Quan Chen
Haibing Guan
ABSTRACT
Modern mainstream powerful computers adopt Multi-Socket Multi-Core (MSMC) CPU architecture and NUMA-based memory architecture. While traditional work-stealing schedulers are designed for single-socket architectures, they incur severe shared cache misses and remote memory accesses in these computers, which can degrade the performance of memory-bound applications seriously. To solve the problem, we propose a Locality-Aware Work-Stealing (LAWS) scheduler, which better utilizes both the shared cache and the NUMA memory system. In LAWS, a load-balanced task allocator is used to evenly split and store the data set of a program to all the memory nodes and allocate a task to the socket where the local memory node stores its data. Then, an adaptive DAG packer adopts an auto-tuning approach to optimally pack an execution DAG into many cache-friendly subtrees. Meanwhile, a triple-level work-stealing scheduler is applied to schedule the subtrees and the tasks in each subtree. Experimental results show that LAWS can improve the performance of memory-bound programs up to 54.2% compared with traditional work-stealing schedulers.
- U. Acar, G. Blelloch, and R. Blumofe. The data locality of work stealing. Theory of Computing Systems, 35(3):321--347, 2002.Google Scholar
Cross Ref
- AMD. BIOS and Kernel Developer Guide (BKDG) For AMD Family 10h Processors. AMD, 2010.Google Scholar
- E. Ayguadé, N. Copty, A. Duran, J. Hoeflinger, Y. Lin, F. Massaioli, X. Teruel, P. Unnikrishnan, and G. Zhang. The design of OpenMP tasks. IEEE TPDS, 20(3):404--418, 2009. Google ScholarDigital Library
- R. D. Blumofe. Executing Multithreaded Programs Efficiently. PhD thesis, MIT, Sept. 1995. Google ScholarDigital Library
- R. D. Blumofe, C. F. Joerg, B. C. Kuszmaul, C. E. Leiserson, K. H. Randall, and Y. Zhou. Cilk: An efficient multithreaded runtime system. Journal of Parallel and Distributed Computing, 37(1):55--69, 1996. Google ScholarDigital Library
- M. Castro, L. G. Fernandes, C. Pousa, J.-F. Méhaut, and M. S. de Aguiar. NUMA-ICTM: A parallel version of ICTM exploiting memory placement strategies for NUMA machines. In IPDPS, pages 1--8, 2009. Google ScholarDigital Library
- Q. Chen, Y. Chen, Z. Huang, and M. Guo. WATS: Workload-aware task scheduling in asymmetric multi-core architectures. In IPDPS, pages 249--260, 2012. Google ScholarDigital Library
- Q. Chen and M. Guo. Adaptive workload aware task scheduling for single-ISA multi-core architectures. ACM Transactions on Architecture and Code Optimization, 11(1), 2014. Google ScholarDigital Library
- Q. Chen, M. Guo, and Z. Huang. CATS: Cache aware task-stealing based on online profiling in multi-socket multi-core architectures. In ICS, pages 163--172, 2012. Google ScholarDigital Library
- Q. Chen, M. Guo, and Z. Huang. Adaptive cache aware bi-tier work-stealing in multi-socket multi-core architectures. IEEE Transactions on Parallel and Distributed Systems, 24(12):2334--2343, 2013. Google ScholarDigital Library
- Q. Chen, Z. Huang, M. Guo, and J. Zhou. CAB: Cache-aware bi-tier task-stealing in multi-socket multi-core architecture. In ICPP, pages 722--732, 2011. Google ScholarDigital Library
- R. Cole and V. Ramachandran. Analysis of randomized work stealing with false sharing. In IPDPS, pages 985--989, 2013. Google ScholarDigital Library
- M. Frigo, C. E. Leiserson, and K. H. Randall. The implementation of the Cilk-5 multithreaded language. In PLDI, pages 212--223, 1998. Google ScholarDigital Library
- T. Gautier, J. V. Lima, N. Maillard, and B. Raffin. XKaapi: A runtime system for data-flow task programming on heterogeneous architectures. In IPDPS, pages 1299--1308, 2013. Google ScholarDigital Library
- T. Gautier, J. V. F. Lima, N. Maillard, B. Raffin, et al. Locality-aware work stealing on Multi-CPU and Multi-GPU architectures. In MULTIPROG, 2013.Google Scholar
- A. Gerasoulis and T. Yang. A comparison of clustering heuristics for scheduling directed acyclic graphs on multiprocessors. Journal of Parallel and Distributed Computing, 16(4):276--291, 1992.Google ScholarCross Ref
- Y. Guo, R. Barik, R. Raman, and V. Sarkar. Work- first and help-first scheduling policies for async-finish task parallelism. In IPDPS, pages 1--12, 2009. Google ScholarDigital Library
- Y. Guo, J. Zhao, V. Cave, and V. Sarkar. SLAW: a scalable locality-aware adaptive work--stealing scheduler. In IPDPS, pages 1--12, 2010.Google ScholarCross Ref
- L. V. Kale and S. Krishnan. CHARMGoogle Scholar
- : a portable concurrent object oriented system based on CGoogle Scholar
- . ACM, 1993.Google Scholar
- G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20(1):359--392, 1998. Google ScholarDigital Library
- J. Lee and J. Palsberg. Featherweight X10: a core calculus for async-finish parallelism. In PPoPP, pages 25--36, 2010. Google ScholarDigital Library
- C. Leiserson. The CilkGoogle Scholar
- concurrency platform. In DAC, pages 522--527, 2009. Google ScholarDigital Library
- L. L. Pilla, C. P. Ribeiro, D. Cordeiro, A. Bhatele, P. O. Navaux, J.-F. Méhaut, L. V. Kalé, et al. Improving parallel system performance with a NUMA-aware load balancer. TR-JLPC-11-02, 2011.Google Scholar
- J.-N. Quintin and F. Wagner. Hierarchical work-stealing. In EuroPar, pages 217--229, 2010. Google ScholarDigital Library
- J. Reinders. Intel threading building blocks. Intel, 2007. Google ScholarDigital Library
- M. Shaheen and R. Strzodka. NUMA aware iterative stencil computations on many-core systems. In IPDPS, pages 461--473, 2012. Google ScholarDigital Library
- S. Sridharan, G. Gupta, and G. S. Sohi. Holistic run-time parallelism management for time and energy efficiency. In ICS, pages 337--348, 2013. Google ScholarDigital Library
- B. Vikranth, R. Wankar, and C. R. Rao. Topology aware task stealing for on-chip NUMA multi-core processors. Procedia Computer Science, 18:379--388, 2013.Google ScholarCross Ref
- R. M. Yoo, C. J. Hughes, C. Kim, Y.-K. Chen, and C. Kozyrakis. Locality-aware task management for unstructured parallelism: a quantitative limit study. In SPAA, pages 315--325, 2013. Google ScholarDigital Library
Index Terms
- LAWS: locality-aware work-stealing for multi-socket multi-core architectures
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