Data Prefetching Using Off-Line Learning
Palem, Krishna V.
Wong, Weng Fai
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An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from insertion of prefetch instructions by means of program analysis to strictly hardware prefetch mechanisms have been proposed. The former, however, is less successful for pointer intensive applications. In this paper, we propose a hardware solution that utilizes off-line learning algorithms. In essence, a sample trace of the application is fed into various off-line learning schemes. The results from these schemes are then loaded into a prefetching hardware at the appropriate point in the execution of the application to drive the prefetching. We propose a general architecture and scheme for such a process and report on the results of some of the experiments we performed.