Server-directed collective I/O in Panda
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Collective parallel I/O
Disk-directed I/O for MIMD multiprocessors
ACM Transactions on Computer Systems (TOCS)
Data Management: NetCDF: an Interface for Scientific Data Access
IEEE Computer Graphics and Applications
A Parallel Visualization Pipeline for Terascale Earthquake Simulations
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Parallel netCDF: A High-Performance Scientific I/O Interface
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Visualizing Very Large-Scale Earthquake Simulations
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Noncontiguous locking techniques for parallel file systems
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS)
CLADE '08 Proceedings of the 6th international workshop on Challenges of large applications in distributed environments
Adaptable, metadata rich IO methods for portable high performance IO
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Terascale data organization for discovering multivariate climatic trends
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
I/O performance challenges at leadership scale
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
End-to-End Study of Parallel Volume Rendering on the IBM Blue Gene/P
ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
Exploiting inter-file access patterns using multi-collective I/O
FAST'02 Proceedings of the 1st USENIX conference on File and storage technologies
I/O strategies for parallel rendering of large time-varying volume data
EG PGV'04 Proceedings of the 5th Eurographics conference on Parallel Graphics and Visualization
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While additional cores and newer architectures, such as those provided by GPU clusters, steadily increase available compute power, memory and disk access has not kept pace, and most believe this trend will continue. It is therefore of critical importance that we design systems and algorithms which make effective use of off-processor storage. This work details our experiences using parallel file systems, details performance using current systems and software, and suggests a new API which has greater potential for increased scalability.