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155
HPC Benchmarking and Performance Evaluation With Realistic Applications
"... The goal of benchmarking and performance evaluation, as viewed in this paper, is to assess the performance and understand characteristics of HPC platforms and their important applications. An obvious use of the gained results is the search for machines that are best for a given purpose. Equally impo ..."
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The goal of benchmarking and performance evaluation, as viewed in this paper, is to assess the performance and understand characteristics of HPC platforms and their important applications. An obvious use of the gained results is the search for machines that are best for a given purpose. Equally
Improving HPC Application Performance in Cloud through Dynamic Load Balancing
"... Abstract—Driven by the benefits of elasticity and pay-as-you-go model, cloud computing is emerging as an attractive alternative and addition to in-house clusters and supercomputers for some High Performance Computing (HPC) applications. However, poor interconnect performance, heterogeneous and dynam ..."
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Cited by 1 (0 self)
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distribution to VMs, our load balancer adapts to the dynamic variations in cloud resources. Through experimental evaluation on a private cloud with 64 VMs using benchmarks and a real science application, we demonstrate performance benefits up to 45%. Finally, we analyze the effect of load balancing frequency
A Practical Method for Estimating Performance Degradation on Multicore Processors, and its Application to HPC Workloads
"... Abstract—When multiple threads or processes run on a multicore CPU they compete for shared resources, such as caches and memory controllers, and can suffer performance degradation as high as 200%. We design and evaluate a new machine learning model that estimates this degradation online, on previous ..."
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Cited by 6 (1 self)
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learning to this problem domain, and we report on our experience reaping the advantages of machine learning while navigating around its limitations. We demonstrate how the model can be used to improve performance fidelity and save energy for HPC workloads. I.
ACIC: Automatic Cloud I/O Configurator for HPC Applications
"... The cloud has become a promising alternative to tradi-tional HPC centers or in-house clusters. This new environ-ment highlights the I/O bottleneck problem, typically with top-of-the-line compute instances but sub-par communica-tion and I/O facilities. It has been observed that changing cloud I/O sys ..."
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/O system configurations leads to significant varia-tion in the performance and cost efficiency of I/O intensive HPC applications. However, storage system configuration is tedious and error-prone to do manually, even for experts. This paper proposes ACIC, which takes a given applica-tion running on a given
Improve Computer-Aided Diagnosis with Machine Learning Techniques Using Undiagnosed Samples
"... In computer-aided diagnosis, machine learning techniques have been widely applied to learn hypothesis from diagnosed samples in order to assist the medical experts in making diagnosis. To learn a well-performed hypothesis, a large amount of diagnosed samples are required. Although the samples can b ..."
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Cited by 30 (5 self)
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In computer-aided diagnosis, machine learning techniques have been widely applied to learn hypothesis from diagnosed samples in order to assist the medical experts in making diagnosis. To learn a well-performed hypothesis, a large amount of diagnosed samples are required. Although the samples can
Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine
"... Abstract. Efficient management of chronic diseases is critical in mod-ern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demograp ..."
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demographics, diagnoses, pathologies and treatments, and ex-tract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann ma-chine (MV.RBM), as it seamlessly integrates multiple data types for each patient aggregated over time
Structured Comparative Analysis of Systems Logs to Diagnose Performance Problems
, 2011
"... Diagnosis and correction of performance issues in modern, large-scale distributed systems can be a daunting task, since a single developer is unlikely to be familiar with the entire system and it is hard to characterize the behavior of a software system without completely understanding its internal ..."
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Cited by 23 (0 self)
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, given two sets of logs, one with good and one with bad performance, DISTALYZER uses machine learning techniques to compare system behaviors extracted from the logs and automatically infer the strongest associations between system components and performance. The tool outputs a set of inter-related event
Feature selection to diagnose a business crisis by using a real GAbased support vector machine: An empirical study”, Expert Systems with Applications
"... Abstract This research is aimed at establishing the diagnosis models for business crises through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. After finishing the training processes, the proposed GA-SVM can re ..."
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Cited by 3 (0 self)
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Abstract This research is aimed at establishing the diagnosis models for business crises through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. After finishing the training processes, the proposed GA-SVM can
Predictive Machine Learning Techniques for Breast Cancer Detection
"... Abstract-Machine learning is a branch of artificial intelligence that incorporate a variety of statistical, probabilistic and optimization techniques that allow computers to “learn ” from past examples and to detect hard-to-diagnosed patterns from massive, noisy or complex data sets. This features a ..."
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are particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. Machine learning techniques like support vector machine, Bayesian belief network, Artificial neural network are frequently used in cancer diagnosis and detection. More recently machine
A short review paper on Face detection using Machine learning
"... Abstract — Detecting faces in images is the first step of any face application such as face recognition,face localization and face expression. The performance of face detection systems directly affect on correct operating of mentioned applications. Because faces are non-rigid and have high variation ..."
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variation in scale,color,pose and lighting condition,designing an automatic system to overcome all mentioned problems is difficult. Machine learning has been shown that is one of the most successful tools to build high performance face detection systems. Due to huge number of studies in this area
Results 1 - 10
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155
