Nov, 12

Brute force de-shredding algorithm using the GPU

The graphics processing unit (GPU) has seen significant increase in performance over the past few years. Hence the interest in using GPUs for more general purposes has increased. The higher number of cores on a GPU allows it to outperform central processing units (CPUs). However, since in certain aspects instructions executed on the GPU must […]
Nov, 12

Locality-Aware Mapping of Nested Parallel Patterns on GPUs

Recent work has explored using higher level languages to improve programmer productivity on GPUs. These languages often utilize high level computation patterns (e.g., Map and Reduce) that encode parallel semantics to enable automatic compilation to GPU kernels. However, the problem of efficiently mapping patterns to GPU hardware becomes significantly more difficult when the patterns are […]
Nov, 12

Accelerated Runtime Verification of LTL Specifications with Counting Semantics

Runtime verification is an effective automated method for specification-based offline testing and analysis as well as online monitoring of complex systems. The specification language is often a variant of regular expressions or a popular temporal logic, such as LTL. This paper presents a novel and efficient parallel algorithm for verifying a more expressive version of […]
Nov, 12

Grace: a Cross-platform Micromagnetic Simulator On Graphics Processing Units

A micromagnetic simulator running on graphics processing unit (GPU) is presented. It achieves significant performance boost as compared to previous central processing unit (CPU) simulators, up to two orders of magnitude for large input problems. Different from GPU implementations of other research groups, this simulator is developed with C++ Accelerated Massive Parallelism (C++ AMP) and […]
Nov, 12

Code Optimization on Kepler GPUs and Xeon Phi

Kepler GTX Titan Black and Kepler Tesla K40 are still the best GPUs for high performance computing, although Maxwell GPUs such as GTX 980 are available in the market. Hence, we measure the performance of our lattice QCD codes using the Kepler GPUs. We also upgrade our code to use the latest CPS (Columbia Physics […]
Nov, 9

An Execution Model for OpenCL 2.0

A popular approach to programming manycore GPUs is the Single Instruction Multiple Thread (SIMT) abstraction. SIMT has the benefit of presenting a "single thread" view, alleviating the complexity of explicitly vectorizing the source code. However, due to the SIMD nature of the underlying hardware it is often difficult to fully hide all aspects from the […]
Nov, 9

Real-time 3D Reconstruction for FPGAs: A Case Study for Evaluating the Performance, Area, and Programmability Trade-offs of the Altera OpenCL SDK

Embedding real-time 3D reconstruction of a scene from a low-cost depth sensor can improve the development of technologies in the domains of augmented reality, mobile robotics, and more. However, current implementations require a computer with a powerful GPU, which limits its prospective applications with low-power requirements. To implement low-power 3D reconstruction we embedded two prominent […]
Nov, 9

Relax-Miracle: GPU Parallelization of Semi-Analytic Fourier-Domain solvers for Earthquake Modeling

Effective utilization of GPU processing capacity for scientific workloads is often limited by memory throughput and PCIe communication transfer times. This is particularly true for semi-analytic Fourier-domain computations in earthquake modeling (Relax) where operations on large-scale 3D data structures can require moving large volumes of data from storage to the compute in predictable but orthogonal […]
Nov, 9

Parallel FIM Approach on GPU using OpenCL

In this paper, we describe GPU-Eclat algorithm, a GPU (General Purpose Graphics Processing Unit) enhanced implementation of Frequent Item set Mining (FIM). The frequent itemsets are extracted from a transactional database as it is a essential assignment in data mining field because of its broad applications in mining association rules, time series, correlations etc. The […]
Nov, 9

Dogwild! – Distributed Hogwild for CPU & GPU

Deep learning has enjoyed tremendous success in recent years. Unfortunately, training large models can be very time consuming, even on GPU hardware. We describe a set of extensions to the state of the art Caffe library [3], allowing training on multiple threads and GPUs, and across multiple machines. Our focus is on architecture, implementing asynchronous […]
Nov, 5

Graphics Processing Unit-Based Computer-Aided Design Algorithms for Electronic Design Automation

This dissertation presents research focusing on reshaping the design paradigm of electronic design automation (EDA) applications to embrace the computational throughput of a massively parallel computing architecture. The EDA industry has gone through major evolution in algorithm designs over the past several decades, delivering improved and more sophisticated design tools. Today, these tools provide a […]
Nov, 5

GPU Acceleration of k-Nearest Neighbor Search in Face Classifier based on Eigenfaces

Face recognition is a specialized case of object recognition, and has broad applications in security, surveillance, identity management, law enforcement, human-computer interaction, and automatic photo and video indexing. Because human faces occupy a narrow portion of the total image space, specialized methods are required to identify faces based on subtle differences. One such method is […]
Page 10 of 775« First...89101112...203040...Last »

* * *

* * *

Like us on Facebook

HGPU group

194 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1331 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: