Abstract
Models based on task graphs that operate on single-assignment data are attractive in several ways, but also require nuanced algorithms for scheduling and memory management for efficient execution. In this paper, we consider memory-efficient dynamic scheduling of task graphs, and present a novel approach for dynamically recycling the memory locations assigned to data items as they are produced by tasks.
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