Memory Dependence Prediction in Multimedia Applications.
Andreas Moshovos, Gurindar S. Sohi · 2000
We identify that a set of multimedia applications exhibit highly regular read-after-read (RAR) and read-after-write (RAW) memory dependence streams. We exploit this regularity to predict both RAW and RAR memory dependences. We also study how two previously proposed memory dependence prediction-based memory latency reduction techniques perform for this multimedia workload. In the first technique, a load can obtain a value by simply naming a preceding load (or store) with which a RAR (or RAW) dependence is predicted. The second technique speculatively converts a series of LOAD 1 -USE 1 ,...,LOAD N -USE N (or DEF-STORE-LOAD-USE) chains into a single LOAD 1 -USE 1 ...USE N (or DEF-USE) producer/consumer graph. We show that via memory dependence prediction it is possible to correctly predict 33.3% of all loads on the average. Moreover, the two memory dependence prediction based techniques result on average performance improvements of 2.6% over a highly-aggressive, out-of-order, ...