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Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. Department of Energy's Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities-building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software. Members of the Interoperable Design of Extreme-scale Application Software project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This article discusses how these synergistic activities are advancing scientific discovery-mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

The development of scientific software—a cornerstone of long-term collaboration and scientific progress—parallels the development of other types of software but still poses distinct challenges, especially in high-performance computing. Although web searches yield numerous resources on software engineering, there is still a scarcity specifically for scientific software development. This article introduces the Better Scientific Software site (https://bssw.io), a platform that hosts a community of researchers, developers, and practitioners who share their experiences and insights on scientific software development. Since 2017, this collaborative hub has gained traction within the scientific computing community, attracting a growing number of readers and contributors eager to share ideas and elevate their software development practices. In sharing the BSSw.io site's story, we hope to encourage further growth of the BSSw.io community through both readership and contributors, with a long-term goal of fostering culture change by increasing emphasis on best practices in scientific software. [ABSTRACT FROM AUTHOR]

Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. We describe some of the presentations and discussion from the mini symposium in this article.

The U.S. Department of Energy (DOE) has been a long-standing leader in driving advances in science and technology through advanced computing. However, DOE laboratories are currently facing urgent workforce challenges, particularly in terms of underrepresentation from key communities, including people of color, women, persons with disabilities, and first-generation scholars. This paper introduces the work carried out as part of the Exascale Computing Project (ECP) Broadening Participation Initiative, which aims to address workforce challenges through a lens that considers the distinct needs and culture of high-performance computing (HPC). The work focuses on three main efforts: hosting Intro to HPC Bootcamps, expanding the Sustainable Research Pathways (SRP) internship and workforce development program, and establishing an HPC Workforce Development and Retention Action Group. The paper also highlights various workforce efforts throughout the computational science community and explores opportunities for future work aimed at broadening participation in HPC.

The Exascale Computing Project (ECP) was one of the largest open source scientific software development projects ever. It supported approximately 1000 staff from the U.S. Department of Energy laboratories, and university and industry partners. About 250 staff contributed to 70 scientific libraries and tools to support applications on multiple exascale computing systems that were also under development. Funded as a formal construction project, ECP was required to use earned-value management, based on milestones, and a key performance parameter system based, in part, on integrations. With accelerated delivery schedules and significant project risk, we also emphasized software quality using community policies, automated testing, and continuous integration. Software development kit teams provided cross-team collaboration, and products were delivered via E4S, a curated portfolio of libraries and tools. In this article, we discuss the organizational and management elements of ECP that enabled the delivery of libraries and tools, our lessons learned, and our next steps. [ABSTRACT FROM AUTHOR]

Rust and Julia are two modern languages aiming to advance computing in their unique way. Julia is a dynamic language striving to bridge the performance gap between dynamic languages like Python and statically typed ones like C/C++ while improving the ergonomics for scientific computing. Rust, on the other hand, is a statically typed language with a friendly compiler guiding developers to write reliable, correct, and concurrent code while ensuring memory safety and unlocking the highest performance. Both languages seem to be appealing to the scientific domain, especially because of their focus on performance. But choosing one depends on the type of your project. [ABSTRACT FROM AUTHOR]

This article offers perspective on quantum computing programming languages, as well as their emerging runtimes and algorithmic modalities. With the scientific high-performance computing (HPC) community as a target audience, we describe the current state of the art in the field, and outline programming paradigms for scientific workflows. One take-home message is that there is significant work required to first refine the notion of the quantum processing unit in order to integrate in the HPC environments. Programming for today's quantum computers is making significant strides toward modern HPC-compatible workflows, but key challenges still face the field. [ABSTRACT FROM AUTHOR]

The U.S. Department of Energy Computational Graduate Fellowship Program, celebrating 30 years of existence in 2021, is one of the most successful graduate fellowships in the world as well as one of the longest running programs in the U.S. Department of Energy. This article discusses the conception, early years and evolution of the fellowship over the past thirty years.

