Jul, 17

Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs

In this paper, we propose a basic technique to minimize the computational time in executing the infinite-stage dynamic programming (DP) on a GPU. The infinite-stage DP involves computations to probe whether a value function gets sufficiently close to the optimal one. Such computations for probing convergence become obvious when an infinite-stage DP is executed on […]
Jul, 16

GPUdrive: Reconsidering Storage Accesses for GPU Acceleration

GPU-accelerated data-intensive applications demonstrate in excess of ten-fold speedups over CPU-only approaches. However, file-driven data movement between the CPU and the GPU can degrade performance and energy efficiencies by an order of magnitude as a result of traditional storage latency and ineffectual memory management. In this paper, we first analyze these two critical performance bottlenecks […]
Jul, 16

Interactive GPU Ray Casting using Progressive Blue Noise Sampling

We describe a generic approach to incorporate progressive refinement into GPU-based ray casting. Our approach allows to interactively navigate through highly complex scenes that may usually take several seconds to render while producing high-quality anti-aliased images in late stages of the refinement process. It maintains interactivity by initially evaluating only a small number of screen […]
Jul, 16

Parallel Variable Pre-Selection and Lookahead Solving on GPUs

SAT solving strategies that perform backtracking or clause learning are usually difficult to implement efficiently on massively-parallel architectures because the necessary synchronization does not scale linear with the number of processors available. Strategies like Lookahead Solving and Cube and Conquer are more promising. In order to evaluate a potential GPU implementation of Cube and Conquer, […]
Jul, 15

Computational Simulation of Freely Falling Water Droplets on Graphics Processing Units

This work describes and demonstrates a novel numerical framework suitable for simulating the behaviour of freely falling liquid droplets. The specific case studied is designed such that the properties of the system are similar to those of raindrops falling through air. The study of raindrops is interesting from both an engineering standpoint and from a […]
Jul, 15

CUD@SAT: SAT Solving on GPUs

The parallel computing power offered by Graphical Processing Units (GPUs) has been recently exploited to support general purpose applications-by exploiting the availability of general API and the SIMT-style parallelism present in several classes of problems (e.g., numerical simulations, matrix manipulations) – where relatively simple computations need to be applied to all items in large sets […]
Jul, 14

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

With processor clock speeds having stagnated, parallel computing architectures have achieved a breakthrough in recent years. Emerging many-core processors like graphics cards run hundreds of threads in parallel and vector instructions are experiencing a revival. Parallel processors with many independent but simple arithmetical logical units fail executing serial tasks efficiently. However, their sheer parallel processing […]
Jul, 14

A Fine Grained Cycle Sharing System with Cooperative Multitasking on GPUs

The emergence of compute unified device architecture (CUDA), which has relieved application developers from having to understand complex graphics pipelines, has made the graphics processing unit (GPU) useful not only for graphics applications but also for general applications. In this paper, we present a cycle sharing system named GPU grid, which exploits idle GPU cycles […]
Jul, 14

GPU Techniques Applied to Euler Flow Simulations and Comparison to CPU Performance

With the decrease in cost of computing, and the increasingly friendly programming environments, the demand for computer generated models of real world problems has surged. Each generation of computer hardware becomes marginally faster than its predecessor, allowing for decreases in required computation time. However, the progression is slowing and will soon reach a barrier as […]
Jul, 14

Infiniband-Verbs on GPU: A case study of controlling an Infiniband network device from the GPU

Due to their massive parallelism and high performance per watt GPUs gain high popularity in high performance computing and are a strong candidate for future exacscale systems. But communication and data transfer in GPU accelerated systems remain a challenging problem. Since the GPU normally is not able to control a network device, today a hybrid-programming […]
Jul, 14

Benchmarking the Memory Hierarchy of Modern GPUs

Memory access efficiency is a key factor for fully exploiting the computational power of Graphics Processing Units (GPUs). However, many details of the GPU memory hierarchy are not released by the vendors. We propose a novel fine-grained benchmarking approach and apply it on two popular GPUs, namely Fermi and Kepler, to expose the previously unknown […]
Jul, 12

Parallel Implementations for Solving Shortest Path Problem using Bellman-Ford

In this paper, different parallel implementations of Bellman-Ford algorithm on GPU using OpenCL are presented. These variants include Bellman-Ford for solving single source shortest path (SSSP) having two variants and Bellman-Ford for all pair shortest path (APSP) problems. Also, a comparative analysis of their performances on CPU and GPU is discussed in this paper.Write-write consistency […]
Page 3 of 73812345...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

128 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1189 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: