Advanced Architectures for Astrophysical Supercomputing

Astronomers have come to rely on the increasing performance of computers to reduce, analyze, simulate and visualize their data. In this environment, faster computation can mean more science outcomes or the opening up of new parameter spaces for investigation. If we are to avoid major issues when implementing codes on advanced architectures, it is important that we have a solid understanding of our algorithms. A recent addition to the high-performance computing scene that highlights this point is the graphics processing unit (GPU). The hardware originally designed for speeding-up graphics rendering in video games is now achieving speed-ups of $O(100\times)$ in general-purpose computation -- performance that cannot be ignored. We are using a generalized approach, based on the analysis of astronomy algorithms, to identify the optimal problem-types and techniques for taking advantage of both current GPU hardware and future developments in computing architectures.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8 2009, Sapporo, Japan (ASP Conf. Series

Computations in Physics Using Graphic Card Procesor

Tato práce se zabývá problematikou negrafických výpočtů pomocí procesorů grafických karet. Poskytuje základní informace o grafickém hardwaru. Zároveň popisuje CUDA a CTM programovací rozhraní, které jsou určeny speciálně pro tyto výpočty a uvádí i jiné způsoby řešení. Jsou zde rovněž uvedeny možnosti jejich využití a pár praktických příkladů výpočtů.This work deals with issue of general purpose computation on graphics processing units. It provides basic information about the graphics hardware. It also describes CUDA and CTM programming interface, that are intended specially for these calculations and features and alternative methods solving. There are as well mentioned possibilities their usage and several practical instances calculations.

OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF

The actor model of computation has been designed for a seamless support of concurrency and distribution. However, it remains unspecific about data parallel program flows, while available processing power of modern many core hardware such as graphics processing units (GPUs) or coprocessors increases the relevance of data parallelism for general-purpose computation. In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework (CAF). This offers a high level interface for accessing any OpenCL device without leaving the actor paradigm. The new type of actor is integrated into the runtime environment of CAF and gives rise to transparent message passing in distributed systems on heterogeneous hardware. Following the actor logic in CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence operate in a multi-stage fashion on data resident at the GPU. Developers are thus enabled to build complex data parallel programs from primitives without leaving the actor paradigm, nor sacrificing performance. Our evaluations on commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear scaling behavior when offloading larger workloads. For sub-second duties, the efficiency of offloading was found to largely differ between devices. Moreover, our findings indicate a negligible overhead over programming with the native OpenCL API.Comment: 28 page

Graphics Processing Unit Assisted Thermographic Compositing

Objective Develop a software application utilizing high performance computing techniques, including general purpose graphics processing units (GPGPUs), for the analysis and visualization of large thermographic data sets. Over the past several years, an increasing effort among scientists and engineers to utilize graphics processing units (GPUs) in a more general purpose fashion is allowing for previously unobtainable levels of computation by individual workstations. As data sets grow, the methods to work them grow at an equal, and often greater, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU which yield significant increases in performance. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Image processing is one area were GPUs are being used to greatly increase the performance of certain analysis and visualization techniques

Higgs Boson equation in de Sitter spacetime: Numerical investigation of bubbles using GPUs

The Higgs field, along with its corresponding boson, represent a milestone for modern day particle physics. In this work we consider the Higgs boson equation in de Sitter spacetime. Previous work by K. Yagdjian [23] has formulated sufficient conditions for the existence of the zeros of global solutions in the interior of their supports. In searching for such solutions, we turn to heterogeneous parallel computing, which allows for faster computation through graphical processing units (GPUs). Armed with general-purpose computation on graphics hardware (GPGPU) techniques and explicit numerical schemes, we approximate solutions of the equation for the Higgs boson along with the creation, growth, and interaction of the zeros, or bubbles

An OpenGL backend for Halide

Thesis (M. Eng.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 37).High performance image processing requires not only an efficient underlying algorithm but also an implementation tailored to maximally exploit the available hardware resources. In practice, this requires low-level optimization, platform-specific instructions, and, when available, the use of special purpose hardware such as GPU. Halide is a domain-specific programming language targeted at image processing applications. Its programming model decouples an algorithm from the details of its execution, vastly simplifying development and optimization. We present an OpenGL backend for the Halide compiler, which enables Halide programs to run GPU computation on devices that support the OpenGL API. In particular, this paves the way for GPU computation on mobile devices using OpenGL ES. In doing so, we demonstrate how a general image processing framework can be built upon functionality designed for 3D graphics applications.by Nicholas J. Chornay.M.Eng

General Purpose Programming on Modern Graphics Hardware

I start with a brief introduction to the graphics processing unit (GPU) as well as general-purpose computation on modern graphics hardware (GPGPU). Next, I explore the motivations for GPGPU programming, and the capabilities of modern GPUs (including advantages and disadvantages). Also, I give the background required for further exploring GPU programming, including the terminology used and the resources available. Finally, I include a comprehensive survey of previous and current GPGPU work, and end with a look at the future of GPU programming

Load-balanced rendering on a general-purpose tiled architecture

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 77-80).Commodity graphics hardware has become increasingly programmable over the last few years, but has been limited to a fixed resource allocation. These architectures handle some workloads well, others poorly; load-balancing to maximize graphics hardware performance has become a critical issue. I have designed a system that solves the load-balancing problem in real-time graphics by using compile-time resource allocation on general-purpose hardware. I implemented a flexible graphics pipeline on Raw, a tile-based multicore processor. The complete graphics pipeline is expressed using StreamIt, a high-level language based on the stream programming model. The StreamIt compiler automatically maps the stream computation onto the Raw architecture. The system is evaluated by comparing the performance of the flexible pipeline with a fixed allocation representative of commodity hardware on common rendering tasks. The benchmarks place workloads on different parts of the pipeline to determine the effectiveness of the load-balance. The flexible pipeline achieves up to twice the throughput of a static allocation.by Jiawen Chen.M.Eng