The elusive goal of workload characterization
ACM SIGMETRICS Performance Evaluation Review
IEEE Transactions on Parallel and Distributed Systems
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
The EASY - LoadLeveler API Project
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Comparing Logs and Models of Parallel Workloads Using the Co-plot Method
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Core Algorithms of the Maui Scheduler
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
The portable batch scheduler and the maui scheduler on linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
What is worth learning from parallel workloads?: a user and session based analysis
Proceedings of the 19th annual international conference on Supercomputing
Locality of sampling and diversity in parallel system workloads
Proceedings of the 21st annual international conference on Supercomputing
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework
Proceedings of the 20th international symposium on High performance distributed computing
ValuePack: value-based scheduling framework for CPU-GPU clusters
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Workload resampling for performance evaluation of parallel job schedulers
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Extending goal-oriented parallel computer job scheduling policies to heterogeneous systems
The Journal of Supercomputing
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The scheduler is a key component in determining the overall performance of a parallel computer, and as we show here, the schedulers in wide use today exhibit large unexplained gaps in performance during their operation. Also, different scheduling algorithms often vary in the gaps they show, suggesting that choosing the correct scheduler for each time frame can improve overall performance. We present two adaptive algorithms that achieve this: One chooses by recent past performance, and the other by the recent average degree of parallelism, which is shown to be correlated to algorithmic superiority. Simulation results for the algorithms on production workloads are analyzed, and illustrate unique features of the chaotic temporal structure of parallel workloads. We provide best parameter configurations for each algorithm, which both achieve average improvements of 10% in performance and 35% in stability for the tested workloads.