Browsing by Subject "Benchmarking"
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Item A Benchmarking Platform For Network-On-Chip (NOC) Multiprocessor System-On- Chips(2012-02-14) Malave-Bonet, JavierNetwork-on-Chip (NOC) based designs have garnered significant attention from both researchers and industry over the past several years. The analysis of these designs has focused on broad topics such as NOC component micro-architecture, fault-tolerant communication, and system memory architecture. Nonetheless, the design of lowlatency, high-bandwidth, low-power and area-efficient NOC is extremely complex due to the conflicting nature of these design objectives. Benchmarks are an indispensable tool in the design process; providing thorough measurement and fair comparison between designs in order to achieve optimal results (i.e performance, cost, quality of service). This research proposes a benchmarking platform called NoCBench for evaluating the performance of Network-on-chip. Although previous research has proposed standard guidelines to develop benchmarks for Network-on-Chip, this work moves forward and proposes a System-C based simulation platform for system-level design exploration. It will provide an initial set of synthetic benchmarks for on-chip network interconnection validation along with an initial set of standardized processing cores, NOC components, and system-wide services. The benchmarks were constructed using synthetic applications described by Task Graphs For Free (TGFF) task graphs extracted from the E3S benchmark suite. Two benchmarks were used for characterization: Consumer and Networking. They are characterized based on throughput and latency. Case studies show how they can be used to evaluate metrics beyond throughput and latency (i.e. traffic distribution). The contribution of this work is two-fold: 1) This study provides a methodology for benchmark creation and characterization using NoCBench that evaluates important metrics in NOC design (i.e. end-to-end packet delay, throughput). 2) The developed full-system simulation platform provides a complete environment for further benchmark characterization on NOC based MpSoC as well as system-level design space exploration.Item Are icons pictures or logographical words? Statistical, behavioral, and neuroimaging measures of semantic interpretations of four types of visual information(2012-05) Huang, Sheng-Cheng; Bias, Randolph G.; Dillon, Andrew; Francisco-Revilla, Luis; Schnyer, David; Sussman, HarveyThis dissertation is composed of three studies that use statistical, behavioral, and neuroimaging methods to investigate Chinese and English speakers’ semantic interpretations of four types of visual information including icons, single Chinese characters, single English words, and pictures. The goal is to examine whether people cognitively process icons as logographical words. By collecting survey data from 211 participants, the first study investigated how differently these four types of visual information can express specific meanings without ambiguity on a quantitative scale. In the second study, 78 subjects participated in a behavioral experiment that measured how fast people could correctly interpret the meaning of these four types of visual information in order to estimate the differences in reaction times needed to process these stimuli. The third study employed functional magnetic resonance imaging (fMRI) with 20 participants selected from the second study to identify brain regions that were needed to process these four types of visual information in order to determine if the same or different neural networks were required to process these stimuli. Findings suggest that 1) similar to pictures, icons are statistically more ambiguous than English words and Chinese characters to convey the immediate semantics of objects and concepts; 2) English words and Chinese characters are more effective and efficient than icons and pictures to convey the immediate semantics of objects and concepts in terms of people’s behavioral responses, and 3) according to the neuroimaging data, icons and pictures require more resources of the brain than texts, and the pattern of neural correlates under the condition of reading icons is different from the condition of reading Chinese characters. In conclusion, icons are not cognitively processed as logographical words like Chinese characters although they both stimulate the semantic system in the brain that is needed for language processing. Chinese characters and English words are more evolved and advanced symbols that are less ambiguous, more efficient and easier for a literate brain to understand, whereas graphical representations of objects and concepts such as icons and pictures do not always provide immediate and unambiguous access to meanings and are prone to various interpretations.Item Benchmarking tests on recovery oriented computing(2012-05) Raman, Nandita; Perry, Dewayne E.; Krasner, HerbBenchmarks have played a very important role in guiding the progress of computer science systems in various ways. Specifically, in Autonomous environments it has a major role to play. System crashes and software failures are a basic part of a software system’s life-cycle and to overcome or rather make it