Oct, 14

A Complete and Efficient CUDA-Sharing Solution for HPC Clusters

In this paper we detail the key features, architectural design, and implementation of rCUDA, an advanced framework to enable remote and transparent GPGPU acceleration in HPC clusters. rCUDA allows decoupling GPUs from nodes, forming pools of shared accelerators, which brings enhanced flexibility to cluster configurations. This opens the door to configurations with fewer accelerators than […]
Oct, 14

Random Address Permute-Shift Technique for the Shared Memory on GPUs

The Discrete Memory Machine (DMM) is a theoretical parallel computing model that captures the essence of memory access to the shared memory of a streaming multiprocessor on CUDA-enabled GPUs. The DMM has w memory banks that constitute a shared memory, and w threads in a warp try to access them at the same time. However, […]
Oct, 14

Parallel Algorithms for the Summed Area Table on the Asynchronous Hierarchical Memory Machine, with GPU implementations

The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of computing on CUDA-enabled GPUs. The summed area table (SAT) of a matrix is a data structure frequently used in the area of computer vision which can be obtained by computing the column-wise prefix-sums and then the rowwise prefix-sums. The […]
Oct, 13

Scalable approximate k-NN in multidimensional big data

This thesis studies the scalability of the similarity search problem in large-scale multidimensional data. Similarity search, translating into the neighbour search problem, finds many applications for information retrieval, visualization, machine learning and data mining. The current exponential growth of data motivates the need for approximate and scalable algorithms. In most of existing algorithms and data-structures, […]
Oct, 13

A Parallel Algorithm for Enumerating Joint Weight of a Binary Linear Code in Network Coding

In this paper, we present a parallel algorithm for enumerating joint weight of a binary linear (n, k) code, aiming at accelerating assessment of its decoding error probability for network coding. To reduce the number of pairs of codewords to be investigated, our parallel algorithm reduces dimension k by focusing on the all-one vector included […]
Oct, 13

GAIN: GPU-based Constraint Checking for Context Consistency

Applications in pervasive computing are often context-aware. However, due to uncontrollable environmental noises, contexts collected by applications can be distorted or even conflicting with each other. This is known as the context inconsistency problem. To provide reliable services, applications need to validate contexts before using them. One promising approach is to check contexts against consistency […]
Oct, 13

HACC: Simulating Sky Surveys on State-of-the-Art Supercomputing Architectures

Current and future surveys of large-scale cosmic structure are associated with a massive and complex datastream to study, characterize, and ultimately understand the physics behind the two major components of the ‘Dark Universe’, dark energy and dark matter. In addition, the surveys also probe primordial perturbations and carry out fundamental measurements, such as determining the […]
Oct, 13

Towards Efficient Indexing of Spatiotemporal Trajectories on the GPU for Distance Threshold Similarity Searches

Applications in many domains require processing moving object trajectories. In this work, we focus on a trajectory similarity search that finds all trajectories within a given distance of a query trajectory over a time interval, which we call the distance threshold similarity search. We develop three indexing strategies with spatial, temporal and spatiotemporal selectivity for […]
Oct, 11

Mapping dynamic programming algorithms on graphics processing units

Alignment is the fundamental operation used to compare biological sequences. It also serves to identify regions of similarity that are eventually consequences of structural, functional, or evolutionary relationships. Today, the processing of sequences from large DNA or protein databases is a big challenge. Graphics Processing Units (GPUs) are based on a highly parallel, many-core streaming […]
Oct, 11

Interactive Simulations with Navier-Stokes Equations on many-core Architectures

Navier-Stokes Equations are a mathematical model to describe the behaviour of fluids. They have proven to represent real fluid flows quite well and are base for many fluid simulations. In order to exploit the performance provided by modern many-core systems, fluid simulation algorithms must be able to efficiently solve the Navier-Stokes Equations in parallel. The […]
Oct, 11

Monte Carlo Path Tracing with OpenCL

We introduce an interactive Monte Carlo path tracer that uses the OpenCL framework. A path tracer draws a photo-realistic image of a 3D scene by simulating physical effects of light. Interactivity enables the user to move around the scene in real time, while OpenCL makes it possible to run the same code on either CPU […]
Oct, 11

Performance Improvement of Multichannel Audio by Graphics Processing Units

Multichannel acoustic signal processing has undergone major development in recent years due to the increased complexity of current audio processing applications. People want to collaborate through communication with the feeling of being together and sharing the same environment, what is considered as Immersive Audio Schemes. In this phenomenon, several acoustic effects are involved: 3D spatial […]
Page 4 of 763« First...23456...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

169 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1276 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: