Partnering with NVIDIA, this four day CUDA training course, held in Houston is designed for programmers in the oil and gas industry who are looking to develop comprehensive skills in writing and optimizing applications that fully leverage the many-core processing capabilities of the GPU. Commonly used algorithms such as filtering and FFTs will be used and profiled in the examples. The case study on day 4 focuses on efficient implementation of a finite difference algorithm which is highly applicable to reverse time migration. However a background in oil and gas is not necessary. For more information and to view a copy of the course outline please visit: http://acceleware.com/training/987
Boost.Compute is a header-only C++ library for GPGPU and parallel-computing based on OpenCL. It provides a low-level C++ wrapper over OpenCL and high-level STL-like API with containers and algorithms for the GPU. It is available on GitHub and instructions for getting started can be found in the documentation. See the full announcement here: http://kylelutz.blogspot.com/2014/07/boost-compute-v0.3-released.html
A new version of the rCUDA middleware has been released (version 4.2). In addition to fix some minor bugs, the new release provides support for:
- CUDA 6.0 Runtime API
- New stream management
- cuSPARSE libraries
The rCUDA middleware allows to seamlessly use, within your cluster, GPUs that are installed in computing nodes different from the one that is executing the CUDA application, without requiring to modify your program. Please visit www.rcuda.net for more details about the rCUDA technology.
Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful GPUs to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github: https://github.com/wanderine/BROCCOLI.
(A. Eklund, P. Dufort, M. Villani and S. LaConte: “BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs”. Front. Neuroinform. 8:24, 2014. [DOI])
PARALUTION is a library for sparse iterative methods which can be performed on various parallel devices, including multi-core CPU, GPU (CUDA and OpenCL) and Intel Xeon Phi. The new 0.7.0 version provides the following new features:
- Windows support – full windows support for all backends (CUDA, OpenCL, OpenMP)
- Assembling function – new OpenMP parallel assembling function for sparse matrices (includes an update function for time-dependent problems)
- Direct (dense) solvers (for very small problems)
- (Restricted) Additive Schwarz preconditioners
- MATLAB/Octave plug-in
To avoid OpenMP overhead for small sized problems, the library will compute in serial if the size of the matrix/vector is below a pre-defined threshold. Internally, the OpenCL backend has been modified for simplified cross platform compilation.
Join the free webinar on May 20th devoted to accelerating orthorectification, atmospheric correction, and transformations for big data with GPUs. Learn how GPU capabilities can improve time for processing large imagery 50-100 times faster. Amanda O’Connor, a Senior Solutions Engineer at Exelis will walk you through implementation of GPU processing for large imagery datasets, operational use of GPU processing for orthorectification and share benchmarks against desktop algorithms. To register follow this link: https://www2.gotomeeting.com/register/665929994.
Boost.Compute v0.2 has been released! Boost.Compute is a header-only C++ library for GPGPU and parallel-computing based on OpenCL. It is available on GitHub and instructions for getting started can be found in the documentation. Since version 0.1 (released almost two months ago) new algorithms including unique(), search() and find_end() have been added, along with several bug fixes. See the project page on GitHub for more information: https://github.com/kylelutz/compute
A new version of the GPU-profiler for CUDA software stack is available at www.lab4241.com. The GPU-profiler is able to deliver per C++ source-code ‘inside’ kernel performance information in a simple, intuitive way, similar to known CPU domain profilers, like Quantify or Valgrind. The new version, GPUPROF version 0.3 (beta), includes improved stability, refined memory tracing, temporal memory analysis, and CUDA API-driver call tracing.
This webinar covers how Geoweb3d uses the GPU for real-time geospatial 3D visualization, modeling, and analytics. Geoweb3D will demonstrate how native, high resolution datasets including GIS, CAD, 3D Models, LIDAR, and FMV are fused together in real-time with game quality graphics and pixel accurate analysis. The 3D engine uses a GPU resident mesh that adapts to any resolution data on the fly eliminating the need to preprocess any data prior to real-time use. Demonstration will include Geoweb3d Mobile which now uses HTML5 for use on any device in the cloud including phones and tablets.
To register follow this link: https://www2.gotomeeting.com/register/226039466
A new version of the rCUDA middleware has been released (version 4.1). In addition to fix some bugs related with asynchronous memory transfers, the new release provides support for:
- CUDA 5.5 Runtime API
- Mellanox Connect-IB network adapters
- Dynamic Parallelism
- cuFFT and cuBLAS libraries
The rCUDA middleware allows to seamlessly use, within your cluster, GPUs that are installed in computing nodes different from the one that is executing the CUDA application, without requiring to modify nor recompile your program. Please visit www.rcuda.net for more details about the rCUDA technology.