Scientific processes rely on software as an important tool for data acquisition, analysis, and discovery. Over the years, sustainable software development practices have made progress in being considered as an integral component of research. However, management of computation-based scientific studies is often left to individual researchers who design their computational experiments based on personal preferences and the nature of the study. We believe that the quality, efficiency, and reproducibility of computation-based scientific research can be improved by explicitly creating an execution environment that allows researchers to provide a clear record of traceability. This is particularly relevant to complex computational studies in high-performance computing (HPC) environments. In this article, we review the documentation required to maintain a comprehensive record of HPC computational experiments for reproducibility. We also provide an overview of tools and practices that we have developed to perform such studies around Flash-X, a multiphysics scientific software. [ABSTRACT FROM AUTHOR]

Translational software research bridges the gap between scientific innovations and practical applications, driving impactful societal advancements. However, developing such software is challenging due to interdisciplinary collaboration, technology adoption, and postfunding sustainability. This article presents the experiences and insights of the Scalable Adaptive Graphics Environment (SAGE) team, which has spent two decades developing translational, cross-disciplinary, collaboration tools to benefit computational science research. With a focus on SAGE and its next-generation iterations, we explore the inherent challenges in translational research, such as fostering cross-disciplinary collaboration, motivating technology adoption, and ensuring postfunding product sustainability. We also discuss the roles of funding agencies, policymakers, and academic institutions in promoting translational research. Although the journey is fraught with challenges, the societal impact and satisfaction derived from translational research underscore its significance in the broader scientific landscape. This article aims to encourage further conversation and the development of effective models for translational software projects. [ABSTRACT FROM AUTHOR]

The revitalized interest in ensuring that computer and information science and engineering (CISE) is a fair and equitable professional path is one of our grandest opportunities. As professionals who have championed diversity, equity, and inclusion over decades, we are pleased to offer four actions that you, our colleagues, can take to help. In this article, we spotlight the opportunities that exist within conferences to create fair and equitable participation for all members of the CISE profession. We focus on leveraging committee structures, developing new leaders, revising policies and procedures, and learning from successful innovations. Creating cultural and structural changes becomes possible when we take these steps together. [ABSTRACT FROM AUTHOR]

Computational models lie at the heart of computational science, yet few scientists have a clear idea of what a computational model actually is. Is it software? Or an algorithm? How does it relate to mathematical models? What are suitable languages or notations for expressing a computational model in the literature? And will AI make computational models obsolete? [ABSTRACT FROM AUTHOR]

Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the findable, accessible, interoperable, reusable (FAIR) principles and are a driving force for improving the sharing of data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. This small-scale exploratory study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of 19 interviews with seven women, 11 men, and one nonbinary individual from seven states and DC in the United States with a range of professional backgrounds and roles. [ABSTRACT FROM AUTHOR]

Interactive computing with Jupyter Notebooks has transformed the state of the art of scientific research computing. Users can perform a multitude of computational tasks in real time, including data cleansing, analysis, visualization, and postprocessing, with Jupyter Notebooks. Additionally, the ability to write and execute code and include supporting text and images in the same document has made it popular for use in scholarly articles and teaching. These capabilities complement the batch computing services provided by high-performance computing centers exposed through traditional science gateways. However, integrating Jupyter into science gateways and other advanced computing ecosystems introduces new challenges related to scalability, collaboration, and reproducibility. In this article, we discuss the Scalable Interactive Computing (Scinco) project, an open source platform for scalable, reproducible, interactive scientific computing designed to be run in academic computing centers and incorporated into science gateways. We describe the prime features, architecture, deployment choices, and challenges of Project Scinco. [ABSTRACT FROM AUTHOR]

Experimental and observational sciences have developed robust practices for conducting experiments, maintaining their instruments, and record keeping for provenance. Computational science has only recently begun to confront the issue of quality of their instrument, the software, and the credibility of their scientific output. Most of the available literature in software engineering relates to enterprise software. While it can inform practices in scientific software, adjustments are usually needed. From time to time quality conscious practitioners have published collections of best practices for scientific software. This article provides one more such list but with updated suggestions, motivated by the need to keep up with the rapid changes in the computing industry. [ABSTRACT FROM AUTHOR]

