Scheduling across Multiple Applications using Task-Based Programming Models

Task-based programming models have shown their potential for efficiency and scalability in parallel and distributed systems. With such a model, a parallel application is broken down into a graph of tasks, which are subsequently scheduled for execution. Recently, implementations of task-based models...

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Veröffentlicht in:2020 IEEE/ACM Fourth Annual Workshop on Emerging Parallel and Distributed Runtime Systems and Middleware (IPDRM) S. 1 - 8
Hauptverfasser: Chung, Minh Thanh, Weidendorfer, Josef, Samfass, Philipp, Fuerlinger, Karl, Kranzlmuller, Dieter
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2020
Schlagworte:
cooperative task scheduling
coordinated scheduling
distributed memory systems
distributed system
Libraries
parallel application
parallel distributed systems
parallel programming
Programming
reactive tasking
resource allocation
resource management
Runtime
scheduling
scheduling tasks
Synchronization
task analysis
task migration
task-based framework
task-based models
task-based programming model
task-based programming models
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Zusammenfassung:Task-based programming models have shown their potential for efficiency and scalability in parallel and distributed systems. With such a model, a parallel application is broken down into a graph of tasks, which are subsequently scheduled for execution. Recently, implementations of task-based models have addressed distributed memory and heterogeneous systems with accelerators. However, the problem of scheduling tasks as well as allocating resources at runtime is still a challenge. In this paper, we propose coordinated and cooperative task scheduling across multiple applications. The main idea is to exploit the application's idle time e.g. from imbalance to serve tasks from another application. The experiments use Chameleon, a task-based framework for reactive tasking in distributed memory systems. In various example scenarios, we show improvements in CPU utilization of 5% - 15% by coordinated scheduling.
DOI:10.1109/IPDRM51949.2020.00005