摘要：High performance is a critical requirement to all microprocessors manufacturers. Scaling advanced CMOS technology to the next generation effects improves performance, increases transistor density, and reduces power consumption of the processor. In this paper we describe the statistical analysis of SPEC CPU INT 2006 benchmarks workload and their classification. Today we need a processor which can provide a performance boost for many key application areas. We use statistical analysis techniques, Principal Component Analysis (PCA) and Cluster Analysis (CA) for the study of benchmark workload classification using recently published SPEC CPUINT2006 performance numbers of four commercial processors. We calculated three most significant PCs, which are retained for 91.6% of the variance. We classified the CINT benchmarks in two sub groups. We found that the benchmarks 471.omnetpp, 462.libquantum 403.gcc, and 429.mcf exhibits higher memory wait time. Our results and analysis can be used by performance engineers, scientists and developers to better understand the benchmark workload and select input dataset for better microarchitecture design of the processors.
关键词：PCA; SPEC CPU2006; Processor Performance; Benchmarks; Moore's Law
