high performance computing on graphics processing units: hgpu.org

Heterogeneous clusters with multiple sockets and multicore-processors accelerated by dedicated coprocessors like GPUs, Cell BE, FPGAs or others nowadays provide unrivaled computing power in terms of floating point operations. Specific capabilities of additional processor technologies enable dedicated exploitation with respect to particular application and data characteristics. However, resource utilization, programmability, and scalability of applications across […]

The Hyperspectral Image Analysis Toolbox (HIAT) is a MATLABtrade toolbox for the analysis of hyperspectral imagery. HIAT includes a collection of algorithms for processing of hyperspectral and multispectral imagery under the MATLAB environment. The objective of HIAT is to provide a suite of information extraction algorithms to users of hyperspectral and multispectral imagery across different […]

Many time series data mining problems require subsequence similarity search as a subroutine. Dozens of similarity/distance measures have been proposed in the last decade and there is increasing evidence that Dynamic Time Warping (DTW) is the best measure across a wide range of domains. Given DTW’s usefulness and ubiquity, there has been a large community-wide […]

Numerous approaches have been proposed for detecting clusters, groups of data in spatial databases. Of these, the algorithm known as Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a recent approach which has proven efficient for larger databases. Graphical Processing Units (GPUs), used originally to aid in the processing of high intensity graphics, […]

In this paper we describe the implementation of a complete ANN training procedure for speech recognition using the block mode back-propagation learning algorithm. We exploit the high performance SIMD architecture of GPU using CUDA and its C-like language interface. We also compare the speed-up obtained implementing the training procedure only taking advantage of the multi-thread […]

An algorithm for evolving recurrent neural network via the genetic algorithm was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary). Run on a Nvidia Tesla "GPU supercomputer," CuParcone achieves a performance increase of 323 times in face gender recognition compared to the comparable Parcone algorithm on a […]

Held in conjunction with PACT 2011. GPUs are cost-effective platforms for computational intensive applications providing tremendous peak performance. However, it is a major challenge to deliver the intrinsic performance of such architectures to end applications. The goal of this workshop is to bring together GPU experts with computational science experts. The workshop addresses programming approaches […]

The International Symposium on High-Performance Computer Architecture (HPCA 2012) provides a high-quality forum for scientists and engineers to present their latest research findings in this rapidly-changing field. Authors are invited to submit papers on all aspects of high-performance computer architecture. Topics of interest include, but are not limited to: Processor, cache and memory architectures Parallel […]

In order to characterize nanostructures and nanosurfaces in production processes, measuring methods based on light scattering gain increasing importance. Thus the simulation capability of laser light scattering on surfaces with a size of several hundred or thousand wavelenghts in diameter and light scattering models on the nanometer scale are required to validate these new measurement […]

Unlike usual VLSI approaches necessary for the computation of intensive Low-Density Parity-Check (LDPC) code decoders, this paper presents flexible software-based LDPC decoders. Algorithms and data structures suitable for parallel computing are proposed in this paper to perform LDPC decoding on multicore architectures. To evaluate the efficiency of the proposed parallel algorithms, LDPC decoders were developed […]

CUDA (Compute Unified Device Architecture) acceleration of very large scale matrix-vector and matrix-matrix multiplication is presented in this paper. The intrinsic parallelism in the matrix computations are exploited thoroughly. By dividing the entire matrix computation to multiple sub-groups, scalable performance improvement can be achieved using multiple GPUs. The key operations are accelerated by GPU. And […]

Many interrelated planetary height map and surface image map data sets exist, and more data are collected each day. Broad communities of scientists require tools to compose these data interactively and explore them via real-time visualization. While related, these data sets are often unregistered with one another, having different projection, resolution, format, and type. We […]