An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their exe...

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Vydáno v:	IEEE transactions on parallel and distributed systems Ročník 20; číslo 8; s. 1158 - 1172
Hlavní autoři:	Vydyanathan, N., Krishnamoorthy, S., Sabin, G.M., Catalyurek, U.V., Kurc, T., Sadayappan, P., Saltz, J.H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2009 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithm design and analysis Algorithms Allocations Concurrent computing Critical path Data communication data-flow graphs Decisions Graphs Heuristic locality-conscious scheduling Microprocessors mixed parallelism Optimal scheduling Parallel processing Power system modeling Processor allocation Processor scheduling Runtime Scalability Schedules Scheduling Scheduling algorithm Studies Tasks Processor Allocation and Scheduling Mixed-parallelism Scheduling and task partitioning Load balancing and task assignment
ISSN:	1045-9219, 1558-2183
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Abstract	Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their execution. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism required to minimize the parallel completion time (makespan) of these applications. In other words, our algorithm determines the set of tasks that should be run concurrently and the number of processors to be allocated to each task. The processor allocation and scheduling decisions are made in an integrated manner and are based on several factors such as the structure of the task graph, the runtime estimates and scalability characteristics of the tasks, and the intertask data communication volumes. A locality-conscious scheduling strategy is used to improve intertask data reuse. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms. Our algorithm also produces schedules that have a lower makespan than pure task- and data-parallel schedules. For task graphs with known optimal schedules or lower bounds on the makespan, our algorithm generates schedules that are closer to the optima than other scheduling approaches.
AbstractList	Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their execution. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism required to minimize the parallel completion time (makespan) of these applications. In other words, our algorithm determines the set of tasks that should be run concurrently and the number of processors to be allocated to each task. The processor allocation and scheduling decisions are made in an integrated manner and are based on several factors such as the structure of the task graph, the runtime estimates and scalability characteristics of the tasks, and the intertask data communication volumes. A locality-conscious scheduling strategy is used to improve intertask data reuse. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms. Our algorithm also produces schedules that have a lower makespan than pure task- and data-parallel schedules. For task graphs with known optimal schedules or lower bounds on the makespan, our algorithm generates schedules that are closer to the optima than other scheduling approaches. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms.
Author	Saltz, J.H. Sadayappan, P. Kurc, T. Krishnamoorthy, S. Catalyurek, U.V. Sabin, G.M. Vydyanathan, N.
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SubjectTerms	Algorithm design and analysis Algorithms Allocations Concurrent computing Critical path Data communication data-flow graphs Decisions Graphs Heuristic locality-conscious scheduling Microprocessors mixed parallelism Optimal scheduling Parallel processing Power system modeling Processor allocation Processor scheduling Runtime Scalability Schedules Scheduling Scheduling algorithm Studies Tasks
Title	An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications
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