as less vulnerable as possible is the main purpose of recovery oriented computing. This is usually done by trying to reduce the downtime by automatically and efficiently recovering from a broad class of transient software failures without having to modify applications. There have been various types of benchmarks for recovering from a failure, but in this paper we intend to create a benchmark framework called the warning benchmarks to measure and evaluate the recovery oriented systems. It consists of the known and the unknown failures and few benchmark techniques which the warning benchmarks handle with the help of various other techniques in software fault analysis.Item Electrical and Production Load Factors(2010-07-14) Sen, TapajyotiLoad factors are an important simplification of electrical energy use data and depend on the ratio of average demand to peak demand. Based on operating hours of a facility they serve as an important benchmarking tool for the industrial sector. The operating hours of small and medium sized manufacturing facilities are analyzed to identify the most common operating hour or shift work patterns. About 75% of manufacturing facilities fall into expected operating hour patterns with operating hours near 40, 80, 120 and 168 hours/week. Two types of load factors, electrical and production are computed for each shift classification within major industry categories in the U.S. The load factor based on monthly billing hours (ELF) increases with operating hours from about 0.4 for a nominal one shift operation, to about 0.7 for around-the-clock operation. On the other hand, the load factor based on production hours (PLF) shows an inverse trend, varying from about 1.4 for one shift operation to 0.7 for around-the-clock operation. When used as a diagnostic tool, if the PLF exceeds unity, then unnecessary energy consumption may be taking place. For plants operating at 40 hours per week, the ELF value was found to greater than the theoretical maximum, while the PLF value was greater than one, suggesting that these facilities may have significant energy usage outside production hours. The data for the PLF however, is more scattered for plants operating less than 80 hours per week, indicating that grouping PLF data based on operating hours may not be a reasonable approach to benchmarking energy use in industries. This analysis uses annual electricity consumption and demand along with operating hour data of manufacturing plants available in the U.S. Department of Energy?s Industrial Assessment Center (IAC) database. The annual values are used because more desirable monthly data are not available. Monthly data are preferred as they capture the load profile of the facility more accurately. The data there come from Industrial Assessment Centers which employ university engineering students, faculty and staff to perform energy assessments for small to medium-sized manufacturing plants. The nation-wide IAC program is sponsored by the U.S. Department of Energy.Item Enabling the evaluation of learning in instructable software agents(2012-08) Grant, Robert David; Perry, Dewayne E.; Julien, Christine; Nettles, Scott M.; Bias, Randolph G.; Ryall, KathyAn Instructable Software Agent (ISA) is a software agent that humans can teach through Natural Instruction Methods (NIMs)—methods humans naturally use to teach one another. Some examples of NIMs include giving demonstrations, guided practice sessions, and definitions of concepts. If software agents were instructable, humans would be able to impart knowledge to software systems though a more natural interface. In this dissertation, I address generating benchmarks for evaluating the learning ability of ISAs despite the important differences that may exist between human learners and ISAs. I first present three years of case studies uncovering the challenges of such a comparison and then make recommendations for future studies. The main contributions of this dissertation are 1. a theory of using humans to evaluate the learning ability of Instructable Software Agents (ISAs), 2. a refined method for developing curricula and benchmarks for evaluating ISAs, including a scalable lab configuration for performing human benchmarking and a suite of accompanying software tools, and 3. the case studies themselves, amounting to an in-depth ethnographic study of the issues involved in using humans to develop curricula and benchmarks for ISAs.Item Industrial Energy Use Indices(2009-05-15) Hanegan, Andrew AaronEnergy use index (EUI) is an important measure of energy use which normalizes energy use by dividing by building area. Energy use indices and associated coefficients of variation are computed for major industry categories for electricity and natural gas use in small and medium-sized plants in the U.S. The data is very scattered with the coefficients of variation (CoV) often exceeding the average EUI for an energy type. The combined CoV from all of the industries considered, which accounts for 8,200 plants from all areas of the continental U.S., is 290%. This paper discusses EUIs and their variations based on electricity and natural gas consumption. Data from milder climates appears more scattered than that from colder climates. For example, the ratio of the average