The prevalency of cloud computing has changed the HPC landscape necessaiting HPC management tools that can manage and simplify complex enviornments in order to optimize flexibility and speed. Altair’s new solution PBS Cloud Manager makes it easy to build and manage HPC application stacks.
With Intel Scalable System Framework Architecture Specification and Reference Designs, the company is making it easier to accelerate the time to discovery through high-performance computing. The Reference Architectures (RAs) and Reference Designs take Intel Scalable System Framework to the next step—deploying it in ways that will allow users to confidently run their workloads and allow system builders to innovate and differentiate designs
Applications are now open for the annual SuperComputing Camp in Colombia. The five-day camp takes place Oct. 16-21 at CIBioFI at Universidad del Valle in Santiago de Cali.
Thomas Schulthess presented this talk at the MVAPICH User Group. “Implementation of exascale computing will be different in that application performance is supposed to play a central role in determining the system performance, rather than just considering floating point performance of the high-performance Linpack benchmark. This immediately raises the question as to what the yardstick will be, by which we measure progress towards exascale computing. I will discuss what type of performance improvements will be needed to reach kilometer-scale global climate and weather simulations. This challenge will probably require more than exascale performance.”
“This talk will discuss various system performance issues, and the methodologies, tools, and processes used to solve them. The focus is on single systems (any operating system), including single cloud instances, and quickly locating performance issues or exonerating the system. Many methodologies will be discussed, along with recommendations for their implementation, which may be as documented checklists of tools, or custom dashboards of supporting metrics. In general, you will learn to think differently about your systems, and how to ask better questions.”
In this video from the 4th Annual MVAPICH User Group, DK Panda from Ohio State University presents: Overview of the MVAPICH Project and Future Roadmap. “This talk will provide an overview of the MVAPICH project (past, present and future). Future roadmap and features for upcoming releases of the MVAPICH2 software family (including MVAPICH2-X, MVAPICH2-GDR, MVAPICH2-Virt, MVAPICH2-EA and MVAPICH2-MIC) will be presented. Current status and future plans for OSU INAM, OEMT and OMB will also be presented.”
“Spack is like an app store for HPC,” says Todd Gamblin, its creator and lead developer. “It’s a bit more complicated than that, but it simplifies life for users in a similar way. Spack allows users to easily find the packages they want, it automates the installation process, and it allows contributors to easily share their own build recipes with others.” Gamblin is a computer scientist in LLNL’s Center for Applied Scientific Computing and works with the Development Environment Group at Livermore Computing.
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.”
“Between 2011 and 2016, eight projects, with a total budget of more than €50 Million, were selected for this first push in the direction of the next- generation supercomputer: CRESTA, DEEP and DEEP-ER, EPiGRAM, EXA2CT, Mont- Blanc (I + II) and Numexas. The challenges they addressed in their projects were manifold: innovative approaches to algorithm and application development, system software, energy efficiency, tools and hardware design took centre stage.”
AMD’s motivation for developing these open-source GPU tools is based on an opportunity to remove the added complexity of proprietary programming frameworks to GPU application development. “If successful, these tools – or similar versions – could help to democratize GPU application development, removing the need for proprietary frameworks, which then makes the HPC accelerator market much more competitive for smaller players. For example, HPC users could potentially use these tools to convert CUDA code into C++ and then run it on an Intel Xeon co-processor.”