Exploring Summit: The newest intelligent supercomputer

Summit, the world’s fastest supercomputer running at Oak Ridge National Laboratory (ORNL), was designed from the ground up to be flexible and to support a wide range of scientific and engineering workloads. In addition to traditional simulation workloads, Summit is well suited to analysis and AI/ML workloads – it is described as “the world’s first AI supercomputer”. The use of standard components and software makes it easy to port existing applications to Summit as well as develop new applications. As pointed out by Buddy Bland, Project Director for the ORNL Leadership Computing Facility, Summit lets users bring their codes to the machine quickly, thanks to the standard software environment provided by Red Hat Enterprise Linux (RHEL).

Summit’s system is built using “fat node” building block concept, where each identically configured node is a powerful IBM Power System AC922 server which is interconnected with others via high-bandwidth dual rail Mellanox infiniband fabric, for a combined cluster of roughly 4,600 nodes. Each node in the system has:

SUMMIT SUPERCOMPUTER NODE COMPOSITION

The result is a system with excellent CPU compute capabilities, plenty of memory to hold data, high performance local storage, and massive communications bandwidth. Additionally, prominent use of graphical processing units (GPU) from Nvidia at the node architecture level provides robust acceleration platform for artificial intelligence (AI) and other workloads. All of this is achieved using standard hardware components, standard software components, and standard interfaces.

So why is workload acceleration so important? In the past, hardware accelerators such as vector processors and array processors were exotic technologies used for esoteric applications. In today’s systems, hardware accelerators are mainstream in the form of GPUs. GPUs can be used for everything from visualization to number crunching to database acceleration, and are omnipresent across the hardware landscape, existing in desktops, traditional servers, supercomputers, and everything in-between, including cloud instances . And the standard unifying component across these configurations is Red Hat Enterprise Linux, the operating system and software development environment supporting hardware, applications, and users across variety of environments at scale.

The breadth of scientific disciplines targeted by Summit can be seen in the list of applications included in the early science program. To help drive optimal use of the full system as soon as it was available, ORNL identified a set of research projects that were given access to small subsets of the full Summit system while Summit was being built. This enabled the applications to be ported to the Summit architecture, optimized for Summit, and be ready to scale out to the full system as soon as it was available. These early applications include astrophysics, materials science, systems biology, cancer research, and AI/ML.

Machine learning (ML) is a great example of a workload that stresses systems: it needs compute power, I/O, and memory to handle data. It needs massive number crunching for training, which is handled by GPUs. All of that requires an enormous amount of electrical power to run. The Summit system is not only flexible and versatile in the way it can handle workloads, it also withstands one of the biggest challenges of today’s supercomputers – excessive power consumption. Besides being the fastest supercomputer on the planet, it is equally significant that Summit performs well on the Green500 list – a supercomputer measurement of speed and efficiency which puts a premium on energy-efficient performance for sustainable supercomputing. Summit comes in at #1 in its category and #5 overall on this list, a very strong performance.

In summary, the fastest supercomputer in the world supports diverse application requirements, driven by simulation, big data, and AI/ML, employs the latest processor, acceleration and interconnect technologies from IBM, Nvidia and Mellanox, respectively, and shows unprecedented power efficiency for that scale of machines. Critical to the success of this truly versatile system is Linux, in Red Hat Enterprise Linux, as the glue that brings everything together and allows us to interact with this modern marvel.

ARMing IoT with Linaro LITE

Linaro has announced a new project focused on IoT – LITE, or Linaro IoT and Embedded. This project will focus on developing core technology to be used in IoT devices and gateways.

Linaro is a consortium focused on the Linux ecosystem for ARM based systems — see www.linaro.org for details. Much of their work to date has been focused on Android phones and tablets. Active development efforts include server and networking as well as Digital Home. The Digital Home project focuses on set-top boxes and home gateways. Linaro’s goal is to avoid fragmentation of the ARM ecosystem by providing a common foundation that can be used to build a wide range of value-added applications.

LITE extends existing Linaro projects by addressing both

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OpenLMI @ Red Hat Summit 2014

OpenLMI will be represented at the upcoming Red Hat Summit, which is being held in San Francisco from April 14-17.

Stephen Gallagher and I will be giving a talk on OpenLMI, the new Linux Management Infrastructure, on Tuesday, April 15, at 10:40am. This talk will provide an overview of OpenLMI, cover its functional capabilities, and demonstrate using the LMIShell CLI and Scripts to accomplish common management tasks.

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Managing Linux with OpenLMI

Managing Linux servers requires a degree of expertise. We need to do a better job of enabling mid-level system administrators with a background on other systems to manage Linux.

Existing management tools address a variety of needs. Red Hat Satellite Server is excellent for provisioning hardware, managing subscriptions, and handling patches and updates. Configuration management tools such as Puppet are great for putting systems into a known state, especially when you have many identical or near identical systems. The challenge is dealing with systems that need substantial customization and with fine grain control of individual systems.

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