Summary High performance computing (HPC) has led to remarkable advances in science and engineering and has become an indispensable tool for research. Unfortunately, HPC use and adoption by many researchers is often hindered by the complex way these resources are accessed. Indeed, while the web has become the dominant access mechanism for remote computing services in virtually every computing area, HPC is a notable exception. Open OnDemand is an open source project negating this trend by providing web‐based access to HPC resources ( https://openondemand.org ). This article describes the challenges to adoption and other lessons learned over the 3‐year project that may be relevant to other science gateway projects. We end with a description of future plans the project team has during the Open OnDemand 2.0 project including specific developments in machine learning and GPU monitoring.

The XD Metrics on Demand (XDMoD) tool was designed to collect and present detailed utilization data from the XSEDE network of supercomputers. XDMoD displays a wealth of information on computational resources. Its metrics report accounting and performance figures for computational jobs, including resources consumed, wait times, and quality of service. XDMoD can aggregate this information over an ensemble of jobs, or report it for individual jobs. Its web-based interface supports charting, exploration, and reporting for any time range, across all computing resources. Over the last eight years, we have open-sourced XDMoD, generalizing and packaging it to produce Open XDMoD. Open XDMoD may be installed, configured, and run on any computing cluster, and we have extended and refined it to include support for storage, cloud resources, science gateways, and more. In this paper, we describe the federation of XDMoD instances. This new functionality makes it possible to associate and monitor networks of related resources, using the full power of XDMoD to aggregate and display data from disparate XDMoD installations. Charting and reports from XDMoD federations can encompass anything ranging from a single resource to the entire network. The federation capability is flexible; federated resources can be coupled loosely or tightly, and a variety of different information sharing and authentication paradigms are supported. We also highlight new functionality that incorporates storage and cloud facilities into XDMoD federations. Federation will enable XDMoD users to monitor and manage ever more diverse computing resources.

BACKGROUND: MicroRNAs (miRNAs) regulate gene expression by targeting complementary mRNAs for destruction or translational repression. Aberrant expression of miRNAs has been associated with various diseases including cancer, thus making them interesting therapeutic targets. The composite of secondary structural elements that comprise miRNAs could aid the design of small molecules that modulate their function. RESULTS: We analyzed the secondary structural elements, or motifs, present in all human miRNA hairpin precursors and compared them to highly expressed human RNAs with known structures and other RNAs from various organisms. Amongst human miRNAs, there are 3808 are unique motifs, many residing in processing sites. Further, we identified motifs in miRNAs that are not present in other highly expressed human RNAs, desirable targets for small molecules. MiRNA motifs were incorporated into a searchable database that is freely available. We also analyzed the most frequently occurring bulges and internal loops for each RNA class and found that the smallest loops possible prevail. However, the distribution of loops and the preferred closing base pairs were unique to each class. CONCLUSIONS: Collectively, we have completed a broad survey of motifs found in human miRNA precursors, highly expressed human RNAs, and RNAs from other organisms. Interestingly, unique motifs were identified in human miRNA processing sites, binding to which could inhibit miRNA maturation and hence function.

Machine learning techniques were applied to job accounting and performance data for application classification. Job data were accumulated using the XDMoD monitoring technology named SUPReMM, they consist of job accounting information, application information from Lariat/XALT, and job performance data from TACC_Stats. The results clearly demonstrate that community applications have characteristic signatures which can be exploited for job classification. We conclude that machine learning can assist in classifying jobs of unknown application, in characterizing the job mixture, and in harnessing the variation in node and time dependence for further analysis.

We present a framework that compares the publication impact based on a comprehensive peer analysis of papers produced by scientists using XSEDE and NCAR resources. The analysis is introducing a percentile ranking based approach of citations of the XSEDE and NCAR papers compared to peer publications in the same journal that do not use these resources. This analysis is unique in that it evaluates the impact of the two facilities by comparing the reported publications from them to their peers from within the same journal issue. From this analysis, we can see that papers that utilize XSEDE and NCAR resources are cited statistically significantly more often. Hence we find that reported publications indicate that XSEDE and NCAR resources exert a strong positive impact on scientific research.

