1068

Posts

Oct, 27

Getting Started with GPU Programming

This tutorial describes a step-by-step procedure for programming a Macintosh Nvidia GPU. General scientific programmers with some C knowledge can get started in parallel processing application development with relative ease.
Oct, 27

Monte Carlo integration on GPU

We use a graphics processing unit (GPU) for fast computations of Monte Carlo integrations. Two widely used Monte Carlo integration programs, VEGAS and BASES, are parallelized on GPU. By using $W^+$ plus multi-gluon production processes at LHC, we test integrated cross sections and execution time for programs in FORTRAN and C on CPU and those […]
Oct, 27

Computational advances in gravitational microlensing: a comparison of CPU, GPU, and parallel, large data codes

To assess how future progress in gravitational microlensing computation at high optical depth will rely on both hardware and software solutions, we compare a direct inverse ray-shooting code implemented on a graphics processing unit (GPU) with both a widely-used hierarchical tree code on a single-core CPU, and a recent implementation of a parallel tree code […]
Oct, 27

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model [T. Preis et al., Journal of Chemical Physics 228 (2009) 4468–4477] in order to overcome the memory limitations of a single GPU which enables us […]
Oct, 27

Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

GPUs have recently attracted the attention of many application developers as commodity data-parallel coprocessors. The newest generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. This opportunity should redirect efforts in GPGPU research from ad hoc porting of applications to establishing principles […]
Oct, 27

A GPU based real-time software correlation system for the Murchison Widefield Array prototype

Modern graphics processing units (GPUs) are inexpensive commodity hardware that offer Tflop/s theoretical computing capacity. GPUs are well suited to many compute-intensive tasks including digital signal processing. We describe the implementation and performance of a GPU-based digital correlator for radio astronomy. The correlator is implemented using the NVIDIA CUDA development environment. We evaluate three design […]
Oct, 27

Using graphics devices in reverse: GPU-based Image Processing and Computer Vision

Graphics and vision are approximate inverses of each other: ordinarily graphics processing units (GPUs) are used to convert ldquonumbers into picturesrdquo (i.e. computer graphics). In this paper, we discuss the use of GPUs in approximately the reverse way: to assist in ldquoconverting pictures into numbersrdquo (i.e. computer vision). For graphical operations, GPUs currently provide many […]
Oct, 27

Stackless KD-Tree Traversal for High Performance GPU Ray Tracing

Abstract Significant advances have been achieved for realtime ray tracing recently, but realtime performance for complex scenes still requires large computational resources not yet available from the CPUs in standard PCs. Incidentally, most of these PCs also contain modern GPUs that do offer much larger raw compute power. However, limitations in the programming and memory […]
Oct, 27

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing

High-performance scientific 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 […]
Oct, 27

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these […]
Oct, 27

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

This article presents uniform B-spline interpolation, completely contained on the graphics processing unit (GPU). This implies that the CPU does not need to compute any lookup tables or B-spline basis functions. The cubic interpolation can be decomposed into several linear interpolations [Sigg and Hadwiger 05], which are hard-wired on the GPU and therefore very fast. […]
Oct, 27

GPU-based ultrafast IMRT plan optimization

The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient’s geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) […]
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