high performance computing on graphics processing units: hgpu.org

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, […]

We present in this paper several implementations of the 3D Fast Wavelet Transform (3D-FWT) on multicore CPUs and manycore GPUs. On the GPU side, we focus on CUDA and OpenCL programming to develop methods for an efficient mapping on manycores. On multicore CPUs, OpenMP and Pthreads are used as counterparts to maximize parallelism, and renowned […]

Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano. Currently they can harness multiple GPUs on a single machine, but are unable to use GPUs that are […]

High Energy Physics (HEP) needs a huge amount of computing resources. In addition data acquisition, transfer, and analysis require a well developed infrastructure too. In order to prove new physics disciplines it is required to higher the luminosity of the accelerator facilities, which produce more-and-more data in the experimental detectors. Both testing new theories and […]

High-order numerical methods for unstructured grids combine the superior accuracy of high-order spectral or finite difference methods with the geometric flexibility of low-order finite volume or finite element schemes. The Flux Reconstruction (FR) approach unifies various high-order schemes for unstructured grids within a single framework. Additionally, the FR approach exhibits a significant degree of element […]

"Lucky-region" fusion (LRF) is a synthetic imaging technique that has proven successful in enhancing the quality of images distorted by atmospheric turbulence. The LRF algorithm selects sharp regions of an image obtained from a series of short exposure frames, and fuses the sharp regions into a final, improved image. In previous research, the LRF algorithm […]

Approximate computing provides an opportunity for exploiting application characteristics to improve performance of computing systems. However, such opportunity must be balanced against generality of methods and quality guarantees that the system designer can provide to the application developer. Improved parallel processing in graphics processing units (GPUs) provides one such means for data-level parallel applications. We […]

The proliferation of heterogeneous computing platforms presents the parallel computing community with new challenges. One such challenge entails evaluating the efficacy of such parallel architectures and identifying the architectural innovations that ultimately benefit applications. To address this challenge, we need benchmarks that capture the execution patterns (i.e., dwarfs or motifs) of applications, both present and […]

We evaluate the computational performance of the Bernstein-Bezier basis for discontinuous Galerkin (DG) discretizations and show how to exploit properties of derivative and lift operators specific to Bernstein polynomials. Issues of efficiency and numerical stability are discussed in the context of a model wave propagation problem. We compare the performance of Bernstein-Bezier kernels to both […]

This paper puts forward a new FDTD parallel algorithm, which is developed based on the distributed platform, the algorithm was debugged in Shanghai Jiao-tong University for the high performance computing center GPU cluster, "Rubik’s Cube" commercial super computer at Shanghai Supercomputer Center and "divinity blue" domestic super computer platform at the National Supercomputing Center in […]

The programming language Python is widely used to create rapidly compact software. However, compared to low-level programming languages like C or Fortran low performance is preventing its use for HPC applications. Efficient parallel programming of multi-core systems and graphic cards is generally a complex task. Python with add-ons might provide a simple approach to program […]

Motivated by the explosion of Big Data analytics, performance improvements in lowpower (wimpy) systems and the increasing energy efficiency of GPUs, this paper presents a time-energy performance analysis of MapReduce on heterogeneous systems with GPUs. We evaluate the time and energy performance of three MapReduce applications with diverse resource demands on a Hadoop-CUDA framework. As […]