One optimized I/O configuration per HPC application: leveraging the configurability of cloud

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.

[1]  Garth Gibson,et al.  pWalrus: Towards better integration of parallel file systems into cloud storage , 2010, 2010 IEEE International Conference On Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS).

[2]  Robert B. Ross,et al.  PVFS: A Parallel File System for Linux Clusters , 2000, Annual Linux Showcase & Conference.

[3]  John Shalf,et al.  Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[4]  Lustre : A Scalable , High-Performance File System Cluster , 2003 .

[5]  Rob VanderWijngaart,et al.  NAS Parallel Benchmarks I/O Version 2.4. 2.4 , 2002 .

[6]  Jeffrey S. Vetter,et al.  Xen-Based HPC: A Parallel I/O Perspective , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[7]  Matei Ripeanu,et al.  Amazon S3 for science grids: a viable solution? , 2008, DADC '08.

[8]  G. Bruce Berriman,et al.  Data Sharing Options for Scientific Workflows on Amazon EC2 , 2010, 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis.

[9]  Rudolf Eigenmann,et al.  Parallel Ocean Program (POP) , 2011, Encyclopedia of Parallel Computing.

[10]  J. Shalf,et al.  Characterizing and predicting the I/O performance of HPC applications using a parameterized synthetic benchmark , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[11]  Frank B. Schmuck,et al.  GPFS: A Shared-Disk File System for Large Computing Clusters , 2002, FAST.