- 1.BOKHARI, S. Assignment Problems zn Parallel and Distributed Computing. Kluwer Academic Publishers, 1987.]] Google ScholarDigital Library
- 2.CHAKRABARTI, S., DEMMEL, J., AND YELICK, K. Modeling the benefits of mixed data and task parallelism. In Seventh Annual A CM Symposium on Parallel Algomthms and Architectures (Santa Barbara, CA, July 1995).]] Google ScholarDigital Library
- 3.CHANDY, M., FOSTER, I., KENNEDY, #., KOELBEL, C., AND TSENG, C. Integrated support for task and data parallelism. International dournal of Supercomputer Applicat,ons 8, 2 (1994), 80-98.]]Google Scholar
- 4.CHAPMAN, B., MEHROTRA, P., VAN ROSENDALE, J., AND ZIMA, H. A software architecture for multidisciplinary applications: Integrating task and data parallelism. Tech. Rep. 94-18, ICASE, NASA Langley Research Center, Hampton, VA, Mar. 1994.]] Google ScholarDigital Library
- 5.#HOUDHARY, A., NARAHARI, B., NICOL, D., AND SIMttA, R. Optimal processor assignment for a class of pipelined computations. IEEE Transactions on Parallel and Distributed Systems 5, 4 (April 94), 439-445.]] Google ScholarDigital Library
- 6.CROWL, L., CROVELLA, M., LEBLANC, T., AND SCOTT, M. The advantages of multiple parallelizations in combinatorial search. Journal of Parallel and Dzstmbuted Computing 21 (1994), 110-123.]] Google ScholarDigital Library
- 7.DINDA, P., GROSS, T., O'HALLARON, D., SEGALL, E., STICHNOTH, J., SUBHLOK, J., WEBB, J., AND YANG, B. The CMU task parallel program suite. Tech. Rep. CMU-CS-94-131, School of Computer Science, Carnegie Mellon University, Mar. 1994.]]Google Scholar
- 8.FOSTER, I., AVALANI, B., CHOUDHARY, A., AND XU, M. A compilation system that integrates High Performance Fortran and Fortran M. In Proceeding of 1994 Scalable High Performance Comput,ng Conference (Knoxville, TN, October 1994), pp. 293-300.]]Google Scholar
- 9.Gaoss, T., O'HALLARON, D., AND SUBHLOK, J. Task parallelism in a High Performance Fortran framework. IEEE Parallel # Distributed Technology, 3 (1994), 16- 26.]] Google ScholarDigital Library
- 10.HICH PERFORMANCE FORTRAN FORUM. High Performance Fortran Language Specification, Version 1.0, May 1993.]]Google Scholar
- 11.RAMASWAMY, S., SAPATNEKAR, S., AND BANERJEE, P. A convex programming approach for exploiting data and functional parallelism. In Proceedings of the 1994 International Conference on Parallel Processing (St Charles, IL, August 1994), vol. 2, pp. 116-125.]] Google ScholarDigital Library
- 12.SARKAR, V. Partitioning and Scheduling Parallel Programs for Multiprocessors. The MIT Press, Cambridge, MA, 1989.]] Google ScholarDigital Library
- 13.SUBHLOK, J., O'HALLARON, D., GROSS, T., DINDA, P., AND WEBB, J. Communication and memory requirements as the basis for mapping task and data parallel programs. In Supcrcomputing '94 (Washington, DC, November 1994), pp. 330-339.]]Google ScholarCross Ref
- 14.SUBHLOK, J., STICHNOTH, J., O'HALLARON, D., AND GRoss, T. Exploiting task and data parallelism on multicomputer. In ACM SIGPLAN Sympos,um on Pmnciples and Practice of Parallel Programming (San Diego, CA, May 1993), pp. 13-22.]] Google ScholarDigital Library
- 15.SUBHLOK, J., AND VONDRAN, G. Optimal mapping of sequences of data parallel tasks. In Proceedzngs of the Fifth A CM SIGPLAN Symposium on Principles and Practice of Parallel Programming (Santa Barbara, CA, July 1995), pp. 134-143.]] Google ScholarDigital Library
- 16.VONDRAN, G. Optimization of latency, throughput and processors for pipelines of data parallel tasks. Master's thesis, Dept. of Electrical and Computer Engineering, Carnegie Mellon University, May 1995.]]Google Scholar
- 17.WEB#, J. Latency and bandwidth consideration in parallel robotics image processing. In Supercomputzng '93 (Port}#nd, OR, Nov. 1993), pp. 230-239.]] Google ScholarDigital Library
- 18.YANG, B., WEBB, J., STICHNOTH, J., O'HALLARON, D., AND GROSS, T. Do&merge: Integrating parallel loops and reductions. In Sixth Annual Workshop on Languages and Compilers for Parallel Computing (Portland, Oregon, Aug 1993).]] Google ScholarDigital Library
- 19.YANG, T. Scheduling and Code Generat,on }or Parallel Architectures. PhD thesis, Rutgers University, May 1993.]] Google ScholarDigital Library
Index Terms
- Optimal latency-throughput tradeoffs for data parallel pipelines
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