high performance computing on graphics processing units: hgpu.org

We present a highly general implementation of fast multipole methods on graphics processing units (GPUs). Our two-dimensional double precision code features an asymmetric type of adaptive space discretization leading to a particularly elegant and flexible implementation. All steps of the multipole algorithm are efficiently performed on the GPU, including the initial phase which assembles the […]

A current research trend focuses on accelerating database operations with the help of GPUs (Graphics Processing Units). Since GPU algorithms are not necessarily faster than their CPU counterparts, it is important to use them only if they outperform their CPU counterparts. In this paper, we address this problem by constructing a decision model for a […]

With the increasing architectural diversity of many-core architectures the challenges of parallel programming and code portability will sharply rise. The EU project PEPPHER addresses these issues with a component-based approach to application development on top of a task-parallel execution model. Central to this approach are multi-architectural components which encapsulate different implementation variants of application functionality […]

This report presents an approach to compute Alpha Shapes for a 2D un-weighted point set using the graphics processing unit (GPU). The problem of alpha shapes has been well-defined and algorithms have been developed to compute it efficiently in 2D and 3D using CPU. However, the nature of this problem makes it well-suited for solving […]

Spatial data partitioning techniques are important for obtaining fast and efficient simulations of N-Body particle and spatial agent based models where they considerably reduce redundant entity interaction computation times. Highly parallel techniques based on concurrent threading can be deployed to further speed up such simulations. We study the use of GPU accelerators and highly data […]

General purpose graphic programming unit (GPGPU) programming is a novel approach for solving parallel variable independent problems. The graphic processor core (GPU) gives the possibility to use multiple blocks, each of which contains hundreds of threads. Each of these threads can be visualized as a core onto itself, and tasks can be simultaneously sent to […]

Graphic processing units (GPUs) are powerful graphics engines featuring high levels of parallelism and extreme memory bandwidth, which constitute a powerful computing platform to solve complex problems involving chemically reacting flows. In the present study, computer programs for combustion simulations with detailed chemical kinetic mechanisms were compiled in the Compute Unified Device Architecture (CUDA) language […]

A novel algorithm is presented to compute the convex hull of a point set in R3using the graphics processing unit (GPU). By exploiting the relationship between the Voronoi diagram and the convex hull, the algorithm derives the approximation of the convex hull from the former. The missed points are found back by using a two-round […]

Radiation-hardened processors are designed to be resilient against soft errorsbut such processors are slower than Commercial Off-The-Shelf (COTS)processors as well significantly costlier. In order to mitigate the high costs,software techniques such as task re-executions must be deployed together withadequately hardened processors to provide reliability. This leads to a huge designspace comprising of the hardening level […]

Modern computer processing units tend towards simpler cores in greater numbers, favouring the development of data-parallel applications. Evolutionary algorithms are ideal for taking full advantage of SIMD (Single Instruction, Multiple Data) processing, which is available on both CPUs and GPUs. Creating software that runs on a GPU requires the use of specialised programming languages or […]

General purpose computing on graphics processing units (GPGPU) consists of using GPUs to handle computations commonly handled by CPUs. GPGPU programming implies developing specific programs to run on GPUs managed by a host program running on the CPU. To achieve high performance implies to explicitly organize memory transfers between devices. Besides, different incompatible frameworks exist […]

We describe the problem of parallelization of finite difference method (FDM) and finite element method (FEM) computations for certain class of partial differential equations (PDEs) on High Performance Computing (HPC) GPU cluster. For FDM, the structured grids have been employed and optimal data rearrangement operations are performed in GPU computations. For FEM, unstructured triangular and […]