high performance computing on graphics processing units: hgpu.org

Pedestrian movement, although ubiquitous and well-studied, is still not that well under-stood due to the complicating nature of the embedded social dynamics. Interest among researchers in simulating the nature of pedestrian movement and interactions has grown significantly in part due to increased computational and visualization capabilities afforded by high power computing. Different approaches have been […]

Planning under uncertainty in multiagent settings is highly intractable because of history and plan space complexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the computational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the […]

Recently, heterogeneous system architectures are becoming mainstream for achieving high performance and power efficiency. In particular, many-core graphics processing units (GPUs) now play an important role for computing in heterogeneous architectures. However, for application designers, computational workload still needs to be distributed to heterogeneous GPUs manually and remains inefficient. In this paper, we propose a […]

Game tree search is a classical problem in the field of game theory and artificial intelligence. Focus of the system is on how to leverage massive parallelism capabilities of GPUs to accelerate the speed of game tree algorithms and propose a concise and general parallel game tree algorithm on GPUs. Comparison can be done for […]

All-pairs shortest path problem(APSP) finds a large number of practical applications in real world. We owe to present a highly parallel and recursive solution for solving APSP problem based on Kleene’s algorithm. The proposed parallel approach for APSP is implemented using an open standard framework OpenCL which provides a development environment for utilizing massive parallel […]

The biomedical imagery, the numeric communications, the acoustic signal processing and many others digital signal processing applications (DSP) are present more and more everyday in the numeric world. They process growing data volume which is represented with more and more accuracy, and using complex algorithms with time constraints to satisfying. Consequently, a high requirement of […]

The increasing complexity of new parallel architectures has widened the gap between adaptability and efficiency of the codes. As high performance numerical libraries tend to focus more on performance, we wish to address this issue using a C++ library called NT2. By analyzing the properties of the linear algebra domain that can be extracted from […]

Power constraints are forcing HPC systems to continue to increase hardware concurrency. Efficiently scaling applications on future machines will be essential for improved science and it is recognised that the "flat" MPI model will start to reach its scalability limits. The optimal approach is unknown, necessitating the use of mini-applications to rapidly evaluate new approaches. […]

Past researches on multicore scheduling assume that a computational unit has already been parallelized into a prefixed number of threads. However, with recent technologies such as OpenCL, a computational unit can be parallelized in many different ways with runtime selectable numbers of threads. This paper proposes an optimal algorithm for parallelizing and scheduling a set […]

Dictionary training for sparse representations involves dealing with large chunks of data and complex algorithms that determine time consuming implementations. SBO is an iterative dictionary learning algorithm based on constructing unions of orthonormal bases via singular value decomposition, that represents each data item through a single best fit orthobase. In this paper we present a […]

GPU-based beamforming is a relatively unexplored area in radio astronomy, possibly due to the assumption that any such system will be severely limited by the PCIe bandwidth required to transfer data to the GPU. We have developed a CUDA-based GPU implementation of a coherent beamformer, specifically designed and optimised for deployment at the BEST-2 array […]

We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, […]