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We present Generic Refinement Types: a way to write modular higher-order specifications that abstract invariants over function contracts, while preserving automatic SMT-decidable verification. We show how generic refinements let us write a variety of modular higher-order specifications, including specifications for Rust's traits which abstract over the concrete refinements that hold for different trait implementations. We formalize generic refinements in a core calculus and show how to synthesize the generic instantiations algorithmically at usage sites via a combination of syntactic unification and constraint solving. We give semantics to generic refinements via the intuition that they correspond to ghost parameters, and we formalize this intuition via a type-preserving translation into the polymorphic contract calculus to establish the soundness of generic refinements. Finally, we evaluate generic refinements by implementing them in Flux and using it for two case studies. First, we show how generic refinements let us write modular specifications for Rust's vector indexing API that lets us statically verify the bounds safety of a variety of vector-manipulating benchmarks from the literature. Second, we use generic refinements to refine Rust's Diesel ORM library to track the semantics of the database queries issued by client applications, and hence, statically enforce data-dependent access-control policies in several database-backed web applications.

As GenAI becomes embedded in developer toolchains and practices, and routine code is increasingly generated, human creativity will be increasingly important for generating competitive advantage. This paper uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising five connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, and society. can be affected.

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure (ES) methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green’s function based many-body perturbation theory, wave-function based and