BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery

We present a novel computational framework that connects Blue Waters, the NSF-supported, leadership-class supercomputer operated by NCSA, to the Laser Interferometer Gravitational-Wave Observatory (LIGO) Data Grid via Open Science Grid technology. To enable this computational infrastructure, we configured, for the first time, a LIGO Data Grid Tier-1 Center that can submit heterogeneous LIGO workflows using Open Science Grid facilities. In order to enable a seamless connection between the LIGO Data Grid and Blue Waters via Open Science Grid, we utilize Shifter to containerize LIGO's workflow software. This work represents the first time Open Science Grid, Shifter, and Blue Waters are unified to tackle a scientific problem and, in particular, it is the first time a framework of this nature is used in the context of large scale gravitational wave data analysis. This new framework has been used in the last several weeks of LIGO's second discovery campaign to run the most computationally demanding gravitational wave search workflows on Blue Waters, and accelerate discovery in the emergent field of gravitational wave astrophysics. We discuss the implications of this novel framework for a wider ecosystem of Higher Performance Computing users.Comment: 10 pages, 10 figures. Accepted as a Full Research Paper to the 13th IEEE International Conference on eScienc

Secure Integration of Desktop Grids and Compute Clusters Based on Virtualization and Meta-Scheduling

Reducing the cost for business or scientific computations, is a commonly expressed goal in today’s companies. Using the available computers of local employees or the outsourcing of such computations are two obvious solutions to save money for additional hardware. Both possibilities exhibit security related disadvantages, since the deployed software and data can be copied or tampered if appropriate countermeasures are not taken. In this paper, an approach is presented to let a local desktop machines and remote cluster resources be securely combined into a singel Grid environment. Solutions to several problems in the areas of secure virtual networks, meta-scheduling and accessing cluster schedulers from desktop Grids are proposed

HIL: designing an exokernel for the data center

We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.Partial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation awards 1347525 and 1149232 as well as the several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or

Data production models for the CDF experiment

The data production for the CDF experiment is conducted on a large Linux PC farm designed to meet the needs of data collection at a maximum rate of 40 MByte/sec. We present two data production models that exploits advances in computing and communication technology. The first production farm is a centralized system that has achieved a stable data processing rate of approximately 2 TByte per day. The recently upgraded farm is migrated to the SAM (Sequential Access to data via Metadata) data handling system. The software and hardware of the CDF production farms has been successful in providing large computing and data throughput capacity to the experiment.Comment: 8 pages, 9 figures; presented at HPC Asia2005, Beijing, China, Nov 30 - Dec 3, 200

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

A batch scheduler with high level components

In this article we present the design choices and the evaluation of a batch scheduler for large clusters, named OAR. This batch scheduler is based upon an original design that emphasizes on low software complexity by using high level tools. The global architecture is built upon the scripting language Perl and the relational database engine Mysql. The goal of the project OAR is to prove that it is possible today to build a complex system for ressource management using such tools without sacrificing efficiency and scalability. Currently, our system offers most of the important features implemented by other batch schedulers such as priority scheduling (by queues), reservations, backfilling and some global computing support. Despite the use of high level tools, our experiments show that our system has performances close to other systems. Furthermore, OAR is currently exploited for the management of 700 nodes (a metropolitan GRID) and has shown good efficiency and robustness