Summary Application kernels are computationally lightweight benchmarks or applications run repeatedly on high performance computing (HPC) clusters in order to track the Quality of Service (QoS) provided to the users. They have been successful in detecting a variety of hardware and software issues, some severe, that have subsequently been corrected, resulting in improved system performance and throughput. In this work, the application kernels performance monitoring module of eXtreme Data Metrics on Demand (XDMoD) is described. Through the XDMoD framework, the application kernels have been run repetitively on the Texas Advanced Computing Center's Stampede and Lonestar4 clusters for a total of over 14,000 jobs. This provides a body of data on the HPC clusters operation that can be used to statistically analyze how the application performance, as measured by metrics such as execution time and communication bandwidth, is affected by the cluster's workload. We discuss metric distributions, carry out regression and correlation analyses, and use a PCA study to describe the variance and relate the variance to factors such as the spatial distribution of the application in the cluster. Ultimately, these types of analyses can be used to improve the application kernel mechanism, which in turn results in improved QoS of the HPC infrastructure that is delivered to the end users. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARY The important role high‐performance computing (HPC) resources play in science and engineering research, coupled with its high cost (capital, power and manpower), short life and oversubscription, requires us to optimize its usage – an outcome that is only possible if adequate analytical data are collected and used to drive systems management at different granularities – job, application, user and system. This paper presents a method for comprehensive job, application and system‐level resource use measurement, and analysis and its implementation. The steps in the method are system‐wide collection of comprehensive resource use and performance statistics at the job and node levels in a uniform format across all resources, mapping and storage of the resultant job‐wise data to a relational database, which enables further implementation and transformation of the data to the formats required by specific statistical and analytical algorithms. Analyses can be carried out at different levels of granularity: job, user, application or system‐wide. Measurements are based on a new lightweight job‐centric measurement tool ‘TACC_Stats’, which gathers a comprehensive set of resource use metrics on all compute nodes and data logged by the system scheduler. The data mapping and analysis tools are an extension of the XDMoD project. The method is illustrated with analyses of resource use for the Texas Advanced Computing Center's Lonestar4, Ranger and Stampede supercomputers and the HPC cluster at the Center for Computational Research. The illustrations are focused on resource use at the system, job and application levels and reveal many interesting insights into system usage patterns and also anomalous behavior due to failure/misuse. The method can be applied to any system that runs the TACC_Stats measurement tool and a tool to extract job execution environment data from the system scheduler. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents a tool chain, based on the open source tool TACC_Stats, for systematic and comprehensive job level resource use measurement for large cluster computers, and its incorporation into XDMoD, a reporting and analytics framework for resource management that targets meeting the information needs of users, application developers, systems administrators, systems management and funding managers. Accounting, scheduler and event logs are integrated with system performance data from TACC_Stats. TACC_Stats periodically records resource use including many hardware counters for each job running on each node. Furthermore, system level metrics are obtained through aggregation of the node (job) level data. Analysis of this data generates many types of standard and custom reports and even a limited predictive capability that has not previously been available for open-source, Linux-based software systems. This paper presents case studies of information that can be applied for effective resource management. We believe this system to be the first fully comprehensive system for supporting the information needs of all stakeholders in open-source software based HPC systems.

