9373
Nan Wang
The objective in this case is not only to be realistic, but also to provide new and intelligible ways of model representation. This raises new issues in data perception. The question of perception of complex data, especially regarding visual feedback, is an open question, and it is the subject of this work. This PhD thesis […]
View View   Download Download (PDF)   
Jingwen Leng, Tayler Hetherington, Ahmed ElTantawy, Syed Gilani, Nam Sung Kim, Tor M. Aamodt, Vijay Janapa Reddi
General-purpose GPUs (GPGPUs) are becoming prevalent in mainstream computing, and performance per watt has emerged as a more crucial evaluation metric than peak performance. As such, GPU architects require robust tools that will enable them to quickly explore new ways to optimize GPGPUs for energy efficiency. We propose a new GPGPU power model that is […]
View View   Download Download (PDF)   
Michael Boyer, Jiayuan Meng, Kalyan Kumaran
Accelerators such as graphics processors (GPUs) have become increasingly popular for high performance scientific computing. Often, much effort is invested in creating and optimizing GPU code without any guaranteed performance benefit. To reduce this risk, performance models can be used to project a kernel’s GPU performance potential before it is ported. However, raw GPU execution […]
View View   Download Download (PDF)   
Jiayuan Meng, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran
Applications often have a sequence of parallel operations to be offloaded to graphics processors; each operation can become an individual GPU kernel. Developers typically explore a variety of transformations for each kernel. Furthermore, it is well known that efficient data management is critical in achieving high GPU performance and that "fusing" multiple kernels into one […]
View View   Download Download (PDF)   
Amit Sabne, Putt Sakdhnagool, Rudolf Eigenmann
One thrust of the OpenMP standard development focuses on support for accelerators. An important question is whether or not OpenMP extensions are needed, and how much performance difference they would make. The same question is relevant for related efforts in support of accelerators, such as OpenACC. The present paper pursues this question. We analyze the […]
View View   Download Download (PDF)   
Seyong Lee, Rudolf Eigenmann
General-Purpose Graphics Processing Units (GPGPUs) provide inexpensive, high performance platforms for compute-intensive applications. However, their programming complexity poses a significant challenge to developers. Even though the CUDA (Compute Unified Device Architecture) programming model offers better abstraction, developing efficient GPGPU code is still complex and error-prone. This paper proposes a directive-based, high-level programming model, called OpenMPC, […]
View View   Download Download (PDF)   
Andrew Lumsdaine, Georgi Chunev, Todor Georgiev
Processing and rendering of plenoptic camera data requires significant computational power and memory bandwidth. At the same time, real-time rendering performance is highly desirable so that users can interactively explore the infinite variety of images that can be rendered from a single plenoptic image. In this paper we describe a GPU-based approach for lightfield processing […]
View View   Download Download (PDF)   
Muhammad Hasan Jamal, Nabeel AlSaber
Finite element methods (FEM) are most widely used for simulation of structural dynamics problems. Due to their highly compute intensive nature, these methods are used with domain decomposition where the problem is divided into subdomains which are individually solved and coupled together to obtain the final solution. One of the latest and most efficient approach […]
View View   Download Download (PDF)   
Sunpyo Hong, Hyesoon Kim
GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Programming thousands of massively parallel threads is a big challenge for software engineers, but understanding the performance bottlenecks of those parallel programs on GPU architectures to improve application performance is even more dif?cult. Current approaches rely on programmers […]
View View   Download Download (PDF)   
Jiayuan Meng, Vitali A. Morozov, Kalyan Kumaran, Venkatram Vishwanath, Thomas D. Uram
We propose GROPHECY, a GPU performance projection framework that can estimate the performance benefit of GPU acceleration without actual GPU programming or hardware. Users need only to skeletonize pieces of CPU code that are targets for GPU acceleration. Code skeletons are automatically transformed in various ways to mimic tuned GPU codes with characteristics resembling real […]
View View   Download Download (PDF)   
Isha Sanjay Deshpande
CPU-GPU clusters have emerged as a dominant HPC platform, with the three of the four fastest supercomputers in the world falling in this category. The reasons for the popularity of these environments include their cost-effectiveness and energy efficiency. The need for exploiting both the CPU and GPU on each node of such platforms has created […]
View View   Download Download (PDF)   
Timothy D. R. Hartley
This dissertation presents research into the development of high performance dataflow middleware and applications on heterogeneous, distributed-memory supercomputers. We present coarse-grained state-of-the-art ad-hoc techniques for optimizing the performance of real-world, data-intensive applications in biomedical image analysis and radar signal analysis on clusters of computational nodes equipped with multi-core microprocessors and accelerator processors, such as the […]
View View   Download Download (PDF)   
Page 1 of 512345

* * *

* * *

Like us on Facebook

HGPU group

151 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1252 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: