The IceCube collaboration relies on GPU compute for many of its needs, including ray tracing simulation and machine learning activities. GPUs are however still a relatively scarce commodity in the scientific resource provider community, so we expanded the available resource pool with GPUs provisioned from the commercial Cloud providers. The provisioned resources were fully integrated into the normal IceCube workload management system through the Open Science Grid (OSG) infrastructure and used CloudBank for budget management. The result was an approximate doubling of GPU wall hours used by IceCube over a period of 2 weeks, adding over 3.1 fp32 EFLOP hours for a price tag of about $58k. This paper describes the setup used and the operational experience.

Summary The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is a community of research scientists and institutions from around the Pacific Rim that work together to enable scientific expeditions in areas such as biodiversity distribution and lake ecology. PRAGMA's members collaborate on a testbed infrastructure, and over the past 4 years, the technology focus has shifted to cloud and software defined networking as enabling technologies. This paper describes the design of a web‐based cloud scheduler reservation system that enables users to easily run and manage virtual clusters for their science. Based on a client‐server architecture, the cloud scheduler is designed to require minimal installation and management effort for participating PRAGMA testbed sites. The PRAGMA cloud scheduler is built using a web‐based room reservation tool called Booked and leverages several PRAGMA technologies such as pragma_boot, Personal Cloud Controller, and virtual network overlays (eg, IPOP and ViNe). We discuss our implementation of the pilot cloud scheduler and then describe future work.

Summary Science gateways promote collaboration among researchers by providing them with access to community‐developed tools and data collections. The Cyberinfrastructure for Phylogenetic Research (CIPRES) science gateway is one of the most popular gateways, with approximately 3000 active users since 2012, and the user base is growing each year. While increasing the number of compute resources available to CIPRES would address their growth needs, it also introduces additional complexity as the likelihood of failure increases. In this paper, we analyze historical job data from CIPRES and combine it with historical software and services monitoring data to create a machine learning model to predict where a user's job will complete successfully on resources. At one operating point of our classifier, we are able to detect 50% of jobs that will fail with a false detection rate less than 5%. In 2014, accurately predicting 50% of CIPRES job failures and redirecting them to other resources would have resulted in 900K compute core hours saved, furthering phylogenetic research. These statistical models will also be used as a base to build a more generic automated monitoring analysis service for science gateways. Copyright © 2015 John Wiley & Sons, Ltd.

Virtual organizations (VOs), communities that enable coordinated resource sharing among multiple sites, are becoming more prevalent in the high-performance computing community. In order to promote cross-site resource usability, most VOs prepare service agreements that include a minimum set of common resource functionality, starting with a common software stack and evolving into more complicated service and interoperability agreements. VO service agreements are often difficult to verify and maintain, however, because the sites are dynamic and autonomous. Automated verification of service agreements is critical: manual and user tests are not practical on a large scale. The Inca test harness and reporting framework is a generic system for the automated testing, data collection, verification, and monitoring of service agreements. This paper describes Inca’s architecture, system impact, and performance. Inca is being used by the TeraGrid project to verify software installations, monitor service availability, and collect performance data.

Ensembles of distributed, heterogeneous resources, also known as computational grids, have emerged as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in heterogeneous, multiuser grid environments. We discuss the AppLeS project and outline our findings.

Tomography is a popular technique to reconstruct the three-dimensional structure of an object from a series of two-dimensional projections. Tomography is resource-intensive and deployment of a parallel implementation onto Computational Grid platforms has been studied in previous work. In this work, we address on-line execution of the application where computation is performed as data is collected from an on-line instrument. The goal is to compute incremental 3-D reconstructions that provide quasi-real-time feedback to the user.We model on-line parallel tomography as a tunable application: trade-offs between resolution of the reconstruction and frequency of feedback can be used to accommodate various resource availabilities. We demonstrate that application scheduling/tuning can be framed as multiple constrained optimization problems and evaluate our methodology in simulation. Our results show that prediction of dynamic network performance is key to efficient scheduling and that tunability allows for production runs of on-line parallel tomography in Computational Grid environments.