Abstract
In this chapter we address the problem of allocating parallel tasks on a distributed memory machine for coarse-grain applications represented by parameterized task graphs (PTG). A PTG is a new computation model for representing directed acyclic task graphs (DAG) symbolically. The size of a PTG is independent of the problem size and its parameters can be instantiated at run time. Parameterindependent optimization is important for exploiting non-static parallelism in scientific computing programs with varying problem sizes and the previous DAG scheduling algorithms are not able to handle such cases. We present and study a symbolic scheduling algorithm called SLC (Symbolic Linear Clustering) which derives task clusters from a PTG using affine piecewise mapping functions and then evenly assigns clusters to processors. Our experimental results show that the proposed method is effective for a number of compute-intensive problems in scientific applications.
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Cosnard, M., Jeannot, E., Yang, T. (2000). Symbolic Scheduling of Parameterized Task Graphs on Parallel Machines. In: Pardalos, P.M., Pitsoulis, L.S. (eds) Nonlinear Assignment Problems. Combinatorial Optimization, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3155-2_9
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DOI: https://doi.org/10.1007/978-1-4757-3155-2_9
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