- Redmond WA, US Yutaka Suzue - Issaquah WA, US Johnson T. Apacible - Mercer Island WA, US Karthik Kalyanaraman - Redmond WA, US Olatunji Ruwase - Bellevue WA, US Yuxiong He - Bellevue WA, US Feng Yan - Williamsburg VA, US
International Classification:
G06N 3/08 H04L 29/08
Abstract:
A performance investigation tool (PIT) is described herein for investigating the performance of a distributed processing system (DPS). The PIT operates by first receiving input information that describes a graph processing task to be executed using a plurality of computing units. The PIT then determines, based on the input information, at least one time-based performance measure that describes the performance of a DPS that is capable of performing the graphical task. More specifically, the PIT can operate in a manual mode to explore the behavior of a specified DPS, or in an automatic mode to find an optimal DPS from within a search space of candidate DPSs. A configuration system may then be used to construct a selected DPS, using the plurality of computing units. In one case, the graph processing task involves training a deep neural network model having a plurality of layers.
University of Nevada Reno
Assistant Professor
College of William and Mary May 1, 2010 - Jun 2016
Research Assistant
Microsoft Jun 2014 - Aug 2015
Research Intern
Hewlett-Packard Jun 2013 - May 2014
Research Associate
College of William and Mary Aug 2009 - Apr 2010
Teaching Assistant
Education:
William & Mary 2011 - 2014
Doctorates, Doctor of Philosophy, Computer Science, Philosophy
William & Mary 2009 - 2011
Master of Science, Masters, Computer Science
William & Mary 2010
Northeastern University 2004 - 2008
Bachelors, Bachelor of Science, Computer Science
Skills:
Big Data Analytics Data Analysis Cloud Computing Distributed Systems Deep Learning Mapreduce Hadoop Apache Spark Heterogeneous Computing Predictive Modeling Performance Engineering Performance Evaluation of Systems Resource Management Data Science Machine Learning Data Mining Benchmarking Power Management Data Center High Performance Computing C++ C Matlab Perl Mathematical Modeling Modeling Storage Systems Java Sysstat Openmp Simulations Latex Sql Spec Linux
Interests:
Consistency Priority Scheduling Resources Allocation and Consolidation Queuing Theory Data Analysis Resource Management Power Management Time Series Analysis and Prediction Storage Systems Deep Learning Hadoop Performance Modeling and Evaluation Power Savings Tiered Storage Systems Big Data Performance Evaluation Workload Characterization Load Balancing Scheduling Workload Interleaving Cloud Computing Mapreduce
No matter what you think about the new line, high-speed rail is extremely convenient, said Feng Yan, assistant professor at the Communication University of China in Beijing who took the bullet train from Shenzhen to Hong Kong.
Date: Sep 23, 2018
Category: Headlines
Source: Google
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