high performance computing on graphics processing units: hgpu.org

The problem of scheduling sports leagues has received considerable attention in recent years, especially since mathematically optimized schedules often have a large impact both economically and environmentally. The Mirrored Traveling Tournament Problem (mTTP) is an optimization problem that represents certain types of sports scheduling where the main objective is to minimize the total distance traveled […]

Multiple-GPU systems have become ubiquitously available due to their support of massive parallel computing and more device memory for large scale problems. Such systems are ideal for In-Situ visualization applications, which require significant computational power for concurrent execution of simulation and visualization. While pipelining based parallel computing scheme overlaps the execution of simulation and rendering […]

This paper describes recent work on the BIDMach toolkit for large-scale machine learning. BIDMach has demonstrated single-node performance that exceeds that of published cluster systems for many common machine-learning task. BIDMach makes full use of both CPU and GPU acceleration (through a sister library BIDMat), and requires only modest hardware (commodity GPUs). One of the […]

The adoption of multiclass classification strategies that train independent binary classifiers becomes challenging when the goal is to retrieve nonlinear models from large datasets and the process requires several passes through the data. In such scenario, the combined use of a search and score algorithm and GPUs allows to obtain binary classifiers in a reduced […]

A great challenge for scientists is to execute their computational applications efficiently. Nowadays, parallel programming has become a fundamental key to achieve this goal. High-performance computing provides a solution to exploit parallel architectures in order to get optimal performance. Both parallel programming model and the system architecture will maximize the benefits if both together are […]

On the work sharing among GPUs and CPU cores on GPU equipped clusters, it is a critical issue to keep load balance among these heterogeneous computing resources. We have been developing a runtime system for this problem on PGAS language named XcalableMP-dev/StarPU [1]. Through the development, we found the necessity of adaptive load balancing for […]

We propose a convex relaxation approach to space-time 3D reconstruction from multiple videos. Generalizing the works [16], [8] to the 4D setting, we cast the problem of reconstruction over time as a binary labeling problem in a 4D space. We propose a variational formulation which combines a photoconsistency based data term with a spatio-temporal total […]

The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density Estimator (KDE). However, a […]

This paper spotlights the maintaining of scene boundary detection system in video and process of porting it to the OpenCL. The scene boundary detection algorithm proposed by authors is based on bottom-up focus attention principle. The system builds Gaussian pyramids from input image, calculates map of saliency from the image and then detects the most […]

A mechanism for transparent GPU-independent checkpoint-restart of 3D graphics is described. The approach is based on a record-prune-replay paradigm: all OpenGL calls relevant to the graphics driver state are recorded; calls not relevant to the internal driver state as of the last graphics frame prior to checkpoint are discarded; and and the remaining calls are […]

The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been […]

Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretability by approximating sparse PCs whose projections capture the maximal variance […]