GPIUTMD stands for Graphic Processors at Isfahan University of Technology for Many-particle Dynamics. It performs general-purpose many-particle dynamic simulations on a single workstation, taking advantage of NVIDIA GPUs to attain a level of performance equivalent to thousands of cores on a fast cluster. Flexible and configurable, GPIUTMD is currently being used for all atom and coarse-grained molecular dynamics simulations of nano-materials, glasses, and surfactants; dissipative particle dynamics simulations (DPD) of polymers; and crystallization of metals using EAM potentials. GPIUTMD 0.9.6 adds many new features. Highlights include:

Abstract:

We propose a new transparent checkpoint/restart (CPR) tool, named CheCL, for high performance and dependable GPU computing. CheCL can perform CPR on an OpenCL application program without any modification and recompilation of its code. A conventional checkpointing system fails to checkpoint a process if the process uses OpenCL. Therefore, in CheCL, every API call is forwarded to another process called an API proxy, and the API proxy invokes the API function; two processes, an application process and an API proxy, are launched for an OpenCL application. In this case, as the application process is not an OpenCL process but a standard process, it can be safely checkpointed. While CheCL intercepts all API calls, it records the information necessary for restoring OpenCL objects. The application process does not hold any OpenCL handles, but CheCL handles to keep such information. Those handles are automatically converted to OpenCL handles and then passed to API functions. Upon restart, OpenCL objects are automatically restored based on the recorded information. This paper demonstrates the feasibility of transparent checkpointing of OpenCL programs including MPI applications, and quantitatively evaluates the runtime overheads. It is also discussed that CheCL can enable process migration of OpenCL applications among distinct nodes, and among different kinds of compute devices such as a CPU and a GPU.

(Hiroyuki Takizawa, Kentaro Koyama, Katuto Sato, Kazuhiko Komatsu, and Hiroaki Kobayashi: “CheCL: Transparent Checkpointing and Process Migration of OpenCL Applications”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS11), 2011. [PDF])

Abstract:

The calculation of radial distribution functions (RDFs) from molecular dynamics trajectory data is a common and computationally expensive analysis task. The rate limiting step in the calculation of the RDF is building a histogram of the distance between atom pairs in each trajectory frame. Here we present an implementation of this histogramming scheme for multiple graphics processing units (GPUs). The algorithm features a tiling scheme to maximize the reuse of data at the fastest levels of the GPU’s memory hierarchy and dynamic load balancing to allow high performance on heterogeneous configurations of GPUs. Several versions of the RDF algorithm are presented, utilizing the specific hardware features found on different generations of GPUs. We take advantage of larger shared memory and atomic memory operations available on state-of-the-art GPUs to accelerate the code significantly. The use of atomic memory operations allows the fast, limited-capacity on-chip memory to be used much more efficiently, resulting in a fivefold increase in performance compared to the version of the algorithm without atomic operations. The ultimate version of the algorithm running in parallel on four NVIDIA GeForce GTX 480 (Fermi) GPUs was found to be 92 times faster than a multithreaded implementation running on an Intel Xeon 5550 CPU. On this multi-GPU hardware, the RDF between two selections of 1,000,000 atoms each can be calculated in 26.9 s per frame. The multi-GPU RDF algorithms described here are implemented in VMD, a widely used and freely available software package for molecular dynamics visualization and analysis.

(Benjamin G. Levine, John E. Stone, and Axel Kohlmeyer: “Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units — Radial Distribution Function Histogramming”, Journal of Computational Physics, 230(9):3556-3569, 2011. [DOI: 10.1016/j.jcp.2011.01.048])

Aiming at easing OpenCL development on Linux, Wendell Rodrigues has created wizards to start OpenCL application projects using the SDKs from NVIDiA, AMD or Intel, based on Anjuta DevStudio on Linux. Refer to his blog for details and downloads.

Computing in Science & Engineering seeks articles for a May/June 2012 special issue focusing on the use of GPUs in science and engineering applications. Contributions covering all aspects of using GPUs for solving challenging computational science problems are welcome. Of special interest are articles presenting results of porting efforts of large-scale scientific applications on large-scale GPU-based high-performance computers. See the full call for papers for complete details.

The next GPU Technology Conference, GTC 2012, will be held May 14-17 2012, in San Jose, California. Los Alamos National Laboratory’s co-located Accelerated High Performance Computing (HPC) Symposium will move to the same week, as will the new InPar 2012 academic conference, geared towards providing a first-tier venue for peer-reviewed publications in the field of innovative parallel computing.

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:

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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:

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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/