UNLV College of Education Multicultural & Diversity Newsletter

A newsletter discussing a variety of topics dealing with multicultural topics and diversity in education

Continuously Measuring Critical Section Pressure with the Free-Lunch Profiler

International audienceToday, Java is regularly used to implement large multi-threaded server-class applications that use locks to protect access to shared data. However, understanding the impact of locks on the performance of a system is complex, and thus the use of locks can impede the progress of threads on con-figurations that were not anticipated by the developer, during specific phases of the execution. In this paper, we propose Free Lunch, a new lock profiler for Java application servers, specifically designed to identify, in-vivo, phases where the progress of the threads is impeded by a lock. Free Lunch is designed around a new metric, critical section pressure (CSP), which directly correlates the progress of the threads to each of the locks. Using Free Lunch, we have identified phases of high CSP, which were hidden with other lock pro-filers, in the distributed Cassandra NoSQL database and in several applications from the DaCapo 9.12, the SPECjvm-2008 and the SPECjbb2005 benchmark suites. Our evaluation of Free Lunch shows that its overhead is never greater than 6%, making it suitable for in-vivo use

UNLV College of Education Multicultural & Diversity Newsletter

A newsletter discussing a variety of topics dealing with multicultural topics and diversity in education

UNLV College of Education Multicultural & Diversity Newsletter

A newsletter discussing a variety of topics dealing with multicultural topics and diversity in education

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR