A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Improving Software Performance in the Compute Unified Device Architecture

This paper analyzes several aspects regarding the improvement of software performance for applications written in the Compute Unified Device Architecture CUDA). We address an issue of great importance when programming a CUDA application: the Graphics Processing Unit’s (GPU’s) memory management through ranspose ernels. We also benchmark and evaluate the performance for progressively optimizing a transposing matrix application in CUDA. One particular interest was to research how well the optimization techniques, applied to software application written in CUDA, scale to the latest generation of general-purpose graphic processors units (GPGPU), like the Fermi architecture implemented in the GTX480 and the previous architecture implemented in GTX280. Lately, there has been a lot of interest in the literature for this type of optimization analysis, but none of the works so far (to our best knowledge) tried to validate if the optimizations can apply to a GPU from the latest Fermi architecture and how well does the Fermi architecture scale to these software performance improving techniques.Compute Unified Device Architecture, Fermi Architecture, Naive Transpose, Coalesced Transpose, Shared Memory Copy, Loop in Kernel, Loop over Kernel

Co-Occurrence of Multiple Risk Factors and Intimate Partner Violence in an Urban Emergency Department

Introduction: Urban emergency departments (ED) provide care to populations with multiple health-related and overlapping risk factors, many of which are associated with intimate partner violence (IPV). We examine the 12-month rate of physical IPV and its association with multiple joint risk factors in an urban ED.Methods: Research assistants surveyed patients regarding IPV exposure, associated risk factors, and other sociodemographic features. The joint occurrence of seven risk factors was measured by a variable scored 0–7 with the following risk factors: depression; adverse childhood experiences; drug use; impulsivity; post-traumatic stress disorder; at-risk drinking; and partner’s score on the Alcohol Use Disorders Identification Test. The survey (N = 1037) achieved an 87.5% participation rate.Results: About 23% of the sample reported an IPV event in the prior 12 months. Logistic regression showed that IPV risk increased in a stepwise fashion with the number of present risk factors, as follows: one risk factor (adjusted odds ratio [AOR] [3.09]; 95% confidence interval [CI], 1.47-6.50; p<.01); two risk factors (AOR [6.26]; 95% CI, 3.04-12.87; p<.01); three risk factors (AOR = 9.44; 95% CI, 4.44-20.08; p<.001); four to seven risk factors (AOR [18.62]; 95% CI, 9.00-38.52; p<001). Ordered logistic regression showed that IPV severity increased in a similar way, as follows: one risk factor (AOR [3.17]; 95% CI, 1.39-7.20; p<.01); two risk factors (AOR [6.73]; 95% CI, 3.04-14.90; p<.001); three risk factors (AOR [10.36]; 95%CI, 4.52-23.76; p<.001); four to seven risk factors (AOR [20.61]; 95% CI, 9.11-46.64; p<001).Conclusion: Among patients in an urban ED, IPV likelihood and IPV severity increase with the number of reported risk factors. The best approach to identify IPV and avoid false negatives is, therefore, multi-risk assessment