“The current versions of the codes use MPI and depend on finer and finer meshes for higher accuracy which are computationally demanding. To overcome the demands, the team has gained access to their state-of-the-art cluster equipped with POWER CPUs and Tesla P100 GPUs — and turning to OpenACC and machine learning to accelerate their science. This has allowed them to spend the least resources on programming, and effectively utilize available compute resources.”
“GPUs potentially offer exceptionally high memory bandwidth and performance for a wide range of applications. The challenge in utilizing such accelerators has been the difficulty in programming them. Enter GPU Hackathons; Our mentors come from national laboratories, universities and vendors, and besides having extensive experience in programming GPUs, many of them develop the GPU-capable compilers and help define standards such as OpenACC and OpenMP.”
“This talk reports efforts on refactoring and optimizing the climate and weather forecasting programs – CAM and WRF – on Sunway TaihuLight. To map the large code base to the millions of cores on the Sunway system, OpenACC-based refactoring was taken as the major approach, with source-to-source translator tools applied to exploit the most suitable parallelism for the CPE cluster and to fit the intermediate variable into the limited on-chip fast buffer.”
Today ORNL announced the full schedule of 2017 GPU Hackathons at multiple locations around the world. “The goal of each hackathon is for current or prospective user groups of large hybrid CPU-GPU systems to send teams of at least 3 developers along with either (1) a (potentially) scalable application that could benefit from GPU accelerators, or (2) an application running on accelerators that need optimization. There will be intensive mentoring during this 5-day hands-on workshop, with the goal that the teams leave with applications running on GPUs, or at least with a clear roadmap of how to get there.”
OpenACC is a directive based programming model that gives C/C++ and Fortran programmers the ability to write parallel programs simply by augmenting their code with pragmas. Pragmas are advisory messages that expose optimization, parallelization, and accelerator offload opportunities to the compiler so it can generate efficient parallel code for a variety of different target architectures including AMD and NVIDIA GPUs plus ARM, x86, Intel Xeon Phi, and IBM POWER processors.
Today Appentra Solutions announced that the company will participate in the Emerging Technologies Showcase at SC16. As an HPC startup, Appentra was selected for its Parallware technology, an LLVM-based software technology that assists in the parallelization of scientific codes with OpenMP and OpenACC. “The new Parallware Trainer is a great tool for providing support to parallel programmers on their daily work,” said Xavier Martorell, Parallel Programming Models Group Manager at Barcelona Supercomputing Center.
The OpenACC standards group today announced several major milestones including the addition of new member, the National Supercomputing Center in Wuxi, the adoption of OpenACC by several major HPC applications, the addition of support for new target platforms and expanded implementation
Two University of Wyoming graduate students earned a trip to the SC16 conference in November by virtue of winning the poster contest at the recent Rocky Mountain Advanced Computing Consortium (RMACC) High Performance Computing Symposium. “I hope to receive good exposure to the most recent advancements in the field of high-performance computing,” Kommera says.
Oak Ridge National Lab is hosting a 3-day GPU Mini-hackathon led by experts from the OLCF and Nvidia. The event takes place Nov. 1-3 in Knoxville, Tennessee. “General-purpose Graphics Processing Units (GPGPUs) potentially offer exceptionally high memory bandwidth and performance for a wide range of applications. The challenge in utilizing such accelerators has been the difficulty in programming them. This event will introduce you to GPU programming techniques.”
In this video from the 2016 Blue Waters Symposium, GPU Performance Nuggets – Carl Pearson and Simon Garcia De Gonzalo from the University of Illinois present: GPU Performance Nuggets. “In this talk, we introduce a pair of Nvidia performance tools available on Blue Waters. We discuss what the GPU memory hierarchy provides for your application. We then present a case study that explores if memory hierarchy optimization can go too far.”