SUMMARY This paper describes XSEDE Metrics on Demand, a comprehensive auditing framework for use by high‐performance computing centers, which provides metrics regarding resource utilization, resource performance, and impact on scholarship and research. This role‐based framework is designed to meet the following objectives: (1) provide the user community with a tool to manage their allocations and optimize their resource utilization; (2) provide operational staff with the ability to monitor and tune resource performance; (3) provide management with a tool to monitor utilization, user base, and performance of resources; and (4) provide metrics to help measure scientific impact. Although initially focused on the XSEDE program, XSEDE Metrics on Demand can be adapted to any high‐performance computing environment. The framework includes a computationally lightweight application kernel auditing system that utilizes performance kernels to measure overall system performance. This allows continuous resource auditing to measure all aspects of system performance including filesystem performance, processor and memory performance, and network latency and bandwidth. Metrics that focus on scientific impact, such as publications, citations and external funding, will be included to help quantify the important role high‐performance computing centers play in advancing research and scholarship. Copyright © 2012 John Wiley & Sons, Ltd.

Microarray-based comparative genomic hybridization (aCGH) offers an increasingly fine-grained method for detecting copy number variations in DNA. These copy number variations can directly influence the expression of the proteins that are encoded in the genes in question. A useful analysis of the data produced from these microarray experiments is pairwise correlation. However, the high resolution of today's microarray technology requires that supercomputing computation and storage resources be leveraged in order to perform this analysis. This application is an exemplar of the class of data intensive problems which require high-throughput I/O in order to be tractable. Although the performance of these types of applications on a cluster can be improved by parallelization, storage hardware and network limitations restrict the scalability of an I/O-bound application such as this. The Hadoop software framework is designed to enable data-intensive applications on cluster architectures, and offers significantly better scalability due to its distributed file system. However, specialized architecture adhering to the Active Disk paradigm, in which compute power is placed close to the disk instead of across a network, can further improve performance. The Netezza Corporation's database systems are designed around the Active Disk approach, and offer tremendous gains in implementing this application over the traditional cluster architecture. We present methods and performance analyses of several implementations of this application: on a cluster, on a cluster with a parallel file system, with Hadoop on a cluster, and using a Netezza data warehouse appliance. Our results offer benchmarks for the performance of data intensive applications within these distributed computing paradigms.

Virtualization is a common strategy for improving the utilization of existing computing resources, particularly within data centers. However, its use for high performance computing (HPC) applications is currently limited despite its potential for both improving resource utilization as well as providing resource guarantees to its users. This paper systematically evaluates various VMs for computationally intensive HPC applications using various standard benchmarks. Using VMWare Server, xen, and OpenVZ we examine the suitability of full virtualization, paravirtualization, and operating system-level virtualization in terms of network utilization SMP performance, file system performance, and MPI scalability. We show that the operating system-level virtualization provided by OpenVZ provides the best overall performance, particularly for MPI scalability.

In this paper, we present cyberinfrastructure and grid computing efforts in New York State. In particular, we focus on fundamental efforts in Binghamton and Buffalo, including the design, development, and deployment of the New York State Grid, as well as a grass-roots New York State Initiative.

Bioinformatics studies of transcriptional regulation in the metazoa are significantly hindered by the absence of readily available data on large numbers of transcriptional cis-regulatory modules (CRMs). Even the richly annotated Drosophila melanogaster genome lacks extensive CRM information. We therefore present here a database of Drosophila CRMs curated from the literature complete with both DNA sequence and a searchable description of the gene expression pattern regulated by each CRM. This resource should greatly facilitate the development of computational approaches to CRM discovery as well as bioinformatics analyses of regulatory sequence properties and evolution.

Developing and maintaining a large software package is a complex task. Decisions are made early in the design process that affect i) the ability of a user to effectively exploit the package and ii) the ability of a software engineer to maintain it. This case study discusses issues in software development and maintainability of a scientific package called SnB, which is used to determine molecular crystal structures. The design of the user interface is discussed along with important software engineering concepts, including modular programming, data encapsulation, and internal code documentation. Fortran is a language that is still widely used in the scientific community. Issues concerning the integration of Fortran into a modern scientific application with a C-based user interface are also discussed. Scientific applications benefit from being available on a wide variety of platforms. Due to demand, SnB is available on a variety of sequential and parallel platforms. Methods used in the design of SnB for such portability are presented including POSIX compliance, automatic configuration scripts, and parallel programming techniques.