Tag: HPC

In the spring of 2019, environmental modelers at the Lake Michigan Air Directors Consortium (LADCO) had a new problem to solve. Emerging research on air pollution along the shores of the Great Lakes in the United States showed that to properly simulate the pollution episodes in the region we needed to apply our models at a finer spatial granularity than the computational capacity of our in-house HPC cluster could handle. The LADCO modelers turned to AWS ParallelCluster to access the HPC resources needed to do this modeling faster and scale for our member states.

Governments, like the state government of California, are in the midst of a transition to a new way of delivering vital information, services and programs using technology and the cloud. Government organizations are adopting approaches pioneered in the technology industry, including user-centered design, agile development, data science, modular contracting, and the use of modern technology platforms. Many of these governments, like the state of California, are using Amazon Web Services (AWS) to respond quickly and scale to battle unprecedented challenges, like COVID-19, and help them quickly make decisions about how to protect their constituents.

From speeding the time to science to accelerating the delivery of critical citizen services, our customers are migrating to the AWS Cloud to successfully deliver on their missions. Our current environment has pushed us all into new ways of learning, working, and even socializing. And the cloud has made these recent changes and innovations possible. These customer examples can offer a playbook and inspiration for how organizations can leverage the cloud to innovate quickly and deliver on behalf of citizens.

The Water Institute of the Gulf runs its storm surge and wave analysis models on Amazon Web Services (AWS)—a task that sometimes requires large bursts of compute power. These models are critical in forecasting hurricane storm surge event (like Hurricane Laura in August 2020), evaluating flood risk for the Louisiana and other coastal states, helping governments prepare for future conditions, and managing the coast proactively.

Research computing has come a long way from the mainframes of the 1960s. At the recent Practice and Experience in Advanced Research Computing (PEARC) conference, I noted four emerging themes that underscore how the field continues to evolve.

In 2014, the Wall Lab at Stanford University sought to answer one of the most pressing questions in neuroscience: What genes influence autism spectrum disorder (ASD)? According to the Centers for Disease Control (CDC), this neurodevelopmental disorder affects roughly one in 54 children in America and is on the rise—nearly tripling since 1992. In the lab’s study of ASD genetics, they chose the cloud—and a unique experimental approach—to speed the time to science.

If you are a scientific researcher, you are likely more interested in getting your research done than in the computational resources that you use to do it. You may think about ways to continue your research remotely with the rise in remote work. Did you know the cloud and Amazon Web Services (AWS) can accelerate your research and time to science? Here are five ways.

The concept of a COVID-19 High Performance Computing (HPC) Consortium emerged from a roundtable discussion at the White House in March and included input from industry, government, and academic leaders. Following the announcement of the consortium, AWS has been collaborating with teams on a growing number of projects to provide cloud computing resources from AWS. I want to share three early learnings and insights into some of the innovative projects on which we are collaborating with the world’s leading researchers.

Mahmoud Khaled, a PhD student at TUM and a research assistant at LMU, researches how to improve safety-critical systems that require large amounts of compute power. Using AWS, Khaled’s research project, pFaces, accelerates parallelized algorithms and controls computational complexity to speed the time to science. His project findings introduce a new way to design and deploy verified control software for safety-critical systems, such as autonomous vehicles.

Dr. Nicholas Chilton and his research group at The University of Manchester’s Department of Chemistry in the School of Natural Sciences investigate the magnetic properties of molecules for high-density storage, quantum computing, and applications like MRI contrast agents. He turned to the cloud when the university’s onsite HPC cluster couldn’t provide the high-throughput compute power needed to answer his research questions.