VisSched: An Auction-Based Scheduler for Vision Workloads on Heterogeneous Processors.
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| Title: | VisSched: An Auction-Based Scheduler for Vision Workloads on Heterogeneous Processors. |
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| Authors: | Moolchandani, Diksha, Kumar, Anshul, Martinez, Jose F., Sarangi, Smruti R. |
| Source: | IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems; Nov2020, Vol. 39 Issue 11, p4252-4265, 14p |
| Subject Terms: | MONTE Carlo method, EDGE computing, VISION, NEURAL codes, COMPUTER vision, LINEAR codes, LINEAR algebra, ALGORITHMS |
| Abstract: | With the growth of edge computing, application-specific workloads based on computer vision are steadily migrating to edge cloudlets. Scheduling has been identified to be a major problem in these cloudlets. In this article, we propose a generic architectural solution, VisSched, that leverages the fact that most vision workloads share similar code kernels (such as library code for linear algebra), and as a result, they tend to exhibit similar phase behavior. This allows us to create an auction theory-based scheduling mechanism, where we give each thread a replenishable virtual wallet, and threads are scheduled based on the amounts that they bid for executing on a free core. We show that in 20%–40% of the cases, our scheduling algorithm is theoretically optimal, and in the remaining cases, it reaches a global optimum obtained using Monte Carlo simulations 90%–95% of the time. Our results for the MEVBench vision workloads show a 17% higher performance and a 14% lower $ED^{2}$ as compared to the nearest competing algorithm in the literature. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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