Accelerating linpack with CUDA on heterogenous clusters
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
Proceedings of the 36th annual international symposium on Computer architecture
Combining optimizations in automated low power design
Proceedings of the Conference on Design, Automation and Test in Europe
Power-Efficient Work Distribution Method for CPU-GPU Heterogeneous System
ISPA '10 Proceedings of the International Symposium on Parallel and Distributed Processing with Applications
Introduction to High Performance Scientific Computing
Introduction to High Performance Scientific Computing
Compiling for power with ScalaPipe
Journal of Systems Architecture: the EUROMICRO Journal
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This paper introduces a novel approach for exploring heterogeneous computing engines which include GPUs and FPGAs as accelerators. Our goal is to systematically automate finding solutions for such engines that maximize energy efficiency while meeting requirements in throughput and in resource constraints. The proposed approach, based on a linear programming model, enables optimization of system throughput and energy efficiency, and analysis of energy efficiency sensitivity and power consumption issues. It can be used in evaluating current and future computing hardware and interfaces to identify appropriate combinations. A heterogeneous system containing a CPU, a GPU and an FPGA with a PCI Express interface is studied based on the High Performance Linpack application. Results indicate that such a heterogeneous computing system is able to provide energy-efficient solutions to scientific computing with various performance demands. The improvement of system energy efficiency is more sensitive to some of the system components, for example in the studied system concurrently improving the energy efficiency of the interface and the GPU by 10 times could lead to over 10 times improvement of the system energy efficiency.