Numba is a game-changing compiler for high-performance computing with Python. It produces machine code that runs outside of the single-threaded Python interpreter, and that fully utilizes the resources of modern CPUs. This means support for parallel multithreading and auto-vectorization if available, as with compiled languages such as C++ or Fortran. In this article, we document our experience developing PyExaFMM, a multithreaded Numba implementation of the fast multipole method, an algorithm with a nonlinear data structure and a large amount of data organization. We find that designing performant Numba code for complex algorithms can be as challenging as writing in a compiled language. [ABSTRACT FROM AUTHOR]

Scientific codes are complex software systems. Their engineering involves various stakeholders using various computer languages for defining artifacts at different abstraction levels and for different purposes. In this article, we review the overall processes leading to the development of scientific software, and discuss the role of computer languages in the definition of the different artifacts. We provide guidelines to make informed decisions when the time comes to choose a computer language to develop scientific software. [ABSTRACT FROM AUTHOR]

Open-source research software plays a central role in accelerating advances in science and engineering. Its increasing significance, however, incentivizes malicious actors to attack that software and compromise the systems on which it runs, undermining the free and open exchange of trustworthy research codes. In the world of conventional software development, there has been a shift towards integrating security as early as possible in the development process to guard against malicious activity. Given the potential risks at hand, developers of research software must consider how to do the same across the research software lifecycle. This editorial argues for the need to unite diverse forms of expertise in scientific computing and software security to address these challenges and outlines a roadmap for future work in this space. [ABSTRACT FROM AUTHOR]

We review the work of Klaus Hasselmann, one of three recipients of the Nobel Prize in Physics 2021, from the perspective of computational science and engineering (CSE). In addition to highlighting Hasselmann's extensive contributions to climate science, we shine a light on his groundbreaking work in ocean surface wave dynamics and prediction, which preceded his career in climate research. Early on, Hasselmann also gained a strong interest in elementary particle physics, which led him to develop, in his spare time, a unified theory of particles and fields, and which we outline here. With this review we hope to entice computational scientists to delve deeper into Hasselmann's extraordinarily broad work at the interface of climate physics, mathematics, and scientific computing, and to appreciate the central role that CSE continues to play in climate research. [ABSTRACT FROM AUTHOR]

Python is a widely used language in scientific computing. When the goal is high performance, however, Python lags far behind low-level languages such as C and Fortran. To support applications that stress performance, Python needs to access the full capabilities of modern CPUs. That means support for parallel multithreading. In this article, we describe PyOMP, a system that enables OpenMP in Python. Programmers write code in Python with OpenMP, Numba generates code that compiles to LLVM, and the resulting programs run with performance that approaches that from code written with C and OpenMP. In this article, we provide an update on the PyOMP project and explain how to install it and use it to write parallel multithreaded code in Python. [ABSTRACT FROM AUTHOR]

Quantum computers promise to unlock new capabilities in areas ranging from the study of physical systems to cryptography. The field of quantum computing is built on the solid foundations of computational science that the Department of Energy Computational Science Graduate Fellowship continues to build. In this special research retrospective, I discuss my path in quantum computing research that receiving this award made possible. Starting with an introduction to quantum computing, I discuss how variational methods came to be popular on quantum computing hardware today, and the need to mitigate errors. The setting of variational algorithms facilitates a connection between quantum computers and the theory of machine learning, leading to discovery of phenomena, such as barren plateaus in the training landscape. This connection has allowed us to better understand the role of data, and how data can elevate machine learning models above traditional computation. [ABSTRACT FROM AUTHOR]

The increasingly diverse ecosystem of high-performance architectures and programming models presents a mounting challenge for programmers seeking to accelerate scientific computing applications. Code generation offers a promising solution, transforming a simple and general representation of computations into lower level, hardware-specialized and -optimized code. We describe the philosophy set forth by the Python package Pystella, a high-performance framework built upon such tools to solve partial differential equations on structured grids. A hierarchy of abstractions provides increasingly expressive interfaces for specifying physical systems and algorithms. We present an example application from cosmology, using finite-difference methods to solve coupled hyperbolic partial differential equations on (multiple) GPUs. Such an approach may enable a broad range of domain scientists to make efficient use of increasingly heterogeneous computational resources while mitigating the drastic effort and expertise nominally required to do so. [ABSTRACT FROM AUTHOR]

