Processor allocation in multiprogrammed distributed-memory parallel computer systems
Journal of Parallel and Distributed Computing
Scheduling with implicit information in distributed systems
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Automatically tuned collective communications
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Randomization, speculation, and adaptation in batch schedulers
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
IEEE Transactions on Parallel and Distributed Systems
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Parallel performance prediction using lost cycles analysis
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Performance Contracts: Predicting and Monitoring Grid Application Behavior
GRID '01 Proceedings of the Second International Workshop on Grid Computing
A Model for Moldable Supercomputer Jobs
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Characteristics of a Large Shared Memory Production Workload
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Cross-architecture performance predictions for scientific applications using parameterized models
Proceedings of the joint international conference on Measurement and modeling of computer systems
ATOP-space and time adaptation for parallel and grid applications via flexible data partitioning
ARM '04 Proceedings of the 3rd workshop on Adaptive and reflective middleware
Performance Analysis of MPI Collective Operations
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 15 - Volume 16
Ligature: Component Architecture for High Performance Applications
International Journal of High Performance Computing Applications
Concurrency and Computation: Practice & Experience
Modeling application performance by convolving machine signatures with application profiles
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
LOMARC — lookahead matchmaking for multi-resource coscheduling
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Time vs. space adaptation with ATOP-grid
Proceedings of the 5th workshop on Adaptive and reflective middleware (ARM '06)
Adaptive performance control for distributed scientific coupled models
Proceedings of the 21st annual international conference on Supercomputing
Time and space adaptation for computational grids with the ATOP-Grid middleware
Future Generation Computer Systems
Enhancing Prediction on Non-dedicated Clusters
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Performance problems of using system-predicted runtimes for parallel job scheduling
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
Using on-the-fly simulation for estimating the turnaround time on non-dedicated clusters
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
The Journal of Supercomputing
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Using historical information to predict future runs of parallel jobs has shown to be valuable in job scheduling. Trends toward more flexible job-scheduling techniques such as adaptive resource allocation, and toward the expansion of scheduling to grids, make runtime predictions even more important. We present a technique of employing both a user's knowledge of his/her parallel application and historical application-run data, synthesizing them to derive accurate and scalable predictions for future runs. These scalable predictions apply to runtime characteristics for different numbers of nodes (processor scalability) and different problem sizes (problem-size scalability). We employ multiple linear regression and show that for decently accurate complexity models, good prediction accuracy can be obtained.