Tolerant value speculation in coarse-grain streaming computations

Streaming applications are the subject of growing interest, as the need for fast access to data continues to grow. In this work, we present the design requirements and implementation of coarse-grain value speculation in streaming applications. We explain how this technique can be useful in cases where serial parts of applications constitute bottlenecks, and when slower I/O favors using available prefixes of the data. Contrary to previous work, we show how allowing some tolerance can justify early predictions on a scale of a large window of values. We suggest a methodology for runtime support of speculation, along with the mechanisms required for rollback. We present resource management issues consequent to our technique. We study how validation and speculation frequencies impact the performance of the program. Finally, we present our implementation in the context of the Huffman encoder benchmark, running it in different configurations and on different architectures.