Tolerating latency through software-controlled data prefetching
Tolerating latency through software-controlled data prefetching
Evaluating the performance of multithreading and prefetching in multiprocessors
Journal of Parallel and Distributed Computing - Special issue on multithreading for multiprocessors
Improving index performance through prefetching
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Dynamic voltage scaling on a low-power microprocessor
Proceedings of the 7th annual international conference on Mobile computing and networking
Compiler-directed dynamic voltage/frequency scheduling for energy reduction in microprocessors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Energy-conscious compilation based on voltage scaling
Proceedings of the joint conference on Languages, compilers and tools for embedded systems: software and compilers for embedded systems
What is the limit of energy saving by dynamic voltage scaling?
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Optimal voltage allocation techniques for dynamically variable voltage processors
Proceedings of the 40th annual Design Automation Conference
Compile-time dynamic voltage scaling settings: opportunities and limits
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Compiler-directed dynamic voltage and frequency scaling for cpu power and energy reduction
Compiler-directed dynamic voltage and frequency scaling for cpu power and energy reduction
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High energy consumption has become a limiting factor for battery-operated embedded systems. Most low-power compiler optimization techniques take the approach of minimizing the energy consumption while meeting small performance loss. In addition, it is possible that the available energy budget is not sufficient to meet the optimal performance objective. In such situation, energy-constrained optimization is more significant. In this paper, we explore two kinds of energy-aware prefetching optimizations: prefetching optimization with minimizing energy consumption and energy-constrained prefetching optimization. We exploit energy saving opportunities through reducing memory stalls and CPU stalls caused by too early or too late prefetching. We build models for these two kinds of energy-aware prefetching optimization approaches and use a group of array-dominated applications to validate our approach.