While High Performance Computing systems are increasingly
based on heterogeneous cores, their e ectiveness depends on how
well the scheduler can allocate workloads onto appropriate computing devices
and how communication and computation can be overlapped. With
di erent types of resources integrated into one system, the complexity of
the scheduler correspondingly increases. Moreover, for applications with
varying problem sizes on di erent heterogeneous resources, the optimal
scheduling approach may vary accordingly. We thus present PDAWL, an
event-driven pro le-based Iterative Dynamic Adaptive Work-Load balance
scheduling approach to dynamically and adaptively adjust workload
to eciently utilize heterogeneous resources. It combines online scheduling
(DAWL), which can adaptively adjust workload based on available
real time heterogeneous resources, with an oine machine learning (pro lebased
estimation model) which can build a device-speci c communication
computation estimation model. Our scheduling approach is tested on
control-regular applications, Stencil kernel (based on a Jacobi Algorithm)
and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven runtime
system. Experimental results show that PDAWL is either on-par or
far outperforms whichever yields the best results (CPU or GPU).
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PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures
While High Performance Computing systems are increas-ingly based on heterogeneous cores, their effectiveness depends on howwell the scheduler can allocate workloads onto appropriate computing de-vices and how communication and computation can be overlapped. Withdifferent types of resources integrated into one system, the complexity ofthe scheduler correspondingly increases. Moreover, for applications withvarying problem sizes on different heterogeneous resources, the optimalscheduling approach may vary accordingly. We thus present PDAWL, anevent-driven profile-based Iterative Dynamic Adaptive Work-Load bal-ance scheduling approach to dynamically and adaptively adjust workloadto efficiently utilize heterogeneous resources. It combines online schedul-ing (DAWL), which can adaptively adjust workload based on availablereal time heterogeneous resources, with an offline machine learning (profile-based estimation model) which can build a device-specific communica-tion computation estimation model. Our scheduling approach is tested oncontrol-regular applications, Stencil kernel (based on a Jacobi Algorithm)and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven run-time system. Experimental results show that PDAWL is either on-par orfar outperforms whichever yields the best results (CPU or GPU).
more »
« less
- Award ID(s):
- 1763793
- NSF-PAR ID:
- 10154660
- Date Published:
- Journal Name:
- 23rd Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP 2020)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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