SLAW: A scalable locality-aware adaptive work-stealing scheduler for multi-core systems

Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Volumn , Issue , 2010, Pages 341-342

  Guo, Yi ^a   Zhao, Jisheng ^a   Cave, Vincent ^a   Sarkar, Vivek ^a  

^a RICE UNIVERSITY   (United States)

Author keywords

Help first; Work first; Work stealing

Indexed keywords

ADAPTIVE APPROACH; ADAPTIVE TASK SCHEDULING; DATA SIZE; DYNAMIC TASKS; LOCALITY AWARENESS; MULTI-CORE SYSTEMS; PARALLEL COMPUTATION; PARALLEL PROGRAM; PROGRAMMING MODELS; RUNTIMES; SCHEDULING FRAMEWORKS; SCHEDULING POLICIES; TEMPORAL DATA; WORST CASE;

ADAPTIVE ALGORITHMS; DISTRIBUTED COMPUTER SYSTEMS; MICROPROCESSOR CHIPS; SCHEDULING ALGORITHMS;

PARALLEL PROGRAMMING;

EID: 77749243481     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1693453.1693504     Document Type: Conference Paper

References (6)

1. P. Charles et al. X10: An object-oriented approach to non-uniform cluster computing. OOPSLA 2005 Onward! Track.
2. G. Cong et al. Solving Large, Irregular Graph Problems Using Adaptive Work-Stealing. ICPP 2008.
3. M. Frigo, C. E. Leiserson, and K. H. Randall. The implementation of the Cilk-5 multithreaded language. PLDI 1998.
4. Y. Guo, R. Barik, R. Raman, and V. Sarkar. Work-first and help-first scheduling policies for async-finish task parallelism. IPDPS 2009.
5. Habanero Java (HJ) Project. http://habanero.rice.edu/hj.
6. Y. Yan, J. Zhao, Y. Guo, and V. Sarkar. Hierarchical Place Trees: A Portable Abstraction for Task Parallelism and Data Movement. LCPC 2009.