International audienceThe ridge logistic regression has successfully been used in text categorization problems and it has been shown to reach the same performance as the Support Vector Machine but with the main advantage of computing a probability value rather than a score. However, the dense solution of the ridge makes its use unpractical for large scale categorization. On the other side, LASSO regularization is able to produce sparse solutions but its performance is dominated by the ridge when the number of features is larger than the number of observations and/or when the features are highly correlated. In this paper, we propose a new model selection method which tries to approach the ridge solution by a sparse solution. The method first computes the ridge solution and then performs feature selection. The experimental evaluations show that our method gives a solution which is a good trade-off between the ridge and LASSO solutions

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International audienceThis article presents our approach to provide input/output (I/O) scheduling with double adaptivity: to applications and devices. In high-performance computing environments, parallel file systems provide a shared storage infrastructure to applications. In the situation where multiple applications access this shared infrastructure concurrently, their performance can be impaired because of interference. Our work focuses on I/O scheduling as a tool to improve performance by alleviating interference effects. The role of the I/O scheduler is to decide the order in which applications' requests must be processed by the parallel file system's servers, applying optimizations to adjust the resulting access pattern for improved performance. Our approach to improve I/O scheduling results is based on using information from applications' access patterns and storage devices' sensitivity to access sequentiality. We have applied machine learning to provide the ability to automatically select the best scheduling algorithm for each situation. Our approach improves performance by up to 75% over an approach that uses the same scheduling algorithm to all situations, without adaptability. Our results evidence that both aspects – applications and storage devices – are essential to make good scheduling decisions

We present a comprehensive survey on parallel I/O in the high performance computing (HPC) context. This is an important field for HPC because of the historic gap between processing power and storage latencies, which causes applications performance to be impaired when accessing or generating large amounts of data. As the available processing power and amount of data increase, I/O remains a central issue for the scientific community. In this survey, we focus on a traditional I/O stack, with a POSIX parallel file system. We present background concepts everyone could benefit from. Moreover, through the comprehensive study of publications from the most important conferences and journals in a five-year time window, we discuss the state of the art of I/O optimization approaches, access pattern extraction techniques, and performance modeling, in addition to general aspects of parallel I/O research. Through this approach, we aim at identifying the general characteristics of the field and the main current and future research topics.