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

A comprehensive study on disease risk predictions in machine learning

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted

DENCAST: distributed density-based clustering for multi-target regression

Recent developments in sensor networks and mobile computing led to a huge increase in data generated that need to be processed and analyzed efficiently. In this context, many distributed data mining algorithms have recently been proposed. Following this line of research, we propose the DENCAST system, a novel distributed algorithm implemented in Apache Spark, which performs density-based clustering and exploits the identified clusters to solve both single- and multi-target regression tasks (and thus, solves complex tasks such as time series prediction). Contrary to existing distributed methods, DENCAST does not require a final merging step (usually performed on a single machine) and is able to handle large-scale, high-dimensional data by taking advantage of locality sensitive hashing. Experiments show that DENCAST performs clustering more efficiently than a state-of-the-art distributed clustering algorithm, especially when the number of objects increases significantly. The quality of the extracted clusters is confirmed by the predictive capabilities of DENCAST on several datasets: It is able to significantly outperform (p-value $$<0.05$$ ) state-of-the-art distributed regression methods, in both single and multi-target settings

Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis

In high performance computing environments, we observe an ongoing increase in the available numbers of cores. This development calls for re-emphasizing performance (scalability) analysis and speedup laws as suggested in the literature (e.g., Amdahl's law and Gustafson's law), with a focus on asymptotic performance. Understanding speedup and efficiency issues of algorithmic parallelism is useful for several purposes, including the optimization of system operations, temporal predictions on the execution of a program, and the analysis of asymptotic properties and the determination of speedup bounds. However, the literature is fragmented and shows a large diversity and heterogeneity of speedup models and laws. These phenomena make it challenging to obtain an overview of the models and their relationships, to identify the determinants of performance in a given algorithmic and computational context, and, finally, to determine the applicability of performance models and laws to a particular parallel computing setting. In this work, we provide a generic speedup (and thus also efficiency) model for homogeneous computing environments. Our approach generalizes many prominent models suggested in the literature and allows showing that they can be considered special cases of a unifying approach. The genericity of the unifying speedup model is achieved through parameterization. Considering combinations of parameter ranges, we identify six different asymptotic speedup cases and eight different asymptotic efficiency cases. Jointly applying these speedup and efficiency cases, we derive eleven scalability cases, from which we build a scalability typology. Researchers can draw upon our typology to classify their speedup model and to determine the asymptotic behavior when the number of parallel processing units increases. In addition, our results may be used to address various extensions of our setting