Scheduling Recurrent Precedence-Constrained Task Graphs on a Symmetric Shared-Memory Multiprocessor

  • UmaMaheswari C. Devi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5704)


We consider approaches that allow task migration for scheduling recurrent directed-acyclic-graph (DAG) tasks on symmetric, shared-memory multiprocessors (SMPs) in order to meet a given throughput requirement with fewer processors. Within the scheduling approach proposed, we present a heuristic based on grouping DAG subtasks for lowering the end-to-end latency and an algorithm for computing an upper bound on latency. Unlike prior work, the purpose of the grouping here is not to map the subtask groups to physical processors, but to generate aggregated entities, each of which can be treated as a single schedulable unit to lower latency. Evaluation using synthetic task sets shows that our approach can lower processor needs considerably while incurring only a modest increase in latency. In contrast to the work presented herein, most prior work on scheduling recurrent DAGs has been for distributed-memory multiprocessors, and has therefore mostly been concerned with statically mapping DAG subtasks to processors.


Task Graph Incoming Edge Schedule Approach Execution Cost Sporadic Task 
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  1. 1.
    Choudhary, A., Narahari, B., Nicol, D., Simha, R.: Optimal processor assignment for a class of pipelined computations. IEEE Transactions on Parallel and Distributed Systems 5(4), 439–445 (1994)CrossRefGoogle Scholar
  2. 2.
    Garey, M., Johnson, D.: Computers and Intractability: a Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)zbMATHGoogle Scholar
  3. 3.
    Guirado, F., Ripoll, A., Roig, C., Luque, E.: Optimizing latency under throughput requirements for streaming applications on cluster execution. In: Proceedings of the IEEE International Conference on Cluster Computing, September 2005, pp. 1–10 (2005)Google Scholar
  4. 4.
    Hary, S., Ozguner, F.: Precedence-constrained task allocation onto point-to-point networks for pipelined execution. IEEE Transactions on Parallel and Distributed Systems 10(8), 838–851 (1999)CrossRefGoogle Scholar
  5. 5.
    Hoang, P., Rabaey, J.: Scheduling dsp programs onto multiprocessors for maximum throughput. IEEE Transactions on Signal Processing 41(6), 2225–2235 (1993)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kazempour, V., Fedorova, A., Alagheband, P.: Performance implications of cache affinity on multicore processors. In: Proceedings of the 14th International Conference on Parallel Computing, August 2008, pp. 151–162 (2008)Google Scholar
  7. 7.
    Kwok, Y.K., Ahmad, I.: Static scheduling algorithms for allocating derected task graphs to multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999)CrossRefGoogle Scholar
  8. 8.
    Srinivasan, A., Anderson, J.: Optimal rate-based scheduling on multiprocessors. In: Proceedings of the 34th ACM Symposium on Theory of Computing, May 2002, pp. 189–198 (2002)Google Scholar
  9. 9.
    Srinivasan, A., Anderson, J.: Fair scheduling of dynamic task systems on multiprocessors. Journal of Systems and Software 77(1), 67–80 (2005)CrossRefGoogle Scholar
  10. 10.
    Subhlok, J., Vondran, G.: Optimal mapping of sequences of data parallel tasks. In: Proceedings of the 5th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, July 1995, pp. 134–143 (1995)Google Scholar
  11. 11.
    Subhlok, J., Vondran, G.: Optimal latency-throughput tradeoffs for data parallel machines. In: Proceedings of the 8th ACM Symposium on Parallel Algorithms and Architectures, June 1996, pp. 62–71 (1996)Google Scholar
  12. 12.
    Yang, M.-T., Kasturi, R., Sivasubramaniam, A.: A pipeline-based approach for scheduling video processing algorithms on NOW. IEEE Transactions on Parallel and Distributed Systems 14(2), 119–130 (2003)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • UmaMaheswari C. Devi
    • 1
  1. 1.IBM India Research LaboratoryBangaloreIndia

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