AMD announced a GPGPU coding competition, called AMD OpenCL Coding Competition. The first phase of the competition is an open innovation challenge that requires the use of the AMD APP SDK and OpenCL. The competition is heating up with the highest registration for a TopCoder innovation challenge to date. It’s not too late to sign up and show off your ideas! If you submit your abstract before June 30th you will get feedback from AMD, otherwise you will have up until the deadline to submit your OpenCL innovation challenge submission.

Phase two of the competition will be an OpenCL algorithm optimization match that will start later in September. Read more about it in this AMD blog.

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])

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.

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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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.

Heterogeneous computing is moving into the mainstream, and a broader range of applications are already on the way. As the provider of world-class CPUs, GPUs, and APUs, AMD offers unique insight into these technologies and how they interoperate. We’ve been working with industry and academia partners to help advance real-world use of these technologies, and to understand the opportunities that lie ahead. It’s time to share what we’ve learned so far.

With tutorials, hands-on labs, and sessions that span a range of topics from HPC to multimedia, you’ll have the opportunity to expand your view of what heterogeneous computing currently offers and where it is going. You’ll hear from industry innovators and academic pioneers who are exploring different ways of approaching problems, and utilizing new paradigms in computing to help identify solutions. You’ll meet AMD experts with deep knowledge of hardware architectures and the software techniques that best leverage those platforms. And you’ll connect with other software professionals who share your passion for the future of technology.

Learn more at developer.amd.com/afds.

Abstract:

We present a computational method of coupling average interpolating wavelets with high-order finite volume schemes and its implementation on heterogeneous computer architectures for the simulation of multiphase compressible flows. The method is implemented to take advantage of the parallel computing capabilities of emerging heterogeneous multicore/multi-GPU architectures. A highly efficient parallel implementation is achieved by introducing the concept of wavelet blocks, exploiting the task-based parallelism for CPU cores, and by managing asynchronously an array of GPUs by means of OpenCL. We investigate the comparative accuracy of the GPU and CPU based simulations and analyze their discrepancy for two-dimensional simulations of shock-bubble interaction and Richtmeyer–Meshkov instability. The results indicate that the accuracy of the GPU/CPU heterogeneous solver is competitive with the one that uses exclusively the CPU cores. We report the performance improvements by employing up to 12 cores and 6 GPUs compared to the single-core execution. For the simulation of the shock-bubble interaction at Mach 3 with two million grid points, we observe a 100-fold speedup for the heterogeneous part and an overall speedup of 34.

(Rossinelli D., Hejazialhosseini B., Spampinato D., Koumoutsakos P.: “Multicore/Multi-GPU Accelerated Simulations of Multiphase Compressible Flows Using Wavelet Adapted Grids”, SIAM Journal of Scientific Computing 33:512-540, 2011 [DOI])

OpenCL Studio combines OpenCL and OpenGL into a single integrated development environment for high performance computing. The feature rich editor, interactive scripting language and extensible plug-in architecture support the rapid development of complex parallel algorithms and accompanying visualization. The first production version of OpenCL Studio including instructional videos and demo applications are available at www.opencldev.com.

A simple tool for off-line compilation of OpenCL kernel code, called “OpenCLcc”,  is now available at

http://code.google.com/p/openclcc/

OpenCLcc takes a text file with the OpenCL kernel code as input and calls the OpenCL run-time to compile it, echoing errors to the console.