Stride directed prefetching in scalar processors
MICRO 25 Proceedings of the 25th annual international symposium on Microarchitecture
ACM Computing Surveys (CSUR)
Dead-block prediction & dead-block correlating prefetchers
ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
Timekeeping in the memory system: predicting and optimizing memory behavior
ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
Sequential Hardware Prefetching in Shared-Memory Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Improving the Effectiveness of Software Prefetching with Adaptive Execution
PACT '96 Proceedings of the 1996 Conference on Parallel Architectures and Compilation Techniques
IEEE Transactions on Computers
AC/DC: An Adaptive Data Cache Prefetcher
Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques
SBAC-PAD '04 Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing
MicroLib: A Case for the Quantitative Comparison of Micro-Architecture Mechanisms
Proceedings of the 37th annual IEEE/ACM International Symposium on Microarchitecture
The M5 Simulator: Modeling Networked Systems
IEEE Micro
Reducing Cache Pollution via Dynamic Data Prefetch Filtering
IEEE Transactions on Computers
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
A generic high-performance method for deinterleaving scientific data
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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Data prefetching is an effective way to bridge the increasing performance gap between processor and memory. Prefetching can improve performance but it has some side effects which may lead to no performance improvement while increasing memory pressure or to performance degradation. Adaptive prefetching aims at reducing negative effects of prefetching while keeping its advantages. This paper proposes an adaptive prefetching method based on runtime activity, which corresponds to the processor and memory activities retrieved by hardware counters, to predict the prefetch efficiency. Our approach highlights and relies on the correlation between the prefetch effects and runtime activity. Our method learns all along the execution this correlation to predict the prefetch efficiency in order to filter out predicted inefficient prefetches. Experimental results show that the proposed filter is able to cancel the negative impact of prefetching when it is unprofitable while keeping the performance improvement due to prefetching when it is beneficial. Our filter works similarly well when several threads are running simultaneously which shows that runtime activity enables an efficient adaptation of prefetch by providing information on running-applications behaviors and interactions.