The recently released SPEC CPU2006 benchmark suite is expected to be used by computer designers and computer architecture researchers for pre-silicon early design analysis. Partial use of benchmark suites by researchers, due to simulation time constraints, compiler difficulties, or library or system call issues is likely to happen; but a random subset can lead to misleading results. This paper analyzes the SPEC CPU2006 benchmarks using performance counter based experimentation from several state of the art systems, and uses statistical techniques such as principal component analysis and clustering to draw inferences on the similarity of the benchmarks and the redundancy in the suite and arrive at meaningful subsets.

A key challenge in benchmarking is to predict the performance of an application of interest on a number of platforms in order to determine which platform yields the best performance. This paper proposes an approach for doing this. We measure a number of microarchitecture-independent characteristics from the application of interest, and relate these characteristics to the characteristics of the programs from a previously profiled benchmark suite. Based on the similarity of the application of interest with programs in the benchmark suite, we make a performance prediction of the application of interest. We propose and evaluate three approaches (normalization, principal components analysis and genetic algorithm) to transform the raw data set of microarchitecture-independent characteristics into a benchmark space in which the relative distance is a measure for the relative performance differences. We evaluate our approach using all of the SPEC CPU2000 benchmarks and real hardware performance numbers from the SPEC website. Our framework estimates per-benchmark machine ranks with a 0.89 average and a 0.80 worst case rank correlation coefficient.

Since benchmarks drive computer science research and industry product development, which ones we use and how we evaluate them are key questions for the community. Despite complex runtime tradeoffs due to dynamic compilation and garbage collection required for Java programs, many evaluations still use methodologies developed for C, C++, and Fortran. SPEC, the dominant purveyor of benchmarks, compounded this problem by institutionalizing these methodologies for their Java benchmark suite. This paper recommends benchmarking selection and evaluation methodologies, and introduces the DaCapo benchmarks, a set of open source, client-side Java benchmarks. We demonstrate that the complex interactions of (1) architecture, (2) compiler, (3) virtual machine, (4) memory management, and (5) application require more extensive evaluation than C, C++, and Fortran which stress (4) much less, and do not require (3). We use and introduce new value, time-series, and statistical metrics for static and dynamic properties such as code complexity, code size, heap composition, and pointer mutations. No benchmark suite is definitive, but these metrics show that DaCapo improves over SPEC Java in a variety of ways, including more complex code, richer object behaviors, and more demanding memory system requirements. This paper takes a step towards improving methodologies for choosing and evaluating benchmarks to foster innovation in system design and implementation for Java and other managed languages.

This paper proposes a methodology for measuring the similarity between programs based on their inherent microarchitecture-independent characteristics, and demonstrates two applications for it: 1) finding a representative subset of programs from benchmark suites and 2) studying the evolution of four generations of SPEC CPU benchmark suites. Using the proposed methodology, we find a representative subset of programs from three popular benchmark suites - SPEC CPU2000, MediaBench, and MiBench. We show that this subset of representative programs can be effectively used to estimate the average benchmark suite IPC, L1 data cache miss-rates, and speedup on 11 machines with different ISAs and microarchitectures - this enables one to save simulation time with little loss in accuracy. From our study of the similarity between the four generations of SPEC CPU benchmark suites, we find that, other than a dramatic increase in the dynamic instruction count and increasingly poor temporal data locality, the inherent program characteristics have more or less remained unchanged.

It is essential that a subset of benchmark programs used to evaluate an architectural enhancement, is well distributed within the target workload space rather than clustered in specific areas. Past efforts for identifying subsets have primarily relied on using microarchitecture-dependent metrics of program performance, such as cycles per instruction and cache miss-rate. The shortcoming of this technique is that the results could be biased by the idiosyncrasies of the chosen configurations. The objective of this paper is to present a methodology to measure similarity of programs based on their inherent microarchitecture-independent characteristics which will make the results applicable to any microarchitecture. We apply our methodology to the SPEC CPU2000 benchmark suite and demonstrate that a subset of 8 programs can be used to effectively represent the entire suite. We validate the usefulness of this subset by using it to estimate the average IPC and L1 data cache miss-rate of the entire suite. The average IPC of 8-way and 16-way issue superscalar processor configurations could be estimated with 3.9% and 4.4% error respectively. This methodology is applicable not only to find subsets from a benchmark suite, but also to identify programs for a benchmark suite from a list of potential candidates. Studying the four generations of SPEC CPU benchmark suites, we find that other than a dramatic increase in the dynamic instruction count and increasingly poor temporal data locality, the inherent program characteristics have more or less remained the same

We propose a set of statistical metrics for making a comprehensive, fair, and insightful evaluation of features, clustering algorithms, and distance measures in representative sampling techniques for microprocessor simulation. Our evaluation of clustering algorithms using these metrics shows that CLARANS clustering algorithm produces better quality clusters in the feature space and more homogeneous phases for CPI compared to the popular k-means algorithm. We also propose a new micro-architecture independent data locality based feature, reuse distance distribution (RDD), for finding phases in programs, and show that the RDD feature consistently results in more homogeneous phases than basic block vector (BBV) for many SPEC CPU2000 benchmark programs.