3572

Posts

Apr, 3

GPU acceleration of MOLAR for HRRT List-Mode OSEM reconstructions

The Siemens ECAT HRRT PET scanner has the potential to produce images of the human brain with spatial resolution better than 3 mm. MOLAR (a motion-compensation OSEM List-mode Algorithm for Resolution-recovery) was developed to provide reconstructions of HRRT data with the best possible accuracy and precision. However, a computer cluster is required to generate reconstructions […]
Apr, 3

A Light-weight API for Portable Multicore Programming

Multicore nodes have become ubiquitous in just a few years. At the same time, writing portable parallel software for multicore nodes is extremely challenging. Widely available programming models such as OpenMP and Pthreads are not useful for devices such as graphics cards, and more flexible programming models such as RapidMind are only available commercially. OpenCL […]
Apr, 3

Mobile visual computing

Summary form only given. I will talk about camera phones, how you can use camera as a sensor that gives natural access to the information about the real world around you (mobile augmented reality) and how you can combine general computation capability to combine several input images into better or more interesting output images (mobile […]
Apr, 3

Energy consumption of Graphic Processing Units with respect to automotive use-cases

With the introduction of API’s like CUDA, Stream+ or OpenCL, modern Graphics Processing Units (GPU’s) can be easily employed for general purpose computing. Plus, their comparatively low price per GFLOP makes them interesting candidates for coprocessors in future embedded Electronic Control Units (ECUs). Yet, as car manufacturers thrive to reduce the Thermal Design Power (TDP) […]
Apr, 2

Throughput-Effective On-Chip Networks for Manycore Accelerators

As the number of cores and threads in manycore compute accelerators such as Graphics Processing Units (GPU) increases, so does the importance of on-chip interconnection network design. This paper explores throughput-effective network-on-chips (NoC) for future manycore accelerators that employ bulk-synchronous parallel (BSP) programming models such as CUDA and OpenCL. A hardware optimization is “throughput-effective” if […]
Apr, 2

MARC: A Many-Core Approach to Reconfigurable Computing

We present a Many-core Approach to Reconfigurable Computing (MARC), enabling efficient high-performance computing for applications expressed using parallel programming models such as OpenCL. The MARC system exploits abundant special FPGA resources such as distributed block memories and DSP blocks to implement complete single-chip high efficiency many-core micro architectures. The key benefits of MARC are that […]
Apr, 2

Real-time particle filtering with heuristics for 3D motion capture by monocular vision

Particle filtering is known as a robust approach for motion tracking by vision, at the cost of heavy computation in a high dimensional pose space. In this work, we describe a number of heuristics that we demonstrate to jointly improve robustness and real-time for motion capture. 3D human motion capture by monocular vision without markers […]
Apr, 2

Parallel discrete wavelet transform using the Open Computing Language: a performance and portability study

The discrete wavelet transform (DWT) is a powerful signal processing technique used in the JPEG 2000 image compression standard. The multi-resolution sub-band encoding provided by DWT allows for higher compression ratios, avoids blocking artifacts and enables progressive transmission of images. However, these advantages come at the expense of additional computational complexity. Achieving real-time or interactive […]
Apr, 2

Parallel implementation of the Finite-Difference Time-Domain method in Open Computing Language

In this paper we evaluate the usability and performance of Open Computing Language (OpenCL) targeted for implementation of the Finite-Difference Time-Domain (FDTD) method. The simulation speed was compared to implementations based on alternative techniques of parallel processor programming. Moreover, the portability of OpenCL FDTD code between modern computing architectures was assessed. The average speed of […]
Apr, 2

Speeding-up Pearson Correlation Coefficient calculation on graphical processing units

Sample correlation coefficient is used widely for finding signal similarity in data processing, multimedia, pattern recognition and artificial intelligence applications. Pearson Correlation Coefficient is the most common measure for the correlation coefficient between discrete signals. Similarity search in huge pattern databases require a fast way of calculating the correlation coefficient between numerical vectors. In this […]
Apr, 2

GPU-Enabled AI

GPU-enabled AI is a subset of so- called general-purpose GPU computing (GPGPU). But it promises to be one of the fastest-growing subsets. The rise of cloud computing, recent high-powered graphics-chip releases by AMD’s competitor Nvidia, and the growing acceptance of the OpenCL programming platform have all converged to allow GPU-enabled AI to take off in […]
Apr, 2

Uncertainty-Aware Guided Volume Segmentation

Although direct volume rendering is established as a powerful tool for the visualization of volumetric data, efficient and reliable feature detection is still an open topic. Usually, a tradeoff between fast but imprecise classification schemes and accurate but time-consuming segmentation techniques has to be made. Furthermore, the issue of uncertainty introduced with the feature detection […]
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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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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.

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