Naila Farooqui
ABSTRACT
Recent integrated CPU-GPU processors like Intel's Broadwell and AMD's Kaveri support hardware CPU-GPU shared virtual memory, atomic operations, and memory coherency. This enables fine-grained CPU-GPU work-stealing, but architectural differences between the CPU and GPU hurt the performance of traditionally-implemented work-stealing on such processors. These architectural differences include different clock frequencies, atomic operation costs, and cache and shared memory latencies. This paper describes a preliminary implementation of our work-stealing scheduler, Libra, which includes techniques to deal with these architectural differences in integrated CPU-GPU processors. Libra's affinity-aware techniques achieve significant performance gains over classically-implemented work-stealing. We show preliminary results using a diverse set of nine regular and irregular workloads running on an Intel Broadwell Core-M processor. Libra currently achieves up to a 2× performance improvement over classical work-stealing, with a 20% average improvement.
- Intel thread building blocks. URL www.threadbuildingblocks.org.Google Scholar
- C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier. Starpu: a unified platform for task scheduling on heterogeneous multicore architectures. Concurrency and Computation: Practice and Experience, 23 (2):187--198, 2011. Google ScholarDigital Library
- R. D. Blumofe and C. E. Leiserson. Scheduling multithreaded computations by work stealing. J. ACM, 46(5):720--748, Sept. 1999. ISSN 0004-5411. doi: 10.1145/324133.324234. Google ScholarDigital Library
- Y. Guo, J. Zhao, V. Cave, and V. Sarkar. Slaw: A scalable locality-aware adaptive work-stealing scheduler for multi-core systems. In Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP '10, pages 341--342, New York, NY, USA, 2010. ACM. ISBN 978-1-60558-877-3. doi: 10.1145/1693453.1693504. URL http://doi.acm.org/10.1145/1693453.1693504. Google ScholarDigital Library
- S. jai Min, C. Iancu, and K. Yelick. Hierarchical work stealing on manycore clusters. In In Fifth Conference on Partitioned Global Address Space Programming Models, 2011.Google Scholar
- R. Kaleem, R. Barik, T. Shpeisman, B. T. Lewis, C. Hu, and K. Pingali. Adaptive heterogeneous scheduling for integrated gpus. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation, PACT '14, pages 151--162, New York, NY, USA, 2014. ACM. ISBN 978-1-4503-2809-8. doi: 10.1145/2628071.2628088. URL http://doi.acm.org/10.1145/2628071.2628088. Google ScholarDigital Library
- J. Lee, M. Samadi, Y. Park, and S. Mahlke. Transparent CPU-GPU collaboration for data-parallel kernels on heterogeneous systems. In Proceedings of the 22nd international conference on Parallel architectures and compilation techniques, PACT, 2013. Google ScholarDigital Library
- C.-K. Luk, S. Hong, and H. Kim. Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 42, pages 45--55, NY, USA, 2009. ACM. ISBN 978-1-60558-798-1. doi: 10.1145/1669112.1669121. URL http://doi.acm.org/10.1145/1669112.1669121. Google ScholarDigital Library
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