high performance computing on graphics processing units: hgpu.org

The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data (SIMD) execution model. SIMD machines give application developers a tremendous computational power; however, the model also brings restrictions. In particular, processing elements (PEs) execute in lock-step, and may lose performance due to divergences caused by conditional branches. In face […]

GPGPU Computing using CUDA is rapidly gaining ground today. GPGPU has been brought to the masses through the ease of use of CUDA and ubiquity of graphics cards supporting the same. Although CUDA has a low learning curve for programmers familiar with standard programming languages like C, extracting optimum performance from it, through optimizations and […]

Massively-parallel graphics processors have the potential to offer high performance at low cost. However, at present such devices are largely inaccessible from higher-level languages such as Java. This work allows compilation from Java bytecode by making use of annotations to specify loops for parallel execution. Data copying to and from the GPU is handled automatically. […]

The aim of the project was to allow extraction and compilation of Java virtual machine bytecode for parallel execution on graphics cards, specifically the NVIDIA CUDA framework, by both explicit and automatic means. The compiler, which was produced, successfully extracts and compiles code from class files into CUDA C++ code, and outputs transformed classes that […]

With the advent of multi-core processors desktop computers have become multiprocessors requiring parallel programming to be utilized efficiently. Efficient and portable parallel programming of future multi-core processors and GPUs is one of today’s most important challenges within computer science. Okuda Laboratory at The University of Tokyo in Japan focuses on solving engineering challenges with parallel […]

Design of data structures for high performance computing (HPC) is one of the principal challenges facing researchers looking to utilize heterogeneous computing machinery. Heterogeneous systems derive cost, power, and speed efficiency by being composed of the appropriate hardware for the task. Yet, each type of processor requires a specific organization of the application state in […]

Open Computing Language (OpenCL) is fast becoming the standard for heterogeneous parallel computing. It is designed to run on CPUs, GPUs, and other accelerator architectures. By implementing a real world application, a solar radiation model component widely used in climate and weather models, we show that OpenCL multi-threaded programming and execution model can dramatically increase […]

Current GPU programming systems automatically distribute the work on all GPU processors based on a set of fixed assumptions, e.g. that all tasks are independent from each other. We show that automatic distribution limits algorithmic design, and demonstrate that manual work distribution hardly adds any overhead. Our Scan+algorithm is an improved scan relying on manual […]

We present the GPU calculation with the common unified device architecture (CUDA) for the Wolff single-cluster algorithm of the Ising model. Proposing an algorithm for a quasi-block synchronization, we realize the Wolff single-cluster Monte Carlo simulation with CUDA. We perform parallel computations for the newly added spins in the growing cluster. As a result, the […]

Cluster identification tasks occur in a multitude of contexts in physics and engineering such as, for instance, cluster algorithms for simulating spin models, percolation simulations, segmentation problems in image processing, or network analysis. While it has been shown that graphics processing units (GPUs) can result in speedups of two to three orders of magnitude as […]

Recent advances in neuroscientific understanding make parallel computing devices modeled after the human neocortex a plausible, attractive, fault-tolerant, and energye-fficient possibility. Such attributes have once again sparked an interest in creating learning algorithms that aspire to reverseengineer many of the abilities of the brain. In this paper we describe a GPGPU-accelerated extension to an intelligent […]

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU […]