Space efficient execution of deterministic parallel programs
We model a deterministic parallel program by a directed acyclic graph of tasks, where a task can execute as soon as all tasks preceding it have been executed. Each task can allocate or release an arbitrary amount of memory (i.e., heap memory allocation can be modeled). We call a parallel schedule "space efficient" if the amount of memory required is at mast equal to the number of processors times the amount of memory required for some depth-first execution of the program by a single processor. We will describe a simple, locally depth-first, scheduling algorithm and shaw that it is always space efficient. Since the scheduling algorithm is greedy, it will be within a factor of two of being optimal with respect to time. For the special case of a program having a series-parallel structure, we show how to efficiently compute the worst case memory requirements over all possible depth-first executions of a program. Finally, we show how scheduling can be decentralized, making the approach scalable to a large number of processors when there is sufficient parallelism.
parallel programming scheduling memory management worst case performance memory bounds shared memory