The processor industry has been facing challenges for several years. This is due to both technical challenges and changing market conditions. The outcome of these challenges was energy-efficient, multicore processors. This article examines the energy efficiency of several generations of Intel and ARM processors and investigates how architectural changes influenced power consumption. We were able to compare the Intel and ARM processors on an equal footing and could place our work in the context of previous work done in this regard, especially on Intel processors. We found that while the static energy consumption trended downwards, architectural changes in especially the mobile processors resulted in increased dynamic energy use. We also observed that their dynamic energy usage was less favorable even though the ARM processors at idle consumed a fraction of the energy of the Intel processors. [ABSTRACT FROM AUTHOR]

Rich user interfaces like Jupyter have the potential to make interacting with a supercomputer easier and more productive, consequently attracting new kinds of users and helping to expand the application of supercomputing to new science domains. For the scientist user, the ideal rich user interface delivers a familiar, responsive, introspective, modular, and customizable platform upon which to build, run, capture, document, re-run, and share analysis workflows. From the provider or system administrator perspective, such a platform would also be easy to configure, deploy securely, update, customize, and support. Jupyter checks most if not all of these boxes. But from the perspective of leadership computing organizations that provide supercomputing power to users, such a platform should also make the unique features of a supercomputer center more accessible to users and more composable with high-performance computing (HPC) workflows. Project Jupyter’s (https://jupyter.org/about) core design philosophy of extensibility, abstraction, and agnostic deployment has allowed HPC centers like National Energy Research Scientific Computing Center to bring in advanced supercomputing capabilities that can extend the interactive notebook environment. This has enabled a rich scientific discovery platform, particularly for experimental facility data analysis and machine learning problems. [ABSTRACT FROM AUTHOR]

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: 1) interactive computing; 2) computational narratives; and 3) the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in earth and climate science. [ABSTRACT FROM AUTHOR]

Black high school (HS) boys are an untapped source for initiatives in broadening participation in computer science (CS). This article describes a five week program that employs active and problem-based learning activities to teach tech innovation and entrepreneurship to local teens in the Washington, DC, USA area. Called #WatchMeCode, the program's objective is to build black HS boys' confidence in coding, communicating their programming skills, and rebuilding their thoughts about computer science. The program uses a constructivist pedagogical approach allowing students to build their understanding of computer science and technology through Internet-search-based history lessons, podcasts, and presentations by other black male tech entrepreneurs. Additionally, students ventured out into their community with hands-on design-thinking and customer discovery skills to observe their environment, its issues/problems, and developed new technology with their unique perspectives in mind. Results showed that participants became interested and confident in computer science by creating their own tech innovations. [ABSTRACT FROM AUTHOR]

This article identifies and analyzes computer science educators' culturally responsive approaches to teaching an electronic-textiles curricular unit in their high school classes. By examining surveys, interviews, and weekly reflections from 17 teachers, this qualitative study reports how learning about electronic-textiles, and then teaching this curricular unit to students, enabled teachers to better support their learners through empathy, relationships, and a new appreciation for students' home and cultural knowledge. We find that educators honored expertise not typically valued in computer science, brokered rich, authentic interactions with students in and outside of class, strengthened school-home connections, and uplifted students who had not felt successful in computing environments before. [ABSTRACT FROM AUTHOR]

A grand challenge of the computer science (CS) for all education movement is the preparation of thousands of teachers with high quality, accessible professional development (PD) that has evidence of improving teacher knowledge and pedagogical practices necessary to support the learning needs of diverse groups of students. While regional PD programs can provide in-person learning opportunities, geographic and time constraints often inhibit participation. This article shares findings from an online PD program modified from the existing in-person exploring computer science PD program to provide teachers a facilitated online learning community model to support their first year teaching the course. The findings from this study have implications for future directions in the CS education field, indicating that this model of online PD, heavily based on shared experience among participants, can increase CS teachers' confidence in adapting and delivering lessons designed to be engaging and accessible to all students. [ABSTRACT FROM AUTHOR]

