Category: Amazon EC2

Today more than 200,000 volunteers around the world are helping accelerate research toward COVID-19 therapies—by walking away from their computers. That’s because of a concept called distributed computing, which allows anyone with a home computer, laptop, or virtual machine to contribute computing power to a common cause. This month, nonprofit Folding@home has started sharing one of the world’s largest public protein simulation databases as an AWS Open Data Set so that researchers around the world can easily access this data to speed up the search for therapies for COVID-19.

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.

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.

Your workloads have different characteristics and may have evolved over time, making it challenging to support them with high-performing, scalable and low-cost computing. That’s why we’ve added new Amazon Elastic Compute Cloud (EC2) instances that come with the latest processor technology, to help you optimize your workloads even more and save.

CDSM Interactive Solutions Ltd (CDSM), founded in 1998, is a multi-faceted EdTech company that provides bespoke e-learning services and Thinqi, a next generation learning management system (LMS), to the state education sector. We spent some time with two of CDSM’s more senior colleagues, Steve Finch, Head of Marketing, and Darren Wallace, Chief Technical Officer, both of whom have been with CDSM since the early 2000s.

The University of Nottingham has a history dating back to 1881, and while the university is now global with campuses in China and Malaysia, its flagship campus remains in the UK. Today, the university’s research efforts span nearly every discipline. One current project is Crossbow, a new, open source software project conceived and developed by Dr. Christian Suess, a research fellow at The University of Nottingham in conjunction with principal investigator Prof. Charlie Laughton, professor of computational pharmaceutical science. Crossbow is based out of the school of pharmacy at the University of Nottingham, where there is a particular interest in researching the design of new medicines using computer simulations of drugs and proteins.

Scientists at NC State University’s North Carolina Institute for Climate Studies (NCICS) work with large datasets and complex computational analysis. Traditionally, they did their work using on-premises computational resources. As different projects were stretching the limits of those systems, NCICS decided to explore cloud computing. As part of the Amazon Sustainability Data Initiative, we invited Jessica Mathews, Jared Rennie, and Tom Maycock to share what they learned from using AWS for climate research. As they considered exploring the cloud to support their work, the idea of leaving the comfort of the local environment was a bit scary. And they had questions: How much will it cost? What does it take to deploy processing to the cloud? Will it be faster? Will the results match what they were getting with their own systems? Here is their story and what they learned.