Available parallelism with data value prediction

Data dependences (data flow constraints) present a major hurdle to the amount of instruction-level parallelism that can be exploited from a program. Recent work has focused on the use of data value prediction to overcome the limits imposed by data dependences. That is, when an instruction is fetched, its result can be predicted so that subsequent instructions that depend on the result can execute earlier using the predicted value. When the correct result becomes available, it is compared against the value predicted earlier, so as to validate the prediction. Whereas significant work has been done towards developing schemes for accurately predicting data values, not much work has been done towards understanding and quantifying the performance impact of data value prediction. This paper presents a quantitative study of the impact of data value prediction on available parallelism. Our studies, done with the MIPS instruction set and a collection of SPEC95 integer benchmarks, show that data value prediction provides significant increases in available parallelism when infinite size instruction window and perfect branch prediction are used. Our studies with finite size windows shows that the impact of data value prediction is not very significant for small window sizes such as 64. When the instruction window size is increased, the benefits of data value prediction become more apparent.