PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

A wrapper generation tool for the creation of scriptable scientific applications

Journal ArticleIn recent years, there has been considerable interest in the use of scripting languages as a mechanism for controlling and developing scientific software. Scripting languages allow scientific applications to be encapsulated in an interpreted environment similar to that found in commercial scientific packages such as MATLAB, Mathematica, and IDL. This improves the usability of scientific software by providing a powerful meachanism for specifyling and controlling cimplex problems as well as giving users an interactive and exploratory problem solving environment. Scripting languages also provide a framework for building and integrating software components that allows tools be used in a more efficient manner. This streamlines the problem solving process and enable scientists to be more productive

Efficient integration of software components for scientific simulations

Abstract unavailable please refer to PD

Beyond XSPEC: Towards Highly Configurable Analysis

We present a quantitative comparison between software features of the defacto standard X-ray spectral analysis tool, XSPEC, and ISIS, the Interactive Spectral Interpretation System. Our emphasis is on customized analysis, with ISIS offered as a strong example of configurable software. While noting that XSPEC has been of immense value to astronomers, and that its scientific core is moderately extensible--most commonly via the inclusion of user contributed "local models"--we identify a series of limitations with its use beyond conventional spectral modeling. We argue that from the viewpoint of the astronomical user, the XSPEC internal structure presents a Black Box Problem, with many of its important features hidden from the top-level interface, thus discouraging user customization. Drawing from examples in custom modeling, numerical analysis, parallel computation, visualization, data management, and automated code generation, we show how a numerically scriptable, modular, and extensible analysis platform such as ISIS facilitates many forms of advanced astrophysical inquiry.Comment: Accepted by PASP, for July 2008 (15 pages

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac