Exploiting Value Locality to Exceed the Dataflow Limit Corresponding

The serialization constraints imposed by true data dependences have always been regarded as an absolute dataflow limit on the parallel execution of serial programs. This paper describes value prediction, a new technique that allows data dependent instructions to issue and execute in parallel without violating program semantics. This technique exploits value locality, or the likelihood of the recurrence of a previously-seen value within a storage location inside a computer system. Value prediction consists of predicting entire 32and 64-bit register values based on previously-seen values. We find that values loaded from memory or generated by ALU instructions are frequently predictable. Furthermore, we show that simple microarchitectural enhancements to a modern microprocessor implementation based on the PowerPC 620 that enable value prediction can effectively exploit value locality to collapse true dependences, reduce average memory and result latencies, and provide average performance gains of 3%-23% by exceeding the dataflow limit.