high performance computing on graphics processing units: hgpu.org

We present real-time vascular visualization methods, which extend on illustrative rendering techniques to particularly accentuate spatial depth and to improve the perceptive separation of important vascular properties such as branching level and supply area. The resulting visualization can and has already been used for direct projection on a patient’s organ in the operation theater where […]

CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object’s appearance changes (e.g., due to object […]

This work explores how the graphics processing unit (GPU) pipeline model can influence future multi-core architectures which include reconfigurable logic cores. The design challenges of implementing five algorithms on two field programmable gate arrays (FPGAs) and two GPUs are explained and performance results contrasted. Explored algorithm features include data dependence, flexible data reuse patterns and […]

With higher bandwidth requirements and more complex protocols, software defined radio (SDR) has ever growing computational demands. SDR applications have different levels of parallelism that can be exploited on multicore platforms, but design and programming difficulties have inhibited the adoption of specialized multicore platforms like graphics processors (GPUs). In this work we propose a new […]

Rate-distortion (RD)-based mode selections are important techniques in video coding. In these methods, an encoder may compute the RD costs for all the possible coding modes, and select the one which achieves the best trade-off between encoding rate and compression distortion. Previous papers have demonstrated that RD-based mode selections can lead to significant improvements in […]

Graphics processors are designed to perform many floating-point operations per second. Consequently, they are an attractive architecture for high-performance computing at a low cost. Nevertheless, it is still not very clear how to exploit all their potential for general-purpose applications. In this work we present a comprehensive study of the performance of an application executing […]

Graphics processing units (GPUs) have been widely used to accelerate algorithms that exhibit massive data parallelism or task parallelism. When such parallelism is not inherent in an algorithm, computational scientists resort to simply replicating the algorithm on every multiprocessor of a NVIDIA GPU, for example, to create such parallelism, resulting in embarrassingly parallel ensemble runs […]

When reconstructing a specific type or class of object using stereo, we can leverage prior knowledge of the shape of that type of object. A popular class of object to reconstruct is the human face. In this paper we learn a statistical wavelet prior of the shape of the human face and use it to […]

Efficient implementations of the Discrete Fourier Transform (DFT) for GPUs provide good performance with large data sizes, but are not competitive with CPU code for small data sizes. On the other hand, several applications perform multiple DFTs on small data sizes. In fact, even algorithms for large data sizes use a divide-and-conquer approach, where eventually […]

In this work, we present an interactive visual clustering approach for the exploration and analysis of vast volumes of data. Our proposed approach is a bio-inspired collective behavioral model to be used in a 3D graphics environment. Our paper illustrates an extension of the behavioral model for clustering and a parallel implementation, using Compute Unified […]

It is well known that LDPC decoding is computationally demanding and one of the hardest signal operations to parallelize. Beyond data dependencies that restrict the decoding of a single word, it requires a large number of memory accesses. In this paper we propose parallel algorithms for performing in GPUs the most demanding case of irregular […]

Terrain modeling is an important task in digital content creation and physics-based approaches have the potential to simplify it. However, most of the existing simulations are hindered by a low level of user control, because they fail on large-scale phenomena. We introduce a new accessible physics-based framework for digital terrain editing. Our solution is suitable […]