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Posts

Feb, 3

3D GPU Architecture using Cache Stacking: Performance, Cost, Power and Thermal analysis

Graphics Processing Units (GPUs) offer tremendous computational and processing power. The architecture requires high communication bandwidth and lower latency between computation units and caches. 3D die-stacking technology is a promising approach to meet such requirements. To the best of our knowledge no other study has investigated the implementation of 3D technology in GPUs. In this […]
Feb, 3

3D finite element numerical integration on GPUs

The algorithmic and computational aspects of 3D finite element numerical integration on GPUs are investigated in the paper. The special stress is put on selecting the proper parallelization strategies depending upon the properties of FEM problems solved and approximations used. The close interplay between the available computational resources of GPUs and the possible implementation strategies […]
Feb, 3

Data access optimized applications on the GPU using NVIDIA CUDA

This work is an attempt to address the problem of bandwidth limited performance of data intensive GPGPU applications. Performance limited by memory bandwidth is common issue faced by general data intensive HPC applications. In case of the GPU, this problem is more pronounced owing to the unique architecture. This problem has been tackled by optimizing […]
Feb, 3

High Performance Power Spectrum Analysis Using a FPGA Based Reconfigurable Computing Platform

Power-spectrum analysis is an important tool providing critical information about a signal. The range of applications includes communication-systems to DNA-sequencing. If there is interference present on a transmitted signal, it could be due to a natural cause or superimposed forcefully. In the latter case, its early detection and analysis becomes important. In such situations having […]
Feb, 2

Real-time PCA calculation for spectral imaging (using SIMD and GP-GPU)

This article presents two optimized implementations of the PCA algorithm, primarily targeted on spectral image analysis in real time. One of them utilizes the SSE instruction set of contemporary CPUs, and the other one runs on graphics processors, using the CUDA environment. The implementations are evaluated and compared with a multithreaded C implementation compiled by […]
Feb, 2

Software parallel CAVLC encoder based on stream processing

Real-time encoding of high-definition H.264 video is a challenge to current embedded programmable processors. Emerging stream processing methods supported by most GPUs and programmable processors provide a powerful mechanism to achieve surprising high performance in media/signal processing, which bring an opportunity to deal with this challenge. However, traditional serial CAVLC has highly input-dependent execution and […]
Feb, 2

Cortical architectures on a GPGPU

As the number of devices available per chip continues to increase, the computational potential of future computer architectures grows likewise. While this is a clear benefit for future computing devices, future chips will also likely suffer from more faulty devices and increased power consumption. It is also likely that these chips will be difficult to […]
Feb, 2

Learning Two-View Stereo Matching

We propose a graph-based semi-supervised symmetric matching framework that performs dense matching between two uncalibrated wide-baseline images by exploiting the results of sparse matching as labeled data. Our method utilizes multiple sources of information including the underlying manifold structure, matching preference, shapes of the surfaces in the scene, and global epipolar geometric constraints for occlusion […]
Feb, 2

Adaptive enhancement and noise reduction in very low light-level video

A general methodology for noise reduction and contrast enhancement in very noisy image data with low dynamic range is presented. Video footage recorded in very dim light is especially targeted. Smoothing kernels that automatically adapt to the local spatio-temporal intensity structure in the image sequences are constructed in order to preserve and enhance fine spatial […]
Feb, 2

Delta-stepping: a parallelizable shortest path algorithm

The single source shortest path problem for arbitrary directed graphs with n nodes, m edges and nonnegative edge weights can sequentially be solved using O(n log n + m) operations. However, no work-efficient parallel algorithm is known that runs in sublinear time for arbitrary graphs. In this paper we present a rather simple algorithm for […]
Feb, 2

OpenGL(R) ES 2.0 Programming Guide

OpenGL ES 2.0 is the industry’s leading software interface and graphics library for rendering sophisticated 3D graphics on handheld and embedded devices. With OpenGL ES 2.0, the full programmability of shaders is now available on small and portable devices-including cell phones, PDAs, consoles, appliances, and vehicles. However, OpenGL ES differs significantly from OpenGL. Graphics programmers […]
Feb, 2

OpenGL SuperBible: Comprehensive Tutorial and Reference (5th Edition)

OpenGL(R) SuperBible, Fifth Editionis the definitive programmer’s guide, tutorial, and reference for the world’s leading 3D API for real-time computer graphics, OpenGL 3.3. The best all-around introduction to OpenGL for developers at all levels of experience, it clearly explains both the API and essential associated programming concepts. Readers will find up-to-date, hands-on guidance on all […]
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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: 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.

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