high performance computing on graphics processing units: hgpu.org

In this paper, we propose a method for activity recognition from videos based on sparse local features and hypergraph matching. We benefit from special properties of the temporal domain in the data to derive a sequential and fast graph matching algorithm for GPUs. Traditionally, graphs and hypergraphs are frequently used to recognize complex and often […]

We present a new library for scalable 3-D Fast Fourier Transforms (FFT). Despite the large amount of work on 3-D FFTs, we show that significant speedups can be achieved for large problem sizes and core counts. The importance of FFT in science and engineering and the advances in high performance computing necessitate further improvements in […]

In non-linear systems, where explicit analytic solutions usually can’t be found, visualisation is a powerful approach which can give insights into the dynamical behaviour of models; it is also crucial for teaching this area of mathematics. In this paper we present new software, Fireflies, which exploits the power of graphical processing unit (GPU) computing to […]

We present OMP2HMPP, a tool that, in a first step, automatically translates OpenMP code into various possible transformations of HMPP. In a second step OMP2HMPP executes all variants to obtain the performance and power consumption of each transformation. The resulting trade-off can be used to choose the more convenient version. After running the tool on […]

Photon mapping is a global illumination algorithm which is composed of two steps: photon tracing and photon searching. During photon searching step, each shading point needs to search the photon-tree to find k-neighbouring photons for reflected radiance estimation. As the number of shading points and the size of photon-tree are dramatically large, the photon searching […]

Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time interval, and use oriented bounding boxes to speedup the process. However, these discrete methods cannot guarantee no penetration occurs when the components moving. Meanwhile, because some of the components are embedded into each other, these components cannot be separated in the subsequent […]

Algorithms that aim to simultaneously run on a heterogeneous collection of devices on a commodity platform have been in recent research focus. On such platforms, individual devices can have very differing architectures, clock rates, and execution models. Hence, one of the fundamental challenges in designing and implementing such algorithms is to identify load balancing mechanisms […]

This paper proposes a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such as mobile phones. Our method is inspired by the loop perforation technique from source code optimization and accelerates the evaluation of bottleneck convolutional layers by exploiting the […]

In this paper, we introduce IPMACC a framework for executing OpenACC for C applications over OpenCL runtime. We use over framework to compare performance of OpenACC and OpenCL. OpenACC API abstractions remove the low-level control from programmers’ hand. To understand the low-level OpenCL optimizations that are not applicable in OpenACC, we compare highly-optimized OpenCL and […]

Most of the implementations solving photo-realistic image rendering use standard unidirectional path tracing, having fast and accurate results for scenes without caustics or hard cases. These hard cases are usually solved by a bidirectional path tracing algorithm. However, due to the complexity of the bidirectional path tracing algorithms, its implementations almost exclusively target sequential CPUs. […]

The last decade has witnessed the blooming emergence of many-core platforms, especially the graphic processing units (GPUs). With the exponential growth of cores in GPUs, utilizing them efficiently becomes a challenge. The data-parallel programming model assumes a single instruction stream for multiple concurrent threads (SIMT); therefore little support is offered to enforce thread ordering and […]

Email Spam-classification is a fundamental, unseen element of everyday life. As email communication becomes more prolific, and email systems become more robust, it becomes increasingly necessary for Spam-classification systems to run accurately and efficiently while remaining all but invisible to the user. We propose a massively parallel implementation of Spam-classification using the k-Nearest Neighbors (kNN) […]