Distributed scheduling for many-cores using cooperative game theory

ABSTRACT

Many-cores are envisaged to include hundreds of processing cores etched on to a single die and will execute tens of multi-threaded tasks in parallel to exploit their massive parallel processing potential. A task can be sped up by assigning it to more than one core. Moreover, processing requirements of tasks are in a constant state of flux and some of the cores assigned to a task entering a low processing requirement phase can be transferred to a task entering high requirement phase, maximizing overall performance of the system.

This scheduling problem of partial core reallocations can be solved optimally in polynomial time using a dynamic programming based scheduler. Dynamic programming is an inherently centralized algorithm that uses only one of the available cores for scheduling-related computations and hence is not scalable. In this work, we introduce a distributed scheduler that disburses all scheduling-related computations throughout the many-core allowing it to scale up. We prove that our proposed scheduler is optimal and hence converges to the same solution as the centralized optimal scheduler. Our simulations show that the proposed distributed scheduler can result in 1000x reduction in per-core processing overhead in comparison to the centralized scheduler and hence is more suited for scheduling on many-cores.

References

1. I. Ahmad, S. Ranka, and S. U. Khan. Using Game Theory for Scheduling Tasks on Multi-core Processors for Simultaneous Optimization of Performance and Energy. In International Symposium on Parallel and Distributed Processing (IPDPS), 2008.
2. J. Augustine, N. Chen, E. Elkind, A. Fanelli, N. Gravin, and D. Shiryaev. Dynamics of Profit-Sharing Games. Internet Mathematics, 2013.
3. C. Bienia, S. Kumar, J. P. Singh, and K. Li. The PARSEC Benchmark Suite: Characterization and Architectural Implications. In International Conference on Parallel Architectures and Compilation Techniques (PACT), 2008.
4. N. Binkert et al. The gem5 Simulator. In SIGARCH Computer Architecture News, 2011.
5. K. Burns and B. Hasselblatt. The Sharkovsky Theorem: A Natural Direct Proof. Mathematical Monthly, 2011.
6. T. Ebi, M. Faruque, and J. Henkel. TAPE: Thermal-Aware Agent-Based Power Economy Multi/Many-core Architectures. In International Conference on Computer-Aided Design (ICCAD), 2009.
7. D. P. Gulati, C. Kim, S. Sethumadhavan, S. W. Keckler, and D. Burger. Multitasking Workload Scheduling on Flexible-core Chip Multiprocessors. In International Conference on Parallel Architectures and Compilation Techniques (PACT), 2008.
8. J. Henkel, A. Herkersdorf, L. Bauer, T. Wild, M. Hübner, R. K. Pujari, A. Grudnitsky, J. Heisswolf, A. Zaib, B. Vogel, V. Lari, and S. Kobbe. Invasive Manycore Architectures. In Asia and South Pacific Design Automation Conference (ASP-DAC), 2012.
9. J. L. Henning. SPEC CPU2006 Benchmark Descriptions. Computer Architecture News, 2006.
10. C. Kim, S. Sethumadhavan, M. S. Govindan, N. Ranganathan, D. Gulati, D. Burger, and S. W. Keckler. Composable Lightweight Processors. In International Symposium on Microarchitecture (MICRO), 2007.
11. K. M. Lepak, H. W. Cain, and M. H. Lipasti. Redeeming IPC as a Performance Metric for Multithreaded Programs. In International Conference on Parallel Architectures and Compilation Techniques (PACT), 2003.
12. V. Pallipadi and A. Starikovskiy. The Ondemand Governor. In The Linux Symposium, 2006.
13. A. Pathania, V. Venkataramani, M. Shafique, T. Mitra, and J. Henkel. Distributed Fair Scheduling for Many-Cores. In Design, Automation and Test in Europe (DATE), 2016.
14. M. Pricopi and T. Mitra. Bahurupi: A Polymorphic Heterogeneous Multi-core Architecture. Transactions on Architecture and Code Optimization (TACO), 2012.
15. M. Pricopi and T. Mitra. Task Scheduling on Adaptive Multi-Core. Transactions on Computers (TC), 2013.
16. A. Stivala, P. J. Stuckey, M. G. de la Banda, M. Hermenegildo, and A. Wirth. Lock-Free Parallel Dynamic Programming. Parallel and Distributed Computing, 2010.
17. V. Vanchinathan. Performance Modeling of Adaptive Multi-core Architecture. Master's thesis, National University of Singapore, 2015.
18. S. K. Venkata, I. Ahn, D. Jeon, A. Gupta, C. Louie, S. Garcia, S. Belongie, and M. B. Taylor. SD-VBS: The San Diego Vision Benchmark Suite. In International Symposium on Workload Characterization (IISWC), 2009.
19. S. Wildermann, T. Ziermann, and J. Teich. Game-theoretic Analysis of Decentralized Core Allocation Schemes on Many-Core Systems. In Design, Automation and Test in Europe (DATE), 2013.
20. S. C. Woo, M. Ohara, E. Torrie, J. P. Singh, and A. Gupta. The SPLASH-2 Programs: Characterization and Methodological Considerations. In Computer Architecture News, 1995.

Index Terms

• Computer systems organization

  • Architectures

    • Other architectures

      • Self-organizing autonomic computing