high performance computing on graphics processing units: hgpu.org

In this paper CUDA (Compute Unified Device Architecture) programming and OpenMP (Open Multi-Processing) are used for the GPU (Graphics Processing Unit) and CPU (Central Processing Unit) parallel computation of material damage. The material damage is evaluated by a multilevel finite element analysis within material domains reconstructed from a high-resolution micro-focus X-ray computed tomography system. An […]

Sequence alignment is a task that calculates the degree of similarity between two sequences. Given a query sequence, finding a database sequence which is most similar to the query by sequence alignment is the first step in bioinformatics research. The first sequence alignment algorithm was proposed by Needle-man and Wunsch. They got the optimal global […]

Medusa is a parallel graph processing system on graphics processors (GPUs). The core design of Medusa is to enable developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime […]

The demands placed on systems to analyse corpus data increase with input size, and the traditional approaches to processing this data are increasingly having impractical run-times. We show that the use of desktop GPUs presents a significant opportunity to accelerate a number of stages in the normal corpus analysis pipeline. This paper contains our exploratory […]

Geostatistical methods provide a powerful tool to understand the complexity of data arising from Earth sciences. Since the mid 70’s, this numerical approach is widely used to understand the spatial variation of natural phenomena in various domains like Oil and Gas, Mining or Environmental Industries. Considering the huge amount of data available, standard implementations of […]

In this paper, we study application behavior in GPGPUs. We investigate how data type impacts performance in different applications. As we show, expectedly, some applications can take significant advantage of small data types. Such applications benefit from small data types as a result of increasing cache effective capacity, reducing memory pressure, access latency, and memory […]

A recent trend in scientific computing is the increasingly important role of co-processors, originally built to accelerate graphics rendering, and now used for general high-performance computing. The INFN Computing On Knights and Kepler Architectures (COKA) project focuses on assessing the suitability of co-processor boards for scientific computing in a wide range of physics applications, and […]

We present GPU implementations of two fast force calculation methods, based on series expansions of the Poisson equation. One is the Self-Consistent Field (SCF) method, which is a Fourier-like expansion of the density field in some basis set; the other is the Multipole Expansion (MEX) method, which is a Taylor-like expansion of the Green’s function. […]

With the advent of many-core computer architectures such as GPGPUs from NVIDIA and AMD, and more recently Intel’s Xeon Phi, ensuring performance portability of HPC codes is potentially becoming more complex. In this work we have focused on one important application area — structured grid codes — and investigated techniques for ensuring performance portability across […]

The architecture of high performance computing systems is becoming more and more heterogeneous, as accelerators play an increasingly important role alongside traditional CPUs. Programming heterogeneous systems efficiently is a complex task, that often requires the use of specific programming environments. Programming frameworks supporting codes portable across different high performance architectures have recently appeared, but one […]

To improve the performance of large-scale rendering, it requires not only a good view of data structure, but also less disk and network access, especially for achieving the realistic visual effects. This paper presents an optimization method of global illumination rendering for large datasets. We improved the previous rendering algorithm based on Monte Carlo ray […]

The Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Problem, with two or more constraints. It is an important optimization problem with many real-life applications. To solve this NP-hard problem we use a metaheuristic algorithm based on ant colony optimization (ACO). Since several steps of the algorithm can be carried out concurrently, […]