of coefficient of variations for all industry types in warm versus cold regions of the U.S. varies from 1.1 to 1.7 depending on the energy sources considered. The large data scatter indicates that predictions of energy use obtained by multiplying standard EUI data by plant area may be inaccurate and are less accurate in warmer than colder climates (warmer and colder are determined by annual average temperature weather data). Data scatter may have several explanations, including climate, plant area accounting, the influence of low cost energy and low cost buildings used in the south of the U.S. This analysis uses electricity and natural gas energy consumption and area data of manufacturing plants available in the U.S. Department of Energy?s national Industrial Assessment Center (IAC) database. The data there come from Industrial Assessment Centers which employ university engineering students, faculty and staff to perform energy assessments for small to medium-sized manufacturing plants. The nation-wide IAC program is sponsored by the U.S. Department of Energy. A collection of six general energy saving recommendations were also written with Texas manufacturing plants in mind. These are meant to provide an easily accessible starting point for facilities that wish to reduce costs and energy consumption, and are based on common recommendations from the Texas A&M University IAC program.Item Inpatient Rehabilitation Outcomes for Patients with Debility(2013-06-03) Galloway, Rebecca 1977-; Ottenbacher, Kenneth J; Granger, Carl V; Raji, Mukaila; Tan, Alai; Graham, James EBackground: Inpatient rehabilitation facility (IRF) goals are to optimize functional independence and discharge patients to community living. Debility, or deconditioning associated with hospitalization, is the fourth most common impairment group, accounting for about 10% of IRF cases. Objectives were to provide benchmark data for patients with debility, consider trends with respect to health care policy changes, identify risk factors for discharge to acute or subacute care, and examine readmission to acute care after discharge to community. Aim 1: National benchmark data for years 2000 – 2010 were retrospectively analyzed for 260,373 patients from 830 IRFs contributing to the Uniform Data System for Medical Rehabilitation. Trends from 2000 to 2010 included decrease in mean (SD) FIM® instrument (“FIM”) total admission ratings from 73.9 (16.2) to 62.5 (15.8). FIM total discharge ratings decreased from 95.0 (19.7) in 2000 to 88.2 (19.8) in 2010. Mean length of stay decreased from 14.3 (9.1) in 2000 to 12.1 (6.2) days in 2010. FIM efficiency increased from 1.9 (1.7) in 2000 to 2.4 (1.9) in 2010. Discharge to community decreased from 80% in 2000 to 75% in 2010. Health policy changes may have influenced trends. Aims 2 & 3: Centers for Medicare and Medicaid Services data (years 2006 to 2009) were analyzed for factors associated with discharge to acute or subacute care (N = 67,626) and readmissions for 90 days following discharge (N = 45,424). Discharge setting was 76% community, 13% subacute, and 11% acute care. Significant risk factors for both acute and subacute discharge settings were lower FIM motor subscale, male gender, living alone, comorbidity tier, weight loss, and fluid/electrolyte disorders. Rehospitalization rates were 19% at 30 days and 34% at 90 days. Congestive heart failure, renal failure, and chronic pulmonary disease were common causes of hospital readmission and independent risk factors for reshospitalization. Conclusions: National data indicate the number of debility cases is increasing with diverse etiologic diagnoses. A high proportion of patients discharged to acute or subacute care. One-third of patients who discharged to the community experienced acute hospital readmission within 90 days. Functional independence is an important indicator for discharge setting and rehospitalization.Item Inpatient Rehabilitation Outcomes for Patients with Debility(2013-06-03) Galloway, Rebecca 1977-; Ottenbacher, Kenneth J; Granger, Carl V; Raji, Mukaila; Tan, Alai; Graham, James EBackground: Inpatient rehabilitation facility (IRF) goals are to optimize functional independence and discharge patients to community living. Debility, or deconditioning associated with hospitalization, is the fourth most common impairment group, accounting for about 10% of IRF cases. Objectives were to provide benchmark data for patients with debility, consider trends with respect to health care policy changes, identify risk factors for discharge to acute or subacute care, and examine readmission to acute care after discharge to community. Aim 1: National benchmark data for years 2000 – 2010 were retrospectively analyzed for 260,373 patients from 830 IRFs contributing to the Uniform Data System for Medical Rehabilitation. Trends from 2000 to 2010 included decrease in mean (SD) FIM® instrument (“FIM”) total admission ratings from 73.9 (16.2) to 62.5 (15.8). FIM total discharge ratings decreased from 95.0 (19.7) in 2000 to 88.2 (19.8) in 2010. Mean length of stay decreased from 14.3 (9.1) in 2000 to 12.1 (6.2) days in 2010. FIM efficiency increased from 1.9 (1.7) in 2000 to 2.4 (1.9) in 2010. Discharge to community decreased from 80% in 2000 to 75% in 2010. Health