Abstract
Traditional data-oriented programming languages such as dataflow languages and stream languages provide a natural abstraction for parallel programming. In these languages, a developer focuses on the flow of data through the computation and these systems free the developer from the complexities of low-level, thread-oriented concurrency primitives. This simplification comes at a cost --- traditional data-oriented approaches restrict the mutation of state and, in practice, the types of data structures a program can effectively use. Bamboo borrows from work in typestate and software transactions to relax the traditional restrictions of data-oriented programming models to support mutation of arbitrary data structures. We have implemented a compiler for Bamboo which generates code for the TILEPro64 many-core processor. We have evaluated this implementation on six benchmarks: Tracking, a feature tracking algorithm from computer vision; KMeans, a K-means clustering algorithm; MonteCarlo, a Monte Carlo simulation; FilterBank, a multi-channel filter bank; Fractal, a Mandelbrot set computation; and Series, a Fourier series computation. We found that our compiler generated implementations that obtained speedups ranging from 26.2x to 61.6x when executed on 62 cores.
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