Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Chris Gregg, Michael Boyer, Kim Hazelwood, Kevin Skadron
Computer Engineering Lab, University of Virginia
2nd Workshop on Applications for Multi and Many Core Processors: Analysis, Implementation, and Performance (A4MMC), in conjunction with ISCA, 2011


   title={Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data},

   author={Gregg, C. and Boyer, M. and Hazelwood, K. and Skadron, K.},



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Heterogeneous systems often employ processing units with a wide spectrum of performance capabilities. Allowing individual applications to make greedy local scheduling decisions leads to imbalance, with underutilization of some devices and excessive contention for others. If we instead allow the system to make global scheduling decisions and assign some applications to a slower device, we can both increase overall system throughput and decrease individual application runtimes. We present a method for dynamically scheduling applications running on heterogeneous platforms in order to maximize overall throughput. The key to our approach is accurately estimating when an application would finish execution on a given device based on historical runtime information, allowing us to make scheduling decisions that are both globally and locally efficient. We evaluate our approach with a set of OpenCL applications running on a system with a multicore CPU and a discrete GPU. We show that scheduling decisions based on historical data can decrease the total runtime by 39% over GPU-only scheduling and 29% over scheduling that places each application on its preferred device.
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