1442195
Modeling superscalar processors via statistical simulation
Statistical simulation is a technique for fast performance evaluation of superscalar processors. First, intrinsic statistical information is collected from a single detailed simulation of a program. This information is then used to generate a synthetic instruction trace that is fed to a simple processor model, along with cache and branch prediction statistics. Because of the probabilistic nature of the simulation, it quickly converges to a performance rate. The simplicity and simulation speed make it useful for fast design space exploration; as such, it is a good complement to conventional detailed simulation. The accuracy of this technique is evaluated for different levels of modeling complexity. Both errors and convergence properties are studied in detail. A simple instruction model yields an average error of 8% compared with detailed simulation. A more detailed instruction model reduces the error to 5% but requires about three times as long to converge.
195352472
Statistical simulation of symmetric multiprocessor systems
Statistical simulation is driven by a stream of randomly generated instructions, based on statistics collected during a single detailed simulation. This method can give accurate performance estimates within minutes, allowing a large design space to be simulated quickly. Prior work has applied this technique to superscalar processors. We evaluate the extension of statistical simulation to symmetric multiprocessing systems. Key program parameters are identified and program statistics are collected during detailed simulations for both multiprogrammed workloads (SpecInt) and parallel scientific workload (Splash-2). The accuracy of statistical simulation is evaluated at different levels of model detail, and it is shown that for multiprogrammed workloads a 10% average error can be achieved, and for parallel benchmark programs 15% average error can be achieved.
307899
Rapid profiling via stratified sampling
Sophisticated binary translators and dynamic optimizers demand a program profiler with low overhead, high accuracy, and the ability to collect a variety of profile types. A profiling scheme that achieves these goals is proposed. Conceptually, the hardware compresses a stream of profile data by counting identical events; the compressed profile dam is passed to software for analysis. Compressing the high-bandwidth event stream greatly reduces software overhead. Because optimizations can tolerate some profiling errors, we allow the stream compressor to be lossy, thereby enabling a low-cost sampling-based hardware design. Because the hardware compressor is insensitive to the event content, it supports various profile types and can process multiple types simultaneously.
Basic components of our framework are periodic and random samplers, counters, and hash functions. These components are composed to form a variety of stream compressors. One design is both simple and very effective: the input stream is hash-split into multiple substreams, each of which is fed into a simple periodic sampler that selects every kth event. This stratified periodic sampler performs better than conventional random sampling because it biases each substream towards a small number of unique events, thereby reducing sampling error, and allowing faster convergence to an accurate profile. For example, convergence to a given level of accuracy is about twice as fast for gcc. When sampling overhead is considered, the stratified periodic profiler achieves less than 3% error while incurring an overhead of only 3.5% for gcc.
37291538
The Ensembl genome database project
The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.
13961662
Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics
The nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper, a new ensemble technique is proposed to improve the performance of NN classifier. The proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). These feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). The proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI Machine Learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).
7544123
Models and Heuristics for Robust Resource Allocation in Parallel and Distributed Computing Systems
This is an overview of the robust resource allocation research efforts that have been and continue to be conducted by the CSU robustness in computer systems group. Parallel and distributed computing systems, consisting of a (usually heterogeneous) set of machines and networks, frequently operate in environments where delivered performance degrades due to unpredictable circumstances. Such unpredictability can be the result of sudden machine failures, increases in system load, or errors caused by inaccurate initial estimation. The research into developing models and heuristics for parallel and distributed computing systems that create robust resource allocations is presented.
206811307
Learning visual models of semantic concepts
Statistical machine learning provides a computational framework for mapping low level media features to high level semantics concepts. In this paper we expose the challenges that these techniques face. Using support vector machine (SVM) classification we build models for 34 semantic concepts for the TREC 2002 benchmark corpus. We study the effect of number of examples available for training with respect to their impact on detection. We also examine low level feature fusion as well as parameter sensitivity with SVM classifiers.
37610017
Validated Cost Models for Parallel OQL Query Processing
Query cost models are widely used, both for performance analysis and for comparing execution plans during query optimisation. In essence, a cost modelp redicts where time is being spent during query evaluation. Although many cost models have been proposed, for serial, parallel and distributed database systems, surprisingly few of these have been validated against real systems. This paper presents cost models for the parallel evaluation of ODMG OQL queries, which have been compared with experimental results obtained using the Polar object database system. The paper describes the validation of the cost model for a collection of queries, using three join algorithms over the OO7 benchmark database. The results show that the cost model generally both ranks alternative plans appropriately, and gives a useful indication of the response times that can be expected from a plan. The paper also illustrates the application of the cost model by highlighting the contributions of different features and operations to query response times.