Andrew: a distributed personal computing environment
Communications of the ACM - The MIT Press scientific computation series
Server-directed collective I/O in Panda
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Remote I/O: fast access to distant storage
Proceedings of the fifth workshop on I/O in parallel and distributed systems
GASS: a data movement and access service for wide area computing systems
Proceedings of the sixth workshop on I/O in parallel and distributed systems
Smart file objects: a remote file access paradigm
Proceedings of the sixth workshop on I/O in parallel and distributed systems
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Improving Collective I/O Performance Using Threads
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Extending the operating system at the user level: the Ufo global file system
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
Enhancing Data Migration Performance via Parallel Data Compression
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A High-Performance Cluster Storage Server
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
High-Level Buffering for Hiding Periodic Output Cost in Scientific Simulations
IEEE Transactions on Parallel and Distributed Systems
| Hi-index | 0.00 |
Large-scale parallel simulations are a popular tool for investigating phenomena ranging from nuclear explosions to protein folding. These codes produce copious output that must be moved to the workstation where it will be visualized. Scientists have a variety of tools to help them with this data movement, and often have several different platforms available to them for their runs. Thus questions arise such as, which data migration approach is best for a particular code and platform? Which will provide the best end-to-end response time, or lowest cost? Scientists also control how much data is output, and how often. From a scientific perspective, the more output the better; but from a cost and response time perspective, how much output is too much? To answer these questions, we built performance models for data migration approaches and verified them on parallel and sequential platforms. We use a 3D hydrodynamics code to show how scientists can use the models to predict performance and tune the I/O aspects of their codes.