Author: Angel Pizarro

Today, more than 290,000 researchers in France are working to provide better support and care for patients through modern medical treatment. To fulfill their mission, these researchers must be equipped with powerful tools. At AWS, we believe that technology has a critical role to play in medical research. Why? Because technology can take advantage of the significant amount of data generated in the healthcare system and in the research community to enable opportunities for more accurate diagnoses, and better treatments for many existing and future diseases. To support elite research in France, we are proud to be a sponsor of two French organizations:  Gustave Roussy and Sorbonne University. AWS is providing them with the computing power and machine learning technologies needed to accelerate cancer research and develop a treatment for COVID-19.

Training users on how to use high performance computing resources — and the data that comes out as a result of those analyses — is an essential function of most research organizations. Having a robust, scalable, and easy-to-use platform for on-site and remote training is becoming a requirement for creating a community around your research mission. A great example of this comes from the NOAA National Weather Service Warning Decision Training Division (WDTD), which develops and delivers training on the integrated elements of the hazardous weather warning process within a National Weather Service (NWS) forecast office. In collaboration with the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies (OU/CIMMS), WDTD conducts its flagship course, the Radar and Applications Course (RAC), for forecasters issuing warnings for flash floods, severe thunderstorms, and tornadoes. Trainees learn the warning process, the science and application of conceptual models, and technical aspects of analyzing radar and other weather data in the Advanced Weather Interactive Processing System (AWIPS). 

A customer asked us what is the difference between the CancelJob and TerminateJob API calls in AWS Batch. This post provides an overview of AWS Batch job states, and how these two API calls effect the job requests that you have submitted.

This post was written by Dario La Porta, AWS Professional Services Senior Consultant for HPC. Customers across a wide range of industries such as energy, life sciences, and design and engineering are facing challenges in managing and analyzing their data. Not only is the amount and velocity of data increasing, but so is the complexity and […]

Large-scale data analysis usually involves some multi-step process where the output of one job acts as the input of subsequent jobs. Customers using AWS Batch for data analysis want a simple and performant storage solution to share with and between jobs. We are excited to announce that customers can now use Amazon Elastic File System (Amazon […]

This post is written by Deepak Singh, Vice President of Compute Services. At AWS, we love working with customers to solve their toughest challenges. High performance computing (HPC) is one of those challenges that pushes against the boundaries of AWS performance at scale. HPC is also a personal interest of mine, as I came to […]