clpp is an OpenCL library of data-parallel algorithm primitives such as parallel prefix sum (“scan”), parallel sort and parallel reduction. Primitives such as these are important building blocks for a wide variety of data-parallel algorithms, including sorting, stream compaction, and building data structures such as trees and summed-area tables. For more information, visit http://code.google.com/p/clpp.
On June 28, 2011 StreamComputing will present a one-day course on OpenCL in Utrecht. The course covers general GPU computing principles and OpenCL specifics in a top-down fashion, including lectures and short lab sessions. Topics include:
The 2nd International Workshop on GPUs and Scientific Applications (GPUScA 2011) will be held on October 10, 2011, in conjunction with the International Conference on Parallel Architectures and Compilation Techniques (PACT 2011).
The goal of this workshop is to bring together GPU experts with computational science experts. The workshop addresses programming approaches and key techniques to leverage the computing power of GPUs. Experiences gained while adapting scientific codes to run efficiently on such architectures are welcome. The workshop solicits unpublished papers about research challenges and advances addressing porting of scientific codes and algorithms to GPU platforms.
Submission deadline: June 28, 2011. The topics of the workshop include but are not limited to:
A new GPU users group is being established in South Africa. The first event will be held June 9, 2011. For more information, see http://www.meetup.com/GPGPU-ZA/
A 2 day CUDA workshop will be held in Berlin from July 2-3, for developers who want to learn how to program and utilize the Graphics Processing Unit (GPU) using NVIDIA’s CUDA programming framework. No prior knowledge of parallel computing concepts is necessary, but some basic C/C++ knowledge will be required. More information is available at http://cuda.eventbrite.com.
The SoCal GPGPU group has a regular meeting once a month at UCLA, plus other occasional get togethers. Join the group, come to one of the talks, and tell them what you do with GPUs. More Info: http://www.meetup.com/SoCal-GPGPU-and-Commodity-Parallel-Programming-Group/
The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.
(Guillem Pratx & Lei Xing: “GPU computing in medical physics: A review”, Med. Phys., vol 38(5), pp. 2685-2698, May 2011. [DOI])
Many image processing applications use the histogramming algorithm, which fills a set of bins according to the frequency of occurrence of pixel values taken from an input image. Histogramming has been mapped on a GPU prior to this work. Although significant research effort has been spent in optimizing the mapping, we show that the performance and performance predictability of existing methods can still be improved.
In this paper, we present two novel histogramming methods, both achieving a higher performance and predictability than existing methods. We discuss performance limitations for both novel methods by exploring algorithm trade-offs.
The first novel method gives an average performance increase of 33% over existing methods for non-synthetic benchmarks. The second novel method gives an average performance increase of 56% over existing methods and guarantees to be fully data independent. While the second method is specifically designed for Fermi GPU architectures, the first method is also suitable for older architectures.
(Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal, Bart Mesman: “High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs”, GPGPU-4: Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units. [DOI] [Paper and Source Code])
You are cordially invited to attend the GPU Solutions to Multiscale Problems in Science and Engineering Workshop 2011 (GPU-SMP’2011). The workshop will be held in Dunhuang, Gansu, China July 18 – 21, 2011 (Monday through Thursday) with a reception on July 18th. This workshop will cover topics on GPU Solutions to Multiscale Problems in Science and Engineering, including high performance computing methods, advanced software realization and construction of computing environments, and investigations of the mainstream developing trend and key scientific problems of GPUs in computing and visualization technology. Dunhuang is a beautiful city with a long history in China. During the workshop, attendees will have the opportunity to access local attractions. Full details at http://gpu-smp2011.csp.escience.cn/
Facing the Multicore Challenge II – Conference for Young Scientists, will be held September 28-30, 2011, at Karlsruhe Institute of Technology (KIT), Germany
The conference focuses on topics of multi-/manycore and coprocessor technologies and the impact on computational science, day-to-day work, and for large-scale applications. The goal is to address and discuss current issues including mathematical modeling, numerical methods, design of parallel algorithms, aspects of microprocessor architecture, parallel programming languages, compilers, hardware-aware computing, heterogeneous platforms, emerging architectures, tools, performance tuning, and requirements for large-scale applications.
The conference places emphasis on the support and advancement of young scientists in an interdisciplinary environment.
You are cordially invited to submit a paper with unpublished and original work. Furthermore, ongoing research can be presented in a short talk or poster.