high performance computing on graphics processing units: hgpu.org

We present a general framework for GPU-based low-latency data transfer schemes that can be used for a variety of particle-mesh algorithms [8]. This framework allows to hide the latency of the data transfer between GPU-accelerated computing nodes by interleaving it with the kernel execution on the GPU. We discuss as an example the fully relativistic […]

SPRNG (Scalable Parallel Random Number Generators) is widely used in computational science applications, particularly on parallel systems. The LFG and LCG are two frequently used random number generators in this library. In this paper, LFG and LCG are implemented on GPUs in CUDA. As a library for providing random number to GPU scientific applications, GASPRNG […]

We address the problem of file matching by modifying the MD6 algorithm that is best suited to take advantage of GPU computing. MD6 is a cryptographic hash function that is tree-based and highly parallelizable. When the message M is available initially, the hashing operations can be initiated at different starting points within the message and […]

The Cauchy variant of the Reed-Solomon algorithm is implemented on accelerator platforms including GPGPU, FPGA, CellBE and ClearSpeed as well as on a x86 multi-core system. The sustained throughput performance and kernel rates are measured for a 5+3 Reed-Solomon schema. To compare the different technology platforms an efficiency is introduced and the platforms are categorized […]

This paper describes a flexible simulator for background Radio Frequency clutter developed at the Georgia Tech Research Institute, and how this simulation was accelerated with the use of nVidia GPUs using GPU VSIPL. The paper describes the mathematical basis for the simulation and how it can be used to simulate RF environments and scenarios; introduces […]

Given multiple images of the same scene, image registration is the process of determining the correct transformation to bring the images into a common coordinate system-i.e., how the images fit together. Featurebased registration applies a transformation function to the input images before performing the correlation step. The result of that transformation, also called feature extraction, […]

We speed up the tetrahedral interpolation step of a deformable image registration code called MORFEUS. We implement several versions of the interpolation code on a Fermi GPU (GTX480). Despite the irregularity of the code, we obtained kernel speedups of up to 24.6x, 33.7x and 62.4x on three real-life benchmarks. These numbers do not include the […]

The graphics processor units (GPUs) have evolved into high-performance co-processors that can be easily programmed with common high-level language such as C, Fortran and C++. Today’s GPUs greatly outpace CPUs in arithmetic performance and memory bandwidth, making them the ideal coprocessor to accelerate a variety of data parallel applications. Here, we shall describe the application […]

Power has emerged as a significant constraint to high performance systems. We propose modeling power-based performance (performance/watt) and clock-based performance for GPGPUs and FPGAs. Based on the modeling, we perform a case-study with mixed precision linear solvers for a Xilinx XC5VLX330T FPGA and NVIDIA Tesla C1060 GPU. In the case-study, the FPGA shows power- and […]

Double precision floating-point performance is critical for hardware acceleration technologies to be adopted by domain scientists. In this work we use the Hessenberg reduction to demonstrate the potential of FPGAs and GPUs for obtaining satisfactory double precision floating-point performance. Currently a Xeon (Nehalem) 2.26 GHz CPU can outperform Xilinx Virtex4LX200 by 3.6 folds. However, given […]

In this paper, we describe a GPU-accelerated implementation of a logic minimization heuristic based on the near minimal approach. This algorithm has three key kernel computations, and the current version of our implementation, we adapted one of these kernels for GPU execution. In this paper we report our results gained from using NVIDIA’s CUDA development […]

Continuum quantum Monte Carlo (QMC) has proved to be an invaluable tool for predicting the properties of matter from fundamental principles. By solving the manybody Schrodinger equation through a stochastic projection, it achieves greater accuracy than mean-field methods and better scalability than quantum chemical methods, enabling scientific discovery across a broad spectrum of disciplines. The […]