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.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- Heterogeneous Systems for Energy Efficient Scientific Computing
- Springer Berlin Heidelberg
ec4u, Neuer Inhalt/© ITandMEDIA