policy changes may have influenced trends. Aims 2 & 3: Centers for Medicare and Medicaid Services data (years 2006 to 2009) were analyzed for factors associated with discharge to acute or subacute care (N = 67,626) and readmissions for 90 days following discharge (N = 45,424). Discharge setting was 76% community, 13% subacute, and 11% acute care. Significant risk factors for both acute and subacute discharge settings were lower FIM motor subscale, male gender, living alone, comorbidity tier, weight loss, and fluid/electrolyte disorders. Rehospitalization rates were 19% at 30 days and 34% at 90 days. Congestive heart failure, renal failure, and chronic pulmonary disease were common causes of hospital readmission and independent risk factors for reshospitalization. Conclusions: National data indicate the number of debility cases is increasing with diverse etiologic diagnoses. A high proportion of patients discharged to acute or subacute care. One-third of patients who discharged to the community experienced acute hospital readmission within 90 days. Functional independence is an important indicator for discharge setting and rehospitalization.Item Managing large energy and mineral resources (EMR) projects in challenging environments(2009-05) Chanmeka, Arpamart; Thomas, Stephen Richard, 1949-; Caldas, Carlos H.The viability of energy mineral resources (EMR) construction projects is contingent upon the state of the world economic climate. Oil sands projects in Alberta, Canada exemplify large EMR projects that are highly sensitive to fluctuations in the world market. Alberta EMR projects are constrained by high fixed production costs and are also widely recognized as one of the most challenging construction projects to successfully deliver due to impacts from extreme weather conditions, remote locations and issues with labor availability amongst others. As indicated in many studies, these hardships strain the industry’s ability to execute work efficiently, resulting in declining productivity and mounting cost and schedule overruns. Therefore, to enhance the competitiveness of Alberta EMR projects, project teams are targeting effective management strategies to enhance project performance and productivity by countering the uniquely challenging environment in Alberta. The main purpose of this research is to develop industry wide benchmarking tailored to the specific constraints and challenges of Alberta. Results support quantitative assessments and identify the root causes of project performance and ineffective field productivity problems in the heavy industry sector capital projects. Customized metrics produced from the data collected through a web-based survey instrument were used to quantitatively assess project performance in the following dimensions: cost, schedule, change, rework, safety, engineering and construction productivity and construction practices. The system enables the industry to measure project performance more accurately, get meaningful comparisons, while establishing credible norms specific to Alberta projects. Data analysis to identify the root cause of performance problems was conducted. The analysis of Alberta projects substantiated lessons of previous studies to create an improved awareness of the abilities of Alberta-based companies to manage their unique projects. This investigation also compared Alberta- based projects with U.S. projects to point out the differences in project process and management strategies under different environments. The relative impact of factors affecting construction productivity were identified and validated by the input from industry experts. The findings help improve the work processes used by companies developing projects in Alberta.Item Model for multi-strata safety performance measurements in the process industry(Texas A&M University, 2004-09-30) Keren, NirMeasuring process safety performance is a challenge, and the wide variations in understanding, compliance, and implementation of process safety programs increase the challenge. Process safety can be measured in three strata: (1) measurement of process safety elements within facilities; (2) benchmarking of process safety elements among facilities; and (3) use of incident data collection from various sources for industrial safety performance assessment. The methods presently available for measurement of process safety within facilities are deficient because the results are strongly dependent on user judgment. Performance benchmarking among facilities is done within closed groups of organizations. Neither the questionnaires nor the results are available to the public. Many organizations collect data on industrial incidents. These organizations differ from each other in their interests, data collection procedures, definitions, and scope, and each of them analyzes its data to achieve its objectives. However, there have been no attempts to explore the potential of integrating data sources and harnessing these databases for industrial safety performance assessment. In this study we developed models to pursue the measurement of samples of the strata described above. The measurement methodologies employed herein overcome the disadvantages of existing methodologies and increase their capabilities.