Type: Conference
• Year: 2010
• Where: HPDC
• Authors: Thilina Gunarathne, Tak-Lon Wu, Judy Qiu, Geoffrey Fox
• Keywords: Cloud, Cloud Computing, Distributed And Parallel Computing, HPDC 2010, Utility Computing