There is a saying, "Everything changes, but nothing changes." We are realizing a rapid technological revolution in the development, deployment, and application of computing technologies within every discipline and every sector of society. Yet, our ability to respond to the well-documented need for a large, diverse, computationally literate workforce remains a challenge. We summarize our 35 years of lessons learned for preparing the workforce that can inform efforts to address this challenge. We have pursued a multiprong approach to reach instructors, researchers, professionals, and students on a national scale. Our efforts in scaling up and sustaining activities range from teaching computational thinking through imparting HPC skills. We have been able to scale up these activities through community efforts to share, cooperate, and collaborate. The potential for providing life-long learning to everyone wishing to expand their computational knowledge and skills is greater than any organization can achieve on its own. [ABSTRACT FROM AUTHOR]

Momentum exists in the physics community for integrating computation into the undergraduate curriculum. One of many benefits would be preparation for computational research. Our investigation poses the question of which computational skills might be best learned in the curriculum (prior to research) versus during research. Based on a survey of computational physicists, we present evidence that many relevant skills are developed naturally in a research context while others stand out as best learned in advance. [ABSTRACT FROM AUTHOR]

This special issue is intended to inform the scientific computing community about recent advances and the current state of the art in software and data citation. Initial work has been done elsewhere to define standards and principles for software and data citation, and the basic required infrastructure is now in place. The challenge now is to adopt these practices. As their uptake increases, we believe that our professional culture will increasingly encourage production and sharing of software and data, leading to better and more reproducible and reusable results. The articles in this special issue discuss how software, data, and related digital objects are cited and otherwise mentioned in publications today, how to best cite them to make them accessible in the future, and what we can learn from the citations. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on performance portability for advanced architectures. This is a very timely topic as we continue our journey to exascale computers. The leader on the Top 500 list today is the Fujitsu Fugaku computer in Japan, using a modified ARM architecture and 512-bit scalable vector extension (SVE) instructions. In the USA, the National Energy Research Scientific Computing Center (NERSC) has just dedicated Perlmutter, named for the Berkeley Nobel Laureate Saul Perlmutter. Perlmutter, built by Cray/HPE, contains both CPU-only and CPU/GPU nodes. The CPUs are produced by AMD and the GPUs come from NVIDIA. Frontier at Oak Ridge National Laboratory and El Capitan at Lawrence Livermore National Laboratory will contain new AMD GPUs, and Aurora at Argonne National Laboratory will feature new GPUs from Intel. Los Alamos National Laboratory's next-generation machine Crossroads will use Intel CPUs with high-bandwidth memory and AVX-512 vector instructions. Optimizing code for three different GPUs is a daunting challenge for application programmers. Further, there are still many computers that do not feature GPUs, and most computational scientists would not be eager to totally abandon such machines. [ABSTRACT FROM AUTHOR]

Component-based programming is a programming paradigm that eases software reuse but has yet to be widely adopted in scientific computing. We propose to embed component frameworks inside high-performance languages directly to improve flexibility compared to the literature. We present this approach through the example of a high-performance Bayesian inference application. [ABSTRACT FROM AUTHOR]

This document is a list of references and contact information for individuals involved in the field of exascale computing. It includes citations for articles on topics such as exascale computing in the United States, GPU programming, and the potential of exascale computing. The document also provides contact information for Richard Gerber, Steven Gottlieb, Michael A. Heroux, and Lois Curfman McInnes, who are experts in the field. [Extracted from the article]

This document is a list of references related to the topic of sustainable computing. It includes citations for various resources such as research papers, documentation, and programming languages like Rust and Julia. The document also provides contact information for Mohamad Bitar, a researcher working on his master's thesis in Computational Geometry. The references cover a range of topics including securing programming languages, performance tips, data visualization, and scientific computing. [Extracted from the article]

This special issue presents experience reports on computational education in various subjects of science and engineering. They include descriptions of early and current general courses at undergraduate and early graduate levels, as well as some computational exercises for specific courses. General computer science courses were intentionally excluded, because our emphasis is on applications of computers to science and engineering subjects. [ABSTRACT FROM AUTHOR]

