high performance computing on graphics processing units: hgpu.org

Real-time stereo matching, which is important in many applications like self-driving cars and 3-D scene reconstruction, requires large computation capability and high memory bandwidth. The most time-consuming part of stereomatching algorithms is the aggregation of information (i.e. costs) over local image regions. In this paper, we present a generic representation and suitable implementations for three […]

We present a parallel implementation of the randomized (1 + e)-approximation algorithm for packing and covering linear programs presented by Koufogiannakis and Young [4]. In order to make the algorithm more parallelizable we also implemented a deterministic version of the algorithm, i.e. instead of updating a single random entry at each iteration we updated deterministically […]

Graphics Processing Units (GPUs) have been extensively applied in the High Performance Computing (HPC) community. HPC applications require additional special programming environments to improve the utilization of GPUs, for example, NVIDIA’s CUDA and Khronos group’s OpenCL. This thesis will introduce a preprocessor framework called HPC.NET, which is deployed on the Microsoft .NET platform to meet […]

Massively parallel processing is a type of computing that uses many separate CPUs or GPUs running in parallel to execute a single program. Because most computations are contained in program loops, automatic extraction of parallelism available in loops is extremely important for many-core systems. In this paper, we study speed-up and scalability of parallel code […]

Algorithm-specific, i.e., semantic-specific optimizations have been observed to bring significant performance gains, especially for a diverse set of multi/many-core architectures. However, current programming models and compiler technologies for the state-of-the-art architectures do not exploit well these performance opportunities. In this paper, we propose a pattern-making methodology that enables algorithm-specific optimizations to be encapsulated into "optimization […]

Applications based on unstructured meshes are typically compute intensive, leading to long running times. In principle, state-of-the-art hardware, such as multi-core CPUs and many-core GPUs, could be used for their acceleration but these esoteric architectures require specialised knowledge to achieve optimal performance. OP2 is a parallel programming layer which attempts to ease this programming burden […]

This paper describes a local dimming method for reducing the power consumption of LCD monitors. Reducing this load is of ever growing importance as it is getting the dominant power consumer of mobile computing. As a side effect, our method does not only significantly reduce the power consumption but also improves the visual quality (see […]

Automatically parallelizing loop nests into CUDA kernels must exploit the full potential of GPUs to obtain high performance. One state-of-the-art approach makes use of the polyhedral model to extract parallelism from a loop nest by applying a sequence of affine transformations to the loop nest. However, how to automate this process to exploit both intraand […]

Image segmentation is a very important step in many GIS applications. Mean shift is an advanced and versatile technique for clustering-based segmentation, and is favored in many cases because it is non-parametric. However, mean shift is very computationally intensive compared with other simple methods such as k-means. In this work, we present a hybrid design […]

Present need to move towards heterogeneous architectures has been well established. This has increased the importance of parallelization of software to achieve good performance. Use of mixed architectures exponentially increases the need of the programmer to understand the intricacies of the underlying hardware to achieve optimal speedup. Obtaining optimal performance on one such architecture is […]

In this paper an inverse preconditioner for the numerical solution of an elliptic Laplace prob- lem of a global circulation ocean model is presented. The inverse preconditiong technique is adopted in order to efficiently compute the numerical solution of the elliptic kernel by using the Conjugate Gradient (CG) method. We show how the performance and […]

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of […]