Exascale Computing Project Releases New Version of Extreme-Scale HPC Scientific Software Stack

From remaining, Mike Heroux, Sandia Countrywide Lab Sameer Shende, Univ. of Oregon Todd Gamblin, Lawrence Livermore National Lab

The Extraordinary-scale Scientific Computer software Stack (E4S) high-effectiveness computing (HPC) computer software ecosystem—an ongoing wide selection of software package abilities continuously created to tackle rising scientific requires for the US Department of Vitality community—recently launched variation 22.02.

E4S, which commenced in the drop of 2018, is aimed at accelerating the enhancement, deployment, and use of HPC computer software, thus lowering the boundaries for HPC customers.

Spack serves as a meta-construct device to identify the program dependencies of the E4S items and help the generation of a recursively built tree of items.

“E4S delivers Spack recipes for putting in software program on bare-metallic methods aided by a Spack binary create cache,” reported Sameer Shende, ECP E4S project direct and research associate professor and the director of the Functionality Investigate Lab, OACISS, College of Oregon. “It gives a array of containers that span Spack-enabled base illustrations or photos with assistance for NVIDIA GPUs on x86_64, ppc64le, and aarch64 architectures. On x86_64, GPUs from all a few vendors—NVIDIA, Intel, and AMD—are supported. This will allow a user to create personalized, compact, container pictures starting up from E4S foundation images. A full-featured E4S image that supports visualization instruments such as Go to, ParaView, and TAU  has 100 E4S products installed utilizing Spack and includes GPU runtimes together with CUDA, NVHPC, ROCm, and oneAPI as very well as AI/ML offers this sort of as TensorFlow and PyTorch that aid GPUs. This provides builders a vary of platform-unique choices to deploy their HPC and AI/ML apps on GPUs very easily.”

The two main contributors to E4S are ST and the Co-style and design spot within just the ECP Application Enhancement portfolio. ST products have a tendency to encompass extra established ways for how math libraries and instruments can be built available and usable by numerous various groups for establishing, deploying, and functioning scientific purposes on HPC platforms. ECP Co-style and design entails rising designs that epitomize operation that can be utilised by lots of unique purposes.

E4S encapsulates ST and Co-style and design products and solutions with other software growth resources this sort of as the PyTorch machine understanding framework, the TensorFlow resource platform for machine studying, and the distributed memory Horovod, which sits on prime of the other two applications.

“Our groups inside Software program Technology and in Co-style are performing on new attributes for the exascale computing platforms,” Heroux mentioned. “Now, that operate is being finished in really unique environments where by we’re on the early-access programs. We’re doing the job shoulder to shoulder with our Software Advancement teams to enhancement new capabilities. That things is not in E4S nowadays it will be, but it’s not there but.”

The E4S groups have also established capabilities that they have acquired about in current months that are now out there only in the launch branch of impartial software program item advancement teams.

For case in point, the capability that the hypre numerical computer software deal for windfarm modeling and simulation capability used by the ECP ExaWind challenge is available only locally, isolated in the improvement stage of an future version and the current launch of hypre. These kinds of a new functionality is included to the program package’s repository in GitHub soon after bug fixes and other launch-oriented responsibilities are performed, and 4 to six months later, the E4S project adds it to the E4S portfolio.

“You can assume of E4S as being the final shipping motor vehicle of hardened and definitely robust abilities that are provided by ECP as reusable libraries and instruments,” Heroux reported.

Developed to handle the hottest scientific problems, E4S is analyzed for interoperability and portability to several computing architectures, and supports GPUs from NVIDIA, AMD, and Intel in a one distribution. E4S will more and more involve synthetic intelligence (AI) instruments and libraries that are essential especially to use to the scientific trouble sets.

Scientific edge computing, which will involve tons of instrumentation and incoming facts that have to be managed and then assimilated for further scientific being familiar with, will also come to be portion of E4S, as will quantum technology on its more growth. Scientific application products—libraries and tools—will be vital to entry the basic abilities afforded by quantum equipment.

Consumers functioning on computing units that lack the latest innovations can also profit from E4S. For case in point, the most hardened and sturdy versions of the E4S libraries and applications are at this time offered and significant for use on clusters with CPU nodes or on laptops. Lots of E4S developers use a laptop as their key application improvement natural environment and compile and operate their program on that equipment.

“ECP has supplied us with an bold established of ambitions that essential us to do the job with each other across labs, universities, market associates to deliver program to exascale applications and to the broader scientific neighborhood,” Heroux explained. “It is a major plenty of and massive enough—and I would have to say, gnarly enough—problem that pressured us and incentivized us to work jointly in this sort of portfolio way. We have been developing as a DOE scientific group a lot of these merchandise these are not commonly brand name-new items. They are brand name-new capabilities that are concentrating on the exascale programs.”

The enduring legacy of the E4S job will have two aspects: its usability across unique GPU platforms and its portfolio approach.

“Because we’ve experienced to supply these solutions in a pretty aggressive timeline, we have to perform jointly to make guaranteed that we’re not introducing bugs as we develop new options and carrying out that as a portfolio is significantly additional powerful and successful than getting every single person item group go to these early-accessibility units, go to the exascale programs independently and having to go through that entire system all on their individual,” Heroux said. “As a collective portfolio—and even at a finer scale, the SDK [software development kit] stage of integration, wherever we consider factors like the math libraries alongside one another as a group—these two more levels of collaboration make it possible for us to accelerate and make additional strong the selection of software that we’re giving.”

The high quality of E4S as a neighborhood system is its price proposition to researchers. Users can set their purposes on best of E4S and tap into its immense set of capabilities, and contributors to E4S can make their own libraries or instruments obtainable to their person communities. E4S is also portion of the broader Spack ecosystem, so its deals can be made use of in conjunction with much more than 6,000 other application packages in Spack.

The 100 software program packages in E4S count on above 400 dependencies from Spack.

“Spack advantages significantly from the excellent and hardening efforts of the E4S team—they be certain that a main set of incredibly elaborate packages are integrated and perform reliably for Spack customers,” reported Todd Gamblin, Spack direct. “We also use the E4S deals to examination Spack by itself. With each transform to Spack, we operate checks to be certain that E4S builds continue to work. The two endeavours are extremely leveraged—the ongoing work of Spack’s virtually 1,000 contributors ensures that E4S’s dependencies are up to date and get the job done reliably, and the E4S workforce makes sure that the latest ECP application is obtainable to the broader community.”

Heroux elaborated on the uniqueness of E4S.

“It’s a good established of applications, loads of performance that you cannot locate in a very similar collection any where else on the earth,” Heroux explained. “It supplies some quite pleasant build capabilities via Spack, and in truth, if you start off making use of our stuff and you want to make what’s known as a create cache, you can also cut down the amount of money of time you shell out developing the libraries and applications in E4S since we’ll keep a cached binary variation of a former establish, and you can seize that fairly than having to rebuild it. We see create instances likely down from, say, a day-duration style of run to a several hrs. … We also provide E4S as a container, and it supports all of the GPU architectures that we’re focusing on in these containers. Plus, E4S can be mounted on cloud nodes—for case in point, from AWS, Amazon’s cloud—and we’re doing the job with other cloud suppliers so that you can rely on E4S becoming available it becomes section of the ecosystem, fairly than obtaining to develop it by yourself all the time.”

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resource: Scott Gibson, Exascale Computing Challenge