With clouds becoming a standard target for deploying applications, it is more important than ever to be able to seamlessly utilize resources and services from multiple providers. Proprietary vendor APIs make this challenging and lead to conditional code being written to accommodate various API differences, requiring application authors to deal with these complexities and to test their applications against each supported cloud. In this paper, we describe an open source Python library called CloudBridge that provides a simple, uniform, and extensible API for multiple clouds. The library defines a standard ‘contract’ that all supported providers must implement, and an extensive suite of conformance tests to ensure that any exposed behavior is uniform across cloud providers, thus allowing applications to confidently utilize any of the supported clouds without any cloud-specific code or testing.
Optimization and design of nuclear fusion devices is a complex task with large computational requirements. The complexity is defined by the number of parameters involved in every single possible optimization function that focuses on the different aspects of plasma confinement. This paper presents a possible optimization of an existing nuclear fusion device. The optimization process is carried out by a parallel algorithm specifically designed to work with large scale problems. While the focus of the paper is fusion, the approach used can be applied to any other large scale problem. We have run our experiments on an HPC cluster. The results show the validity of our approach and how complex scientific problems can benefit from the outcomes of this work.
Historically, large scale computing and interactivity have been at odds. This is a particularly sore spot for data analytics applications, which are typically interactive in nature. To help address this problem, we introduce a new client/server framework for the R language. This framework allows the R programmer to remotely control anywhere from one to thousands of batch servers running as cooperating instances of R. And all of this is done from the user's local R session. Additionally, no specialized software environment is needed; the framework is a series of R packages, available from CRAN. The communication between client and server(s) is handled by the well-known ZeroMQ library. To handle computations, we use the established pbdR packages for large scale distributed computing. These packages utilize HPC standards like MPI and ScaLAPACK to handle complex, tightly-coupled computations on large datasets. In this paper, we outline the client/server architecture, discuss the pros and cons to this approach, and provide several example workflows which bring
