Capsules: expressing composable computations in a parallel programming model

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Mandviwala, Hasnain A.
Ramachandran, Umakishore
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A well-known problem in designing high-level parallel programming models and languages is the "granularity problem", where the execution of parallel tasks that are too fine grain incur large overheads in the parallel runtime and adversely affect the speed-up that can be achieved by parallel execution. On the other hand, tasks that are too coarse-grain create load imbalance and do not adequately utilize the parallel machine. In this work we attempt to address the issue of granularity with a concept of expressing "composable computations" within a parallel programming model called "Capsules". In Capsules, we provide a unifying framework that allows composition and adjustment of granularity for both data and computation over iteration space and computation space. The Capsules model not only allows the user to express the decision on granularity of execution, but also the decision on the granularity of garbage collection (and therefore, the aggressiveness of the GC optimization), and other features that may be supported by the programming model. We argue that this adaptability of execution granularity leads to efficient parallel execution by matching the available application concurrency to the available hardware concurrency, thereby reducing parallelization overhead. By matching, we refer to creating coarsegrain Computation Capsules that encompass multiple instances of fine-grain computation instances. In effect, creating coarse-grain computations reduces overhead by simply reducing the number of parallel computations. Reducing parallel computation instances in turn leads to: (1) Reduced synchronization cost such as that required to access and search in shared data-structures; (2) Reduced distribution and scheduling cost for parallel computation instances; and (3) Reduced book-keeping costs consisting of maintain data-structures such as blocked lists for unfulfilled data requests. Capsules builds on our prior work, TStreams, a data-flow oriented parallel programming framework. Our results on an CMP/SMP machine using real vision applications such as the Cascade Face Detector, and the Stereo Vision Depth applications, and other synthetic applications show benefits in application performance. We use profiling to help determine optimal coarse-grain serial execution granularity, and provide empirical proof that adjusting execution granularity reduces parallelization overhead to yield maximum application performance.
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