high performance computing on graphics processing units: hgpu.org

At the Large Hadron Collider (LHC), the trigger systems for the detectors must be able to process a very large amount of data in a very limited amount of time, so that the nominal collision rate of 40 MHz can be reduced to a data rate that can be stored and processed in a reasonable […]

OpenACC compilers allow one to use Graphics Processing Units without having to write explicit CUDA codes. Programs can be modified incrementally using OpenMP like directives which causes the compiler to generate CUDA kernels to be run on the GPUs. In this article we look at the performance gain in lattice simulations with dynamical fermions using […]

Network traffic recorders are devices that record massive volumes of network traffic for security applications, like retrospective forensic investigations. When deployed over very high-speed networks, traffic recorders must process and store millions of packets per second. To enable interactive explorations of such large traffic archives, packet indexing mechanisms are required. Indexing packets at wire rates […]

We present Sailfish, an open source fluid simulation package implementing the lattice Boltzmann method (LBM) on modern Graphics Processing Units (GPUs) using CUDA/OpenCL. We take a novel approach to GPU code implementation and use run-time code generation techniques and a high level programming language (Python) to achieve state of the art performance, while allowing easy […]

Graphic Processing Units (GPU) are increasingly popular in the field of high-performance computing for their ability to provide computational power for massively parallel problems at a reduced cost. However, the programming model exposed by the GPGPU software development tools is often insufficient to achieve full performance, and a major rethinking of algorithmic choices is needed. […]

Graphic Processing Units (GPU) has been proved to be a promising platform to accelerate large size Fast Fourier Transform (FFT) computation. However, current GPU-based FFT implementation only uses GPU to compute, but employs CPU as a mere memory-transfer controller. The computation power in today’s high-performance CPU is wasted. In this project, a hybrid optimization framework […]

Over the years, R has been adopted as a major data analysis and mining tool in many domain fields. As Big Data overwhelms those fields, the computational needs and workload of existing R solutions increases significantly. With recent hardware and software developments, it is possible to enable massive parallelism with existing R solutions with little […]

Within the field of Computer Graphics, there have been two competing approaches to doing rendering, namely rasterisation and raytracing. Rasterisation became, and has been, the dominant of the two methods for realtime rendering for a long period of time. With recent developments in graphics hardware, however, raytracing is starting to gain popularity once again. At […]

BACKGROUND: RNA performs many diverse functions in the cell in addition to its role as a messenger of genetic information. These functions depend on its ability to fold to a unique three-dimensional structure determined by the sequence. The conformation of RNA is in part determined by its secondary structure, or the particular set of contacts […]

This paper presents preliminary performance comparisons of parallel applications developed natively for the Intel Xeon Phi accelerator using three different parallel programming environments and their associated runtime systems. We compare Intel OpenMP, Intel CilkPlus and XKaapi together on the same benchmark suite and we provide comparisons between an Intel Xeon Phi coprocessor and a Sandy […]

We report on our investigations into the viability of the ARM processor and the Intel Xeon Phi co-processor for scientific computing. We describe our experience porting software to these processors and running benchmarks using real physics applications to explore the potential of these processors for production physics processing.

The power of high performance computing (HPC) heavily depends on the ability to efficiently enhancing huge amounts of parallelism. Random numbers or pseudo random numbers are very important for the efficient implementation for stochastic algorithms. Multi-core CPU and many-core Graphic Processing Units (GPUs) are conductive accelerator to produce the countless random numbers. Nevertheless, GPU does […]