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International Conference on Parallel Computing 2013, ParCo2013
ParCo2013 continues the tradition of the international conferences on parallel computing started in Berlin, Germany in 1983. This makes it one of the longest running international conferences on parallel computing. Over the years the conference established itself as the foremost platform for exchanging know-how on the newest parallel computing strategies, technologies, methods and tools.
The aim of the conference is to give an overview of the state of the art of the developments, applications, and future trends in parallel computing for the whole range of platforms. The conference addresses all aspects of parallel computing, including applications, hardware and software technologies as well as languages and development environments.
Section 1: Algorithms
Design, analysis, and implementation of parallel algorithms in science and engineering, focusing on issues such as:
* Scalability and speedup
* Efficient utilization of the memory hierarchy
* Communication and synchronization
* Data management and exploration
* Energy efficiency
The parallel computing aspects should be emphasized.
Section 2: Software and Architectures
Software engineering for developing and maintaining parallel software, including:
* Parallel programming languages, compilers, and environments
* Tools and techniques for generating reliabl;e and efficient parallel code
* Parallel programming languages, compilers and environments
* Testing and debugging techniques and tools
* Best practices of parallel computing on multicore, manycore, and stream processors.
Software, architectures and operating systems for all types of parallel computers may be considered, including multicores, GPU accelerators, FPGA reconfigurable systems, high-end machines and cloud computing.
Section 3: Applications
The application of parallel computing to solve all types of business, industrial, scientific, and engineering problems using high-performance computing technologies, in particular:
* Applications of high-end machines including exascale
* Applications of multicore / manycore processors
* GPU-based applications
* Cloud and Grid computing applications.
Most viewed papers (last 30 days)
- OpenCL Performance Evaluation on Modern Multi Core CPUs
- JPEG-GPU:: a GPGPU Implementation of JPEG Core Coding Systems
- Surface Reconstruction from Scattered Point via RBF Interpolation on GPU
- Parallelization of the Ant Colony Optimization for the Shortest Path Problem using OpenMP and CUDA
- Enabling OS Research by Inferring Interactions in the Black-Box GPU Stack
- CLgrep: A Parallel String Matching Tool
- Rapid Computation of Sodium Bioscales Using GPU-Accelerated Image Reconstruction
- Using GPU Simulation to Accurately Fit to the Power-Law Distribution
- OCLoptimizer: An Iterative Optimization Tool for OpenCL
- 3DES ECB Optimized for Massively Parallel CUDA GPU Architecture
Parallel GPU-accelerated Recursion-based Generators of Pseudorandom Numbers
Optimizing a Biomedical Imaging Orientation Score Framework
Accelerating Computer Vision Algorithms Using OpenCL on Mobile GPU - A Case Study
Speeding up Large-Scale Point-in-Polygon Test Based Spatial Join on GPUs
Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods
OCLoptimizer: An Iterative Optimization Tool for OpenCL
A CUDA-Based Cooperative Evolutionary Multi-Swarm Optimization Applied to Engineering Problems
Implementations of the FFT algorithm on GPU
Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling
CUSIMANN: An optimized simulated annealing software for GPUs
May 18-21, 2014
March 17-19, 2014
June 26, 2013 9:00 AM - 10:00 AM PDT
June 4, 2013 10:00 AM - 11:00 AM PDT
June 20, 2013 9:00 AM - 10:00 AM PDT
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
- 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
- HDD: 2TB, Raid-0
- OS: OpenSUSE 11.4
- SDK: AMD APP SDK 2.8
- 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
- HDD: 2TB, Raid-0
- OS: OpenSUSE 12.2
- SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8
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.