Each machine generation provides a fresh challenge to U.S. computer manufacturers—from the racks to the processors to the networking to the I/O system. Similarly, fulfilling the science potential of each new computing architecture requires significant changes to today’s software. The initiative is, and will continue to be, guided by pioneering visionaries in the mathematics and computational science community, stewarded by the DOE’s Office of Science, and operated at the cutting edge.
But while people have been using supercomputers to solve big problems for years, the capabilities of the machines that will soon begin rolling out in national labs around the country will be brand new.
Researchers will be able to run a greater diversity of workloads, including machine learning and data intensive tasks, in addition to traditional simulations. Providing the data science software “stack”—the high-level programming languages, frameworks, and I/O middleware that are conventional toolkits—at exascale, is a major effort in deploying Aurora.
Aurora will feature several technological innovations, including a revolutionary I/O system—the Distributed Asynchronous Object Store (DAOS)—to support new types of workloads. Aurora will be built on a future generation of Intel® Xeon® Scalable processor accelerated by Intel’s Xe compute architecture. Cray Slingshot™ fabric and Shasta™ platform will form the backbone of the system. Programming techniques already in use on current systems will apply directly to Aurora. The system will be highly optimized across multiple dimensions that are key to success in simulation, data, and learning applications.
As the world’s data-centric workloads become more diverse, so do architectures that process that data. Intel’s breadth of architectures span scalar (CPU), vector (GPU), matrix (AI) and spatial (FPGA). These architectures, often referred to at Intel with the acronym SVMS, require an efficient software programming model to deliver performance. oneAPI addresses this with ease-of-use and performance, while eliminating the need to maintain separate code bases, multiple programming languages, and different tools and workflows.
We are identifying ways that you can begin preparing your code for the Argonne Leadership Computing Facility’s forthcoming exascale system. Please check out the links provided on this page to get started. We’ll continue to keep update as more information can be made available to the public.
For links to early adopter information, please see alcf.anl.gov/support-center/aurora
The Aurora Early Science Program will prepare key applications for Aurora’s scale and architecture, and will solidify libraries and infrastructure to pave the way for other production applications to run on the system.
The program has selected 15 projects, proposed by investigator-led teams from universities and national labs and covering a wide range of scientific areas and numerical methods.
In collaboration with experts from Intel and Cray, ALCF staff will train the teams on the Aurora hardware design and how to program it. This includes not only code migration and optimization, but also mapping the complex workflows of data-focused, deep learning, and crosscutting applications. The facility will publish technical reports that detail the techniques used to prepare the applications for the new system.
In addition to fostering application readiness for the future supercomputer, the Early Science Program allows researchers to pursue innovative computational science campaigns not possible on today’s leadership-class supercomputers.