PVFS: a parallel file system for linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Xen-Based HPC: A Parallel I/O Perspective
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Amazon S3 for science grids: a viable solution?
DADC '08 Proceedings of the 2008 international workshop on Data-aware distributed computing
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Data Sharing Options for Scientific Workflows on Amazon EC2
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
GPFS: a shared-disk file system for large computing clusters
FAST'02 Proceedings of the 1st USENIX conference on File and storage technologies
ACIC: automatic cloud I/O configurator for parallel applications
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
ACIC: automatic cloud I/O configurator for HPC applications
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Analysis of I/O Performance on an Amazon EC2 Cluster Compute and High I/O Platform
Journal of Grid Computing
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There is a trend to migrate HPC (High Performance Computing) applications to cloud platforms, such as the Amazon EC2 Cluster Compute Instances (CCIs). While existing research has mainly focused on the performance impact of virtualized environments and interconnect technologies on parallel programs, we suggest that the configurability enabled by clouds is another important dimension to explore. Unlike on traditional HPC platforms, on a cloud-resident virtual cluster it is easy to change the I/O configurations, such as the choice of file systems, the number of I/O nodes, and the types of virtual disks, to fit the I/O requirements of different applications. In this paper, we discuss how cloud platforms can be employed to form customized and balanced I/O subsystems for individual I/O-intensive MPI applications. Through our preliminary evaluation, we demonstrate that different applications will benefit from individually tailored I/O system configurations. For a given I/O-intensive application, different I/O settings may lead to significant overall application performance or cost difference (up to 2.5-fold). Our exploration indicates that customized system configuration for HPC applications in the cloud is important and non-trivial.