Among the top challenges of reproducible computational science are the following: 1) creation, curation, usage, and publication of research software; 2) acceptance, adoption, and standardization of open-science practices; and 3) misalignment with academic incentive structures and institutional processes for career progression. I will mainly address the first two here, proposing a praxis of reproducible computational science. [ABSTRACT FROM AUTHOR]

In recent years, high-performance computing and powerful supercomputers have become staples in many areas of academia and industry. The author introduces the concept of shared memory programming in the context of solving the heat equation, which will allow the exploration of several finite difference and parallelization schemes. [ABSTRACT FROM AUTHOR]

An industry-led research team at the US Department of Energys Argonne National Laboratory recently conducted a computationally guided combustion system optimization on the IBM Blue Gene/Q Mira supercomputer. The team used a high-fidelity simulation approach to optimize the fuel spray and combustion bowl geometry of a heavy-duty diesel engine using a gasoline-like fuel. The accelerated simulation time allowed the team to evaluate an unprecedented number of design variations and improve the production design using a new fuel. [ABSTRACT FROM AUTHOR]

The National Strategic Computing Initiative (NSCI) creates a framework for partnerships among government, industry, and academia to advance the use of high-performance computing. The National Science Foundations Office of Advanced Cyberinfrastructure furthers this mission through the instantiation of two new software institutes. Although the program predates NSCI, The Science Gateways Community Institute and the Molecular Sciences Software Institute advance NSCIs objectives by acting as hubs of excellence, serving broad communities and creating software and workforce ecosystems. [ABSTRACT FROM AUTHOR]

Simulating the hydrogen ingestion flash in asymptotic giant branch stars is discussed as an illustration of a computational science research problem demanding high performance computation at scale. The relation of this work to the National Strategic Computing Initiatives objectives is discussed. [ABSTRACT FROM AUTHOR]

Many scientific software developers have applied software engineering practices in their work in recent years. Agile methods are gaining increased interest from both industry and academia, including scientific application domains. Test-driven development (TDD) and refactoring practices are critical to the success of agile methods. Although many scientific projects employ agile practices, the effect of TDD on scientific software development remains unknown and should thus be investigated. The authors investigated the effects of using TDD to develop scientific software in a high-performance computing environment, finding both advantages and disadvantages. In particular, they observed that developers face problems with writing unit tests and with a lack of experience with software engineering practices. [ABSTRACT FROM AUTHOR]

The US National Oceanic and Atmospheric Administration Air Research Laboratorys HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory) model uses a hybrid Lagrangian and Eulerian calculation method to compute particle dispersion and deposition simulations as well as air parcel paths (trajectories), forward or backward in time. This model is used worldwide in a variety of scientific contexts. The author presents the first Python package designed from the ground up to facilitate and expedite HYSPLIT trajectory analysis workflows by providing an intuitive and efficient API for generating, inspecting, and plotting trajectory paths and data. PySPLIT enables fully reproducible workflows, with orders of magnitude superior efficiency compared to what was previously possible with HYSPLIT alone, and leverages the capabilities of the scientific Python ecosystem and matplotlib to generate reproducible, publication-quality figures. [ABSTRACT FROM AUTHOR]

A fundamental problem of Einsteins theory of classical general relativity is the existence of singularities such as the big bang. All known laws of physics end at these boundaries of classical space-time. Thanks to recent developments in quantum gravity, supercomputers are now playing an important role in understanding the resolution of big bang and black hole singularities. Using supercomputers, explorations of the very genesis of space and time from quantum geometry are revealing a novel picture of what lies beyond classical singularities and the new physics of the birth of our universe. [ABSTRACT FROM AUTHOR]

Open data and open source software might be part of the solution to sciences reproducibility crisis, but they are insufficient to guarantee reproducibility. Requiring minimal end-user expertise, the encapsulator system creates a time capsule with reproducible code in a self-contained computational environment. encapsulator provides end users with a fully featured desktop environment for reproducible research. [ABSTRACT FROM AUTHOR]

The authors present quantum molecular design, a novel cost-saving automated framework for de novo computational drug design. This technology not only addresses many of the challenges faced in the computer-aided drug design field by using highly accurate physics-based models, it also dramatically lowers costs by leveraging an AI heuristic search algorithm with targeted chemical space. [ABSTRACT FROM AUTHOR]