Space/Time-Efficient Scheduling and Execution of Parallel Irregular Computations

this article we investigate the trade-off between time and space efficiency in scheduling and executing parallel irregular computations on distributed-memory machines. We employ acyclic task dependence graphs to model irregular parallelism with mixed granularity, and we use direct remote memory access to support fast communication. We propose new scheduling techniques and a run-time active memory management scheme to improve memory utilization while retaining good time efficiency, and we provide a theoretical analysis on correctness and performance. This work is implemented in the context of the RAPID system which uses an inspector/executor approach to parallelize irregular computations at run-time. We demonstrate the effectiveness of the proposed techniques on several irregular applications such as sparse matrix code and the fast multipole method for particle simulation. Our experimental results on Cray-T3E show that problems of large sizes can be solved under limited space capacity, and that the loss of execution efficiency caused by the extra memory management overhead is reasonable. Categories and Subject Descriptors: C.1.2 [Processor Architectures]: Multiple Data Stream Architectures (Multiprocessors); D.1.3 [Programming Techniques]: Parallel Programming; D.3.4 [Programming Languages]: Run-Time Environments; G.2.2 [Discrete Mathematics]