Results 141 to 150 of about 173,867 (186)
Some of the next articles are maybe not open access.
2016
The GPUs (Graphics Processing Unit) were mainly used to speed up computation intensive high performance computing applications. There are several tools and technologies available to perform general purpose computationally intensive application. This chapter primarily discusses about GPU parallelism, applications, probable challenges and also highlights
K. Bhargavi, Sathish Babu B.
openaire +1 more source
The GPUs (Graphics Processing Unit) were mainly used to speed up computation intensive high performance computing applications. There are several tools and technologies available to perform general purpose computationally intensive application. This chapter primarily discusses about GPU parallelism, applications, probable challenges and also highlights
K. Bhargavi, Sathish Babu B.
openaire +1 more source
GPU Computing: The Future of Computing
Proceedings of the West Virginia Academy of Science, 2018PARKER ANTHONY, Department of Computer Science, Mathematics, and Engineering, Shepherd University, Shepherdstown, WV, 25443.Computing requirements have grown over the last few years, bringing with it the benefits of complex computing. However, this had led to a bottleneck within the system.
openaire +1 more source
2010
The success of the gaming industry is now pushing processor technology like we have never seen before. Since recent graphics processors (GPU's) have been improving both their programmability as well as have been adding more and more floating point processing, it makes them very appealing as accelerators for general-purpose computing. This minisymposium
Elster Anne C., Requena Stéphane
openaire +1 more source
The success of the gaming industry is now pushing processor technology like we have never seen before. Since recent graphics processors (GPU's) have been improving both their programmability as well as have been adding more and more floating point processing, it makes them very appealing as accelerators for general-purpose computing. This minisymposium
Elster Anne C., Requena Stéphane
openaire +1 more source
GPU-based cone beam computed tomography
Computer Methods and Programs in Biomedicine, 2010The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction.
Peter B, Noël +5 more
openaire +2 more sources
SIGGRAPH Asia 2013 Courses, 2013
Modern GPUs support more flexible programming models through systems such as DirectCompute, OpenGL compute, OpenCL, and CUDA. Although much has been made of GPGPU programming, this course focuses on the application of compute on GPUs for graphics in particular.We will start with a brief overview of the underlying GPU architectures for compute.
openaire +1 more source
Modern GPUs support more flexible programming models through systems such as DirectCompute, OpenGL compute, OpenCL, and CUDA. Although much has been made of GPGPU programming, this course focuses on the application of compute on GPUs for graphics in particular.We will start with a brief overview of the underlying GPU architectures for compute.
openaire +1 more source
Automatic execution of single-GPU computations across multiple GPUs
Proceedings of the 23rd international conference on Parallel architectures and compilation, 2014We present AMGE, a programming framework and runtime system to decompose data and GPU kernels and execute them on multiple GPUs concurrently. AMGE exploits the remote memory access capability of recent GPUs to guarantee data accessibility regardless of its physical location, thus allowing AMGE to safely decompose and distribute arrays across GPU ...
Javier Cabezas +5 more
openaire +1 more source
Scientific Computing with GPUs
Computing in Science & Engineering, 2012This special issue attests to the widespread use of GPUs in the scientific computing community. Here the guest editor discusses the articles selected for this issue, and considers how they represent the range of possibilities (and risks) for using GPUs in scientific applications.
openaire +1 more source
GPU Computing and Applications
2015This book presents a collection of state of the art research on GPU Computing and Application. The major part of this book is selected from the work presented at the 2013 Symposium on GPU Computing and Applications held in Nanyang Technological University, Singapore (Oct 9, 2013). Three major domains of GPU application are covered in the book including
openaire +1 more source
CIRCA-GPUs: Increasing Instruction Reuse Through Inexact Computing in GP-GPUs
IEEE Design & Test, 2016The authors introduce a method that exploits fine-grained parallelism and approximate computing in GP-GPU architecture to increase the energy efficiency through spatial and temporal reuse of instructions.
Rahimi, Abbas +2 more
openaire +2 more sources
Adding GPU Computing to Computer Organization Courses
2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 2013How can parallel computing topics be incorporated into core courses that are taken by the majority of undergraduate students? This paper reports our experiences adding GPU computing with CUDA into the core undergraduate computer organization course at two different colleges.
David Bunde +3 more
openaire +1 more source