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Fine-grain non-strict data structures such as I-structure provide high level abstraction to easily write programs with potentially high parallelism due to the eager evaluation (lenient evaluation) of ...non-strict functions and non-strict structured-data such as arrays. Non-strict data structures require frequent dynamic scheduling at fine-grain level, which offsets the gain of latency hiding. Not only the dynamic scheduling but also asynchronous accesses to structured-data using non-strict data structures cause heavy overhead on stock machines. Mutual exclusion of structured-data on shared memory systems also causes overhead. In order to reduce overhead of fine-grain non-strict structured-data, we propose a compilation technique to analyze dependencies between the structured-data and to schedule producers and consumers of the structured-data. The performance evaluation results indicate that the technique is effective to improve the performance of fine-grain non-strict programs with structured-data on shared memory systems.続きを見る
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