The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units （GPU）. This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.
因为对高性能微芯片和系统设计的广泛影响,能量消耗问题受到计算机界越来越广泛的关注.多个层次的技术被用于改善系统的能量效率,并行处理是体系结构层提高能量效率的主要手段.并行处理使用性能适中的计算节点减少能量消耗,使用多个节点并行执行维持高吞吐量.文中分析了并行处理提高能量效率的基本原理,给出了并行处理的时间开销和能量开销模型.基于模型分析,对低电压并行系统、动态电压调节(Dynamic Voltage Scaling,DVS)的并行系统和多核微处理器3个并行处理方向进行了展望,给出了这些并行处理方向改善能量效率的空间.
