Prediction models for multi-dimensional power-performance optimization on many cores
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Thread motion: fine-grained power management for multi-core systems
Proceedings of the 36th annual international symposium on Computer architecture
Power Consumption of GPUs from a Software Perspective
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
PDCAT '09 Proceedings of the 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies
Energy-Aware Optimisation for Run-Time Reconfiguration
FCCM '10 Proceedings of the 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines
Energy-aware high performance computing with graphic processing units
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Power modeling and characteristics of field programmable gate arrays
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Fast Design Exploration for Performance, Power and Accuracy Tradeoffs in FPGA-Based Accelerators
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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Processing speed and energy efficiency are two of the most critical issues for computer systems. This paper presents a systematic approach for profiling the power and performance characteristics of application targeting heterogeneous multi-core computing platforms. Our approach enables rapid and automated design space exploration involving optimisation of workload distribution for systems with accelerators such as FPGAs and GPUs. We demonstrate that, with minor modification to the design, it is possible to estimate performance and power efficiency trade off to identify optimized workload distribution. Our approach shows that, for N-body computation, the fastest design which involves 2 CPU cores, 10 FPGA cores and 40960 GPU threads, is 2 times faster than a design with only FPGAs while achieving better overall energy efficiency.