high performance computing on graphics processing units: hgpu.org

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) […]

We present a fast and user friendly exoplanet transit light curve modelling package PyTransit, implementing optimised versions of the Gimen’ez and the Mandel & Agol transit models. The package offers an object-oriented Python interface to access the two models implemented natively in Fortran with OpenMP parallelisation. A partial OpenCL version of the quadratic Mandel-Agol model […]

The implementation of parallel genetic algorithms on a graphic processor GPU to solve the Travelling Salesman Problem instances is presented. Two versions of parallel genetic algorithms are implemented, a Parallel Genetic Algorithm with Islands Model and a Parallel Genetic Algorithm with Elite Island; the two versions were executed on a GPU. In both cases, each […]

Sparse matrix-vector multiplication (SpMV) is a central building block for scientific software and graph applications. Recently, heterogeneous processors composed of different types of cores attracted much attention because of their flexible core configuration and high energy efficiency. In this paper, we propose a compressed sparse row (CSR) format based SpMV algorithm utilizing both types of […]

In this thesis we present a state-of-the-art approach to accelerate Monte Carlo valuations of embedded options. Due to regulations and improved risk management, nested simulations (scenarios in scenarios) are becoming increasingly important for institutional investors like: insurance companies, pension funds and housing corporations. Preferably one wishes to use a framework in which multiple related problems […]

Real-world optimization problems are very complex and NP-hard. The modeling of such problems is in constant evolution in term of constraints and objectives and their resolution is expensive in computation time. With all this change, even metaheuristics, well known for their efficiency, begin to be overtaken by data explosion. Recently, Thanks to the publication of […]