Fine-grain non-strict data structures such as I-structures provide high level abstraction to easily write programs with potentially high parallelism due to the eager evaluation of non-strict functions and non-strict structured-data. Non-strict data structures require frequent dynamic scheduling at a fine-grain level, which offsets the gain of latency hiding and asynchronous accesses to structured-data using non-strict data structures. These cause heavy overhead on commodity machines. In order to solve these problems for fine-grain non-strict structured-data, we employ a method to analyze dependencies between the structured-data and to schedule their producers and consumers. The performance evaluation results indicate that the scheduling technique is effective to improve the performance of fine-grain non-strict programs on commodity machines.
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