Mar, 18

2D and 3D level-set algorithms on GPU

Locating object boundaries, modeling shapes is still an interesting and important task in many applications such as computer vision, object detection, image segmentation and tracking. In this paper we show the implementation of 2D and 3D algorithms based on the level sets using the advantages residing in today’s common GPUs. One main goal of this […]
Mar, 18

Directionally Unsplit Hydrodynamic Schemes with Hybrid MPI/OpenMP/GPU Parallelization in AMR

We present the implementation and performance of a class of directionally unsplit Riemann-solver-based hydrodynamic schemes on Graphic Processing Units (GPU). These schemes, including the MUSCL-Hancock method, a variant of the MUSCL-Hancock method, and the corner-transport-upwind method, are embedded into the adaptive-mesh-refinement (AMR) code GAMER. Furthermore, a hybrid MPI/OpenMP model is investigated, which enables the full […]
Mar, 17

Accelerated ray tracing for radiotherapy dose calculations on a GPU

PURPOSE: The graphical processing unit (GPU) on modern graphics cards offers the possibility of accelerating arithmetically intensive tasks. By splitting the work into a large number of independent jobs, order-of-magnitude speedups are reported. In this article, the possible speedup of PLATO’s ray tracing algorithm for dose calculations using a GPU is investigated. METHODS: A GPU […]
Mar, 17

Task Scheduling of Parallel Processing in CPU-GPU Collaborative Environment

With the rapid development of GPU (Graphics Processor Unit) in recent years, GPGPU (General-Purpose computation on GPU) has become an important technique in scientific research. However GPU might well be seen more as a cooperator than a rival to CPU. Therefore, we focus on exploiting the power of CPU and GPU in solving generic problems […]
Mar, 17

Language virtualization for heterogeneous parallel computing

As heterogeneous parallel systems become dominant, application developers are being forced to turn to an incompatiblemix of low level programming models (e.g. OpenMP, MPI, CUDA, OpenCL). However, these models do little to shield developers from the difficult problems of parallelization, data decomposition and machine-specific details. Most programmersare having a difficult time using these programming models […]
Mar, 17

Implementation of algorithms with a fine-grained parallelism on GPUs

The efficiency of implementations of algorithms with a fine-grained parallelism on GPUs that support the CUDA architecture is studied. Cellular automata and difference schemes are used for testing. Several versions of implementations are proposed and their efficiency is analyzed. An example of GPU application for modeling the process of carbon dioxide oxidation on the catalyst […]
Mar, 17

RDMA-Based Job Migration Framework for MPI over InfiniBand

Coordinated checkpoint and recovery is a common approach to achieve fault tolerance on large-scale systems. The traditional mechanism dumps the process image to a local disk or a central storage area of all the processes involved in the parallel job. When a failure occurs, the processes are restarted and restored to the latest checkpoint image. […]
Mar, 17

Live, Video-Rate Super-Resolution Microscopy Using Structured Illumination and Rapid GPU-Based Parallel Processing

Structured illumination fluorescence microscopy is a powerful super-resolution method that is capable of achieving a resolution below 100 nm. Each super-resolution image is computationally constructed from a set of differentially illuminated images. However, real-time application of structured illumination microscopy (SIM) has generally been limited due to the computational overhead needed to generate super-resolution images. Here, […]
Mar, 17

Performance analysis of single-phase, multiphase, and multicomponent lattice-Boltzmann fluid flow simulations on GPU clusters

The lattice-Boltzmann method is well suited for implementation in single-instruction multiple-data (SIMD) environments provided by general purpose graphics processing units (GPGPUs). This paper discusses the integration of these GPGPU programs with OpenMP to create lattice-Boltzmann applications for multi-GPU clusters. In addition to the standard single-phase single-component lattice-Boltzmann method, the performances of more complex multiphase, multicomponent […]
Mar, 17

Memory-Scalable GPU Spatial Hierarchy Construction

Recent GPU algorithms for constructing spatial hierarchies achieve promising performance for moderately complex models by using the BFS (breadth-first search) construction order. While being able to exploit the massive parallelism on the GPU, the BFS order consumes excessive GPU memory, which becomes a serious issue. In this paper, we propose to use the PBFS (partial […]
Mar, 17

CUDA Compatible GPU as an Efficient Hardware Accelerator for AES Cryptography

This paper presents a study of the efficiency in applying modern Graphics Processing Units in symmetric key cryptographic solutions. It describes both traditional style approaches based on the OpenGL graphics API and new ones based on the recent technology trends of major hardware vendors. It presents an efficient implementation of the Advanced Encryption Standard (AES) […]
Mar, 17

High-Throughput Transaction Executions on Graphics Processors

OLTP (On-Line Transaction Processing) is an important business system sector in various traditional and emerging online services. Due to the increasing number of users, OLTP systems require high throughput for executing tens of thousands of transactions in a short time period. Encouraged by the recent success of GPGPU (General-Purpose computation on Graphics Processors), we propose […]
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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

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