Jun, 25

Concurrent Analytical Query Processing with GPUs

In current databases, GPUs are used as dedicated accelerators to process each individual query. Sharing GPUs among concurrent queries is not supported, causing serious resource underutilization. Based on the profiling of an open-source GPU query engine running commonly used single-query data warehousing workloads, we observe that the utilization of main GPU resources is only up […]
Jun, 25

GPU-Parallel simulation of rigid fibers in Stokes flow

The simulation of a fiber suspension requires that all interactions between the fibers involved are computed. This is a compute-intensive N-body problem that is highly data parallel. Using the GPU for these types of computations can be very beneficial. In this thesis an extension to a simulator, written in MATLAB, for rigid fibers in Stokes […]
Jun, 25

GPU accelerated Hybrid Tree Algorithm for Collision-less N-body Simulations

We propose a hybrid tree algorithm for reducing calculation and communication cost of collision-less N-body simulations. The concept of our algorithm is that we split interaction force into two parts: hard-force from neighbor particles and soft-force from distant particles, and applying different time integration for the forces. For hard-force calculation, we can efficiently reduce the […]
Jun, 25

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

Recent NVIDIA Graphics Processing Units (GPUs) can execute multiple kernels concurrently. On these GPUs, the thread block scheduler (TBS) uses the FIFO policy to schedule their thread blocks. We show that FIFO leaves performance to chance, resulting in significant loss of performance and fairness. To improve performance and fairness, we propose use of the preemptive […]
Jun, 24

AES encryption on modern consumer architectures

Specialized cryptographic processors target professional applications and offer both low latency and high throughput at the expense of cost. At the consumer level, a modern SoC embodies several accelerators and vector extensions (e.g. SSE, AES-NI), having a high degree of programmability through multiple APIs (OpenMP, OpenCL, etc). This work explains how a modern x86 system […]
Jun, 24

GPU-driven Parallel Processing for Realtime Creation of Tree Animation

The technological demand for graphically generating natural plants in real time recently have been more and more increasing in a variety of interactive content-creating areas such as computer games. In this paper, we propose a GPU-driven high-speed parallel processing algorithm for generating trees and their branches and leaves in real time. The method that we […]
Jun, 23

GPU-based ray-casting of non-rigid deformations: a comparison between direct and indirect approaches

For ray-casting of non-rigid deformations, the direct approach (as opposed to the traditional indirect approach) does not require the computation of an intermediate volume to be used for the rendering step. The aim of this study was to compare the two approaches in terms of performance (speed) and accuracy (image quality). The direct and the […]
Jun, 23

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

From its modest beginnings as a tool to validate forms, JavaScript is now an industrial-strength language used to power online applications such as spreadsheets, IDEs, image editors and even 3D games. Since all modern web browsers support JavaScript, it provides a medium that is both easy to distribute for developers and easy to access for […]
Jun, 23

Feature Generation for Quantification of Visual Similarity

The complex nature of visual similarity makes it extremely difficult to hand code a set of good features that incorporate all of the important aspects for all images. This thesis work shows that machine learning techniques can be used to generate statistically optimal low dimensional features that work well with calculating similarity using Euclidean distance […]
Jun, 23

Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction

In this paper we present new hybrid CPU-GPU routines to accelerate the solution of linear systems, with band coefficient matrix, by off-loading the major part of the computations to the GPU and leveraging highly tuned implementations of the BLAS for the graphics processor. Our experiments with an nVidia S2070 GPU report speed-ups up to 6x […]
Jun, 23

Efficient GPU-based Training of Recurrent Neural Network Language Models Using Spliced Sentence Bunch

Recurrent neural network language models (RNNLMs) are becoming increasingly popular for a range of applications including speech recognition. However, an important issue that limits the quantity of data, and hence their possible application areas, is the computational cost in training. A standard approach to handle this problem is to use class-based outputs, allowing systems to […]
Jun, 23

An efficient parallel algorithm for accelerating computational protein design

MOTIVATION: Structure-based computational protein design (SCPR) is an important topic in protein engineering. Under the assumption of a rigid backbone and a finite set of discrete conformations of side-chains, various methods have been proposed to address this problem. A popular method is to combine the dead-end elimination (DEE) and A* tree search algorithms, which provably […]
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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
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  • 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

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