high performance computing on graphics processing units: hgpu.org

PET technology has an important role in modern medical diagnostics. With this process we can view snapshot of a given part of the body’s metabolism which provides more information than examining the organ’s anatomy. Processing list mode measurement data is a demanding task, to solve this problem we use GPU which provides the necessary parallel […]

Graphics Processing Unit (GPU)-based parallel computer architectures have shown increased popularity as a building block for high performance computing, and possibly for future Exascale computing. However, their programming complexity remains as a major hurdle for their widespread adoption. To provide better abstractions for programming GPU architectures, researchers and vendors have proposed several directive-based GPU programming […]

Semidefinite programming (SDP) is one of the most important problems among optimization problems at present. It is relevant to a wide range of fields such as combinatorial optimization, structural optimization, control theory, economics, quantum chemistry, sensor network location and data mining. The capability to solve extremely large-scale SDP problems will have a significant effect on […]

Heterogeneous computing nodes are becoming commonplace today, and recent trends strongly indicate that clusters, supercomputers, and cloud environments will increasingly host more heterogeneous resources, with some being massively parallel (e.g., GPU). With such heterogeneous environments becoming common, it is important to revisit scheduling problems for clusters and cloud environments. In this paper, we formulate and […]

In this paper, we present an adaptive multiresolution mesh representation exploring the computational differences of the CPU and the GPU. We build our representation considering a dense-polygon mesh simplified to a base mesh which stores the original geometry by means of an atlas structure. For both simplification and refinement processes, we present a hierarchical method […]

OpenCL platform provides unified development environment for various multicore processors. In this paper, we evaluate the OpenCL framework for application in pattern recognition. We have selected the most common algorithm for Artificial Neural Networks (ANN) training – the backpropagation algorithm for parallelization with OpenCL because of its high demand for processing resources. We will show […]

Applications often have a sequence of parallel operations to be offloaded to graphics processors; each operation can become an individual GPU kernel. Developers typically explore a variety of transformations for each kernel. Furthermore, it is well known that efficient data management is critical in achieving high GPU performance and that "fusing" multiple kernels into one […]

The work presented here is driven by two observations. First, heterogeneous architectures that integrate a CPU and a GPU on the same chip are emerging, and hold much promise for supporting power-efficient and scalable high performance computing. Second, MapReduce has emerged as a suitable framework for simplified parallel application development for many classes of applications, […]

In this paper, we present a scalable, numerically stable, high-performance tridiagonal solver. The solver is based on the SPIKE algorithm for partitioning a large matrix into small independent matrices, which can be solved in parallel. For each small matrix, our solver applies a general 1-by-1 or 2-by-2 diagonal pivoting algorithm, which is also known to […]

The introduction of Graphical Processing Units (GPUs) in scientific computing has shown great promise in many different fields. While GPUs are capable of very high floating point performance and memory bandwidth, its massively parallel architecture requires algorithms to be reimplemented to suit the different architecture. Interior point method can be used to solve convex optimization […]

The topic of this thesis is GPU accelerated sparse linear algebra for subsurface reservoir modeling. Numerical techniques for reservoir sim- ulations are described and we present the open source reservoir simulation software toolbox MRST. We discuss some of the challenges related to linear algebra and reservoir simulation. Furthermore, we discuss the possibility GPU-acceleraing the linear […]

The optimisation of Technical Trading parameters is a computationally intensive exercise. Models comprising a modest number of Technical Indicators require many thousands of simulations to be executed over a sample period of data, with the best performing sets of parameters employed to generate future trading signals. The purpose of this research is to investigate the […]