As cloud computing grows,  the types of computational hardware available in the cloud are diversifying. Field Programmable Gate Arrays (FPGAs) are a relatively new addition to high-performance computing in the cloud, with the ability to accelerate a range of different applications, and the flexibility to offer different cloud computing models. A new and growing configuration is to have the FPGAs directly connected to the network and thus reduce the latency in delivering data to processing elements. We survey the state-of-the-art in FPGAs in the cloud and present the Open Cloud Testbed (OCT), a testbed for research and experimentation into new cloud platforms, which includes network-attached FPGAs in the cloud.  

Skills in computer science can have great value in studying, doing and communicating physics. As educators, we asked ourselves how to make students aware of that, and how to offer them a new and appealing approach to physics. We also wondered how to increase students' engagement, participation and understanding, particularly when lessons are delivered online. Thus, we began a project to develop study materials for an introductory course in physics for computer science and we chose to use open source software. The materials are organized as a set of Jupyter notebooks hosted on an open GitHub repository. The notebooks deal with fundamental concepts of physics related to everyday life, offering examples of what can be done with a few lines of Python code. In the notebooks we propose activities to observe phenomena, describe problems, experiment, acquire and analyze data, and model the behavior of systems. The contents are suitable for undergraduates, high-school students, and evergreen students. We have used the materials for lectures, guided laboratory activities and presentations to freshmen and younger students, and we plan to continue with this project.

Introduction Most people tend to shy away from conversations around mental health. This is due to the stigma that is rooted in the long mistreatment of people with mental illness. In this article, I aim to bring conversations about mental health to the forefront and advocate for a change in culture so that the field of computing better supports the mental health of the community and is more inclusive of people with mental illness.

At GTC 2022 Nvidia announced a new product family that aims to cover from small enterprise workloads through exascale HPC and trillion-parameter AI models. This column highlights the most interesting features of their new Hopper GPU and Grace CPU computer chips and the Hopper product family. We also discuss some of the history behind Nvidia technologies and their most useful features for computational scientists such as the Hopper DPX dynamic programming  instruction set, increased number of SMs, and FP 8 tensor core availability. Also included are descriptions of the new Hopper Clustered SMs architecture and updated NVSwitch technologies that integrates their new ARM-based Grace CPU. 

The is a research and education center that supports software development in the . One of ’s core objectives is to provide education and training for the next generation of computational researchers. Education targets various career stages and skill levels through its live workshops, online resources, and software fellowship program. Education focuses its efforts within four areas: programming and software development, and , faculty and curriculum development, and the software fellowship program. This article delineates educational efforts at the , overall goals, and resources that can be useful to researchers in the computational molecular sciences.

The paper Reproducibility of the First Image of a Black Hole in the Galaxy M87 from the Event Horizon Telescope (EHT) Collaboration by Patel et al. aims to reproduce the M87* imaging results presented by the Event Horizon Telescope (EHT) from observations made in 2017, as well as provide documentation and computational environments that allow others to reproduce the results more easily in the future. The paper gives an overview of the procedures used to generate the images, as well as clearly lays out what software and documentation is available by the collaboration and what is missing. The authors are able to qualitatively reproduce the results using scripts provided by the EHT collaboration, and quantitatively reproduce results that are similar to those presented by the collaboration. I recommend publication after addressing the following comments.

There is a lot of focus on how Quantum Computing as an accelerator differs from other traditional HPC resources, including accelerators like GPUs and FPGAs. In classical computing, how to design the interfaces that connect the different layers of the software stack, from the applications and its high-level programming language description through compilers, schedulers, down to the hardware, and gate-level, has been critical. Likewise, quantum computing's interfaces enable the access to quantum technology as a viable accelerator. From the ideation of the quantum application to the manipulation of the quantum chip, each interface has its challenges. In this column feature, we discuss the structure of this set of quantum interfaces, their many similarities to the traditional HPC compilation stack, and how these interfaces impact the potential of quantum computers as HPC accelerators.

Our biology affects how we interact with the world, including how we learn new knowledge and respond to challenges. This article explores the impact of neurochemicals in our brain on learning and explains how to leverage our biology to improve education and problem-solving, focusing on computing education. Within this context, the article particularly examines the role of failure while learning. Learning, especially in technical fields, includes making errors on the path to success. While these errors trigger the necessary neurochemical conditions for rapid learning, these failures can also be demotivating. To gain the benefits of failure while mitigating its negative consequences, this article recommends evidence-based behavioral strategies for making the best out of failing while learning and designing for failure in learning environments.

This article demonstrates how scientists and engineers can use modern AI (artificial intelligence) tools such as ChatGPT and GitHub Copilot to learn computer programming skills that are relevant for their jobs. It begins by summarizing common ways that AI tools can already help people learn programming in general. Then it presents six learning opportunities that are catered to the needs of scientists and engineers, including using AI tools to 1) create customized programming tutorials for one’s own domain of work, 2) learn complex data visualization libraries, 3) learn to refactor exploratory code into more maintainable software, 4) learn about inherited legacy code, 5) learn new programming languages on-demand within the context of one’s workflow, and 6) question the assumptions that one’s scientific code is making. Taken together, these opportunities point toward a future where AI can help scientists and engineers learn programming on-demand within the context of their existing real-world workflows.