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Cazorla, Francisco

Publications and source records attributed to Cazorla, Francisco.

Adaptive prefetching on POWER7: Improving performance and power consumption

Hardware data prefetch engines are integral parts of many general purpose server-class microprocessors in the field today. Some prefetch engines allow users to change some of their parameters. But, the prefetcher is usually enabled in a default configuration during system bring-up, and dynamic reconfiguration of the prefetch engine is not an autonomic feature of current machines. Conceptually, however, it is easy to infer that commonly used prefetch algorithms—when applied in a fixed mode—will not help performance in many cases. In fact, they may actually degrade performance due to useless bus bandwidth consumption and cache pollution, which in turn, will also waste power. We present an adaptive prefetch scheme that dynamically modifies the prefetch settings in order to adapt to workloads

adaptive system↗

Making Data Prefetch Smarter: Adaptive Prefetching on POWER7

Hardware data prefetch engines are integral parts of many general purpose server-class microprocessors in the eld to- day. Some prefetch engines allow the user to change some of their parameters. The prefetcher, however, is usually enabled in a default conguration during system bring-up and dynamic reconguration of the prefetch engine is not an autonomic feature of current machines. Conceptually, however, it is easy to infer that commonly used prefetch algorithms, when applied in a xed mode will not help per- formance in many cases. In fact, they may actually de- grade performance due to useless bus bandwidth consump- tion and cache pollution. In this paper, we present an adap- tive prefetch scheme that dynamically modies the prefetch settings in order to adapt to the workload requirements. We implement and evaluate adaptive prefetching in the con- text of an existing, commercial processor, namely the IBM POWER7. Our adaptive prefetch mechanism improves per- formance with respect to the default prefetch setting up to 2.7X and 30% for single-threaded and multiprogrammed workloads, respectively. Categories

Jimenez, Victor↗