Artificial intelligence (AI) technologies have fundamentally transformed numerical-based high-performance computing (HPC) applications with data-driven approaches and endeavored to address existing challenges, e.g. high computational intensity, in various scientific domains. In this study, we explore the scenarios of coupling HPC and AI (HPC-AI) in the context of emerging scientific applications, presenting a novel methodology that incorporates three patterns of coupling: surrogate, directive, and coordinate. Each pattern exemplifies a distinct coupling strategy, AI-driven prerequisite, and typical HPC-AI ensembles. Through case studies in materials science, we demonstrate the application and effectiveness of these patterns. The study highlights technical challenges, performance improvements, and implementation details, providing insight into promising perspectives of HPC-AI coupling. The proposed coupling patterns are applicable not only to materials science but also to other scientific domains, offering valuable guidance for future HPC-AI ensembles in scientific discovery.

As the world increasingly turns to renewable energy sources to power our homes and businesses, the need for high-performance computing (HPC) and artificial intelligence (AI) is also increasing. HPC and AI are used to model and predict complex phenomena, like weather patterns and climate change, as well as to optimize the design of renewable energy systems. The demand for HPC and AI is therefore increasing in many industries that are critical to the transition to a low-carbon economy. In addition, a great deal of research and development (R&D) has been put into play using these technologies, which are leading to breakthroughs that promise to change the way people live and work. With supercomputing technology in the limelight and companies focusing on enhancing their data centers' performance, it's easy to get caught up in the hype surrounding the new computer systems that boast high computing power. But a lot of people aren't sure where all of this is going or why it's such a big deal.

SmartSim gives businesses not only the ability to integrate modern AI methodology into their work but also leverages a new paradigm for rapid data communication at scale. Learn how SmartSim works and what opportunities it brings to businesses in every industry. Recently, interest in applying machine learning (ML) algorithms to improve scientific simulations has been increasing. That's exactly why we developed SmartSim. This open-source library enables the use of ML with existing traditional high-performance computing (HPC) simulations.

William Gropp (University of Illinois at Urbana-Champaign), Sujata Banerjee (VMware Research) and Ian Foster (University of Chicago) High Performance Computing (HPC), Artificial Intelligence (AI)/Machine Learning (ML), and Quantum Computing (QC) and communications offer immense opportunities for innovation and impact on society. Researchers in these areas depend on access to computing infrastructure, but these resources are in short supply and are typically siloed in support of their research communities, making it more difficult to pursue convergent and interdisciplinary research. Such research increasingly depends on complex workflows that require different resources for each stage. This paper argues that a more-holistic approach to computing infrastructure, one that recognizes both the convergence of some capabilities and the complementary capabilities from new computing approaches, be it commercial cloud to Quantum Computing, is needed to support computer science research. The types of infrastructure needed to support HPC and AI/ML share many features; GPU systems originally developed for HPC have become essential for ML, and those systems have further been optimized for ML, with features now being applied to HPC simulations.

HPC and AI in a phone booth: ScaleMatrix and Nvidia announced today at the SC19 conference in Denver a joint offering that puts up to 13 petaflops of Nvidia DGX-1 compute power in an air conditioned, water-cooled ScaleMatrix Dynamic Density Control (DDC) "clean room" cabinet. Built for modular deployments and designed for high-demand AI workloads, ScaleMatrix said its ruggedized cabinet can be erected "anywhere power and a roof exist," and it includes biometric security and fire suppression. At the high end of the product line is a composable SKU comprised of the Nvidia DGX-1 system, a single rack running at 42kW, containing 13 DGX-1 units and delivering 13 Pflops of throughput. Other configurations come with a DGX POD deployment, four DGX-2s, run at 43kW and deliver 8 Pflops of compute, the companies said. The units will be sold with storage and networking following DGX POD reference architecture designs, such as NetApp's ONTAP AI solution.

New York is always an exciting, energetic city to visit and ... was made even more so as I attended the (recent) 'HPC & AI on Wall Street' conference, which HPCWire are now championing. It was well worth the train ride from Boston and interesting to see the varied mix of attendees present and hear how HPC and AI is evolving in the world of finance. All sectors of the financial services industry (FSI) were represented at the conference from traditional, old school banks to venture capital firms, fintechs and hedge funds. AI adoptions varied from integrating machine learning into existing applications and processes to mining unearthly amounts of market data for a trading edge. My first lesson was there is a dichotomy between too much data volume and insufficient data context within this community. Don't worry the bank account and market value data is as solid as always.

History and economics – as if you could separate the two – are burgeoning with examples of products being developed for one task and then being used, perhaps after some tweaking, for an entirely new and usually unexpected task. History is also full of stories of technologies aimed squarely at a task that, for one reason or another, miss the mark even if it looks like they were right on target. Product substitution as a means of lowering costs and thereby making a technology more prevalent is one of the primary reasons that economies exist. Some people make money in the transformation, and others lose out, but the overall economy improves from the efficiency engendered in that change. So it is a net good, and if done right, there is some money left over to invest in something else entirely. Every once in a while, you get a product substitution working from two different angles, and you can get a whole bunch of different things converging on a technology.

Gaining an advantage in the global high-tech market is often the result of research and development (R&D) combined with execution and incremental improvement. Today's emerging cloud-based artificial intelligence (AI) platforms are a perfect example of R&D-fueled competition. With the cloud giants and many governments investing heavily in AI R&D, what's a mere multinational corporation to do? IBM spent the past few years restructuring to address the combination of "cognitive services" (AI-based services) and cloud platforms. During that time, IBM continued to invest in R&D, but focused their efforts on AI software, "as-a-service" initiatives and scale-out systems designed for delivering cloud-based analytics and AI services. As part of this transition, IBM opened its cloud infrastructure development to a much broader community and also partnered with other AI leaders like NVIDIA to provide high-performance AI systems.

This sponsored post from Lenovo's Bhushan Desam covers how new HPC tools like Lenovo's LiCO (Lenovo Intelligent Computing Orchestration) are working to address the growing popularity of AI and to simplify the convergence of HPC and AI. Artificial Intelligence (AI) is coming for your HPC cluster – and while there are no autonomous robots taking over the data center, some days it might feel that way to cluster administrators. The HPC cluster looks very attractive to the "outside world", particularly to those who will need performance beyond a single system or workstation. That is, until they try to use it and realize there is a learning curve they have to overcome. AI workloads are well suited for running on a cluster – but is your cluster management ready for AI users?

With the debut of the IBM POWER9-equipped Summit supercomputer, the world's smartest AI machine, IBM is putting our money where our mouth is when it comes to building data-centric systems. You see, it's no longer about which systems are the fastest, it's about systems that are designed to handle the massive amounts of data that AI and big data applications demand. Conversely, without the ability to ingest the volumes of data this system is capable of processing, the applications might not succeed, so that's why we embrace a data-centric approach. It all comes down to data. Of course, we're very proud of the reveal from the Top500 organization today that Summit and Sierra placed #1 and #3, respectively, on their LINPACK benchmark rankings, but speed is no longer the principle parameter to assess the value of these systems.