high performance computing on graphics processing units: hgpu.org

Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically for move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search. Research interest in MCTS has risen sharply due to its spectacular success with computer Go and its potential […]

In this paper, we analyze the potential of using weights for block-asynchronous relaxation methods on GPUs. For this purpose, we introduce different weighting techniques similar to those applied in block-smoothers for multigrid methods. For test matrices taken from the University of Florida Matrix Collection we report the convergence behavior and the total runtime for the […]

Hardware accelerators are becoming ubiquitous high performance scientific computing. They are capable of delivering an unprecedented level of concurrent execution contexts. High-level programming languages (e.g., CUDA), profiling tools (e.g., PAPI-CUDA, CUDA Profiler) are paramount to improve productivity, while effectively exploiting the underlying hardware. We present an optimized numerical kernel for computing the symmetric matrix-vector product […]

JPEG2000 has been accepted by The Society of Motion Picture and Television Engineers (SMPTE) as the image compression standard for the digital distribution of motion pictures. In JPEG2000, the biggest contribution to the coding performance comes from the Embedded Block Coding with Optimized Truncation (EBCOT), which is also the most time-consuming module by occupying almost […]

With encouragement by the company DHI are the aim of this B.Sc. thesis to investigate, whether if it is possible to accelerate the simulation speed of DHIs commercial product MIKE 21 HD, by formulating a parallel solution scheme and implementing it to be executed on a CUDA-enabled GPU (massive parallel hardware). MIKE 21 HD is […]

Block iterative methods are extremely important as smoothers for multigrid methods, as preconditioners for Krylov methods, and as solvers for diagonally dominant linear systems. Developing robust and efficient algorithms suitable for current and evolving GPU and multicore CPU systems is a significant challenge. We address this issue in the case of constant-coefficient stencils arising in […]

Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this paper we […]

Mesh simplification and mesh compression are important processes in computer graphics and scientific computing, as such contexts allow for a mesh which takes up less memory than the original mesh. Current simplification and compression algorithms do not take advantage of both the central processing unit (CPU) and the graphics processing unit (GPU). We propose three […]

This paper presents a parallel approach to estimate the parameters in the heat loading of a district heating system by use of the traditional particle swarm optimisation (TPSO) on the Graphic Processing Unit (GPU) using OpenCL. The running time of the algorithm is greatly reduced compared to running on CPU. The heat load is approximated […]

As high-speed networks are becoming commonplace, it is increasingly challenging to prevent the attack attempts at the edge of the Internet. While many high-performance intrusion detection systems (IDSes) employ dedicated network processors or special memory to meet the demanding performance requirements, it often increases the cost and limits functional flexibility. In contrast, existing softwarebased IDS […]

Data warehousing applications represent an emergent application arena that requires the processing of relational queries and computations over massive amounts of data. Modern general purpose GPUs are high core count architectures that potentially offer substantial improvements in throughput for these applications. However, there are significant challenges that arise due to the overheads of data movement […]

We propose the new framework of the distributed tabu search metaheuristic designed to be executed using a multi-GPU cluster, i.e. cluster of nodes equipped with GPU computing units. We propose a hybrid single-walk parallelization of the tabu search, where hybridization consists in examining a number of solutions from a neighborhood concurrently by several GPUs (multi-GPU). […]