Towards optimizing energy costs of algorithms for shared memory architectures
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Analysis of Parallel Algorithms for Energy Conservation with GPU
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Energy cost evaluation of parallel algorithms for multiprocessor systems
Cluster Computing
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Chip multiprocessing has demonstrated to be a promising approach in microprocessor design. With ever-increasing concerns for energy consumption, performance-energy trade-offs are often necessary, especially in the design of real-time embedded systems. This paper presents our performance and energy study on an in-house developed FPGA-based mixed-mode chip multiprocessor, where the SIMD (Single- Instruction, Multiple-Data), MIMD (Multiple- Instruction, Multiple-Data) and M-SIMD (Multiple- SIMD) computing modes can coexist simultaneously in one system. We propose performance-energy trade-off techniques based on the observation that SIMD and MIMD tasks involve substantially different granularities of computation and communication, which result in different time and energy behaviors; this provides us with opportunities to realize various performance-energy objectives. Generalized matrix-matrix multiplication (MMM) is employed as an example to illustrate our approach. Experimental results on a Xilinx Virtex II XC2V6000-5 FPGA demonstrate the effectiveness of the proposed approach.