“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.”
“We have been working on developing a number of tools that enable users to quantify power and performance in both software and hardware, and then design a more efficient system. We can also utilize the tools to predict the performance of a piece of software on a system that may not be available or does not yet exist – the aim is to take the guesswork away from novel system design.”
“At the Minnesota Supercomputing Institute we are exploring ways to provide the immediacy and flexibility of interactive computing within the batch-scheduled, tightly controlled world of traditional cluster supercomputing. As Jupyter Notebook has gained in popularity, the steps needed to use it within such an environment have proven to be a barrier to entry even as increasingly powerful Python tools have developed to take advantage of large computational resources. JupyterHub to the rescue! Except out of the box, it doesn’t know anything about resource types, job submission, and so on. We developed BatchSpawner and friends as a general JupyterHub backend for batch-scheduled environments. In this talk I will walk through how we have deployed JupyterHub to provide a user-friendly gateway to interactive supercomputing.”
In this video from ISC 2016, Dave Sundstrom from Hewlett Packard Enterprise describes the newly enhanced HPE Software Stack for High Performance Computing. “The HPE Core HPC Software Stack is a complete software set for the creation, optimization, and running of HPC applications. It includes development tools, runtime libraries, a workload scheduler, and cluster management, integrated and validated by Hewlett Packard Enterprise into a single software set. Core HPC Stack uses the included HPC Cluster Setup Tool to simplify and speed the installation of an HPC cluster built with HPE servers.”
In this Intel Chip Chat podcast with Allyson Klein, Cray CTO Steve Scott describes the collaboration between Cray and Intel on the Intel Xeon Phi Processor for supercomputer integration. Steve highlights that Cray chose to implement the new Intel Xeon Phi Processor for its supercomputers because of the potential to support a diverse array of customer needs and deliver the best performance per application. He emphasizes that Cray software tools are key to optimizing Intel Xeon Phi processor performance at the system level.