As part of the U.S. National Science Foundation (NSF) funded XD Metrics Service project, we are developing tools and techniques for the audit and analysis of High Performance Computing (HPC) and Cloud infrastructure. This includes a suite of tools for the analysis of HPC jobs, based on performance metrics collected from compute nodes. To date, we have developed two closely related utilities: XDMoD, which was designed to monitor usage and performance of NSF's innovative HPC resources (known as XSEDE), and Open XDMoD, which was designed to monitor usage and performance in academic, governmental or commercial cyberinfrastructures. Considerable effort has been made to continually improve XDMoD, in order to capture the most important aspects of modern research computing.One area in which XDMoD is lacking is in tracking workflows, which are broadly designated as containing the elements of data transfer/input and one to many computational steps. As data sets have become larger, data movement has become more time and resource intensive, and hence more important to characterize. In addition, multiple step workflows, in which one input spawns a complex series of processes, are becoming more common. Although XDMoD currently captures some of the information required to properly track complex workflows, there are clearly some key data that are missing. In this paper, we discuss the existing state of workflow monitoring, and suggest strategies to improve on the information captured.

MOTIVATION: The extension of mapped sequence tags is a common step in the analysis of single-end next-generation sequencing (NGS) data from protein localization and chromatin studies. The optimal extension can vary depending on experimental and technical conditions. Improper extension of sequence tags can obscure or mislead the interpretation of NGS results. We present an algorithm, ArchTEx (Architectural Tag Extender), which identifies the optimal extension of sequence tags based on the maximum correlation between forward and reverse tags and extracts and visualizes sites of interest using the predicted extension. AVAILABILITY AND IMPLEMENTATION: ArchTEx requires Java 1.6 or newer. Source code and the compiled program are freely available at http://sourceforge.net/projects/archtex/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

To achieve optimal performance, garbage-collected applications must balance the sizes of their heaps dynamically. Sizing the heap too small can reduce throughput by increasing the number of garbage collections that must be performed. Too large a heap, however, can cause the system to page and drag down the overall throughput. In today's multicore, multiprocessor machines, multiple garbage-collected applications may run simultaneously. As a result, each virtual machine (VM) must adjust its memory demands to reflect not only the behavior of the application it is running, but also the behavior of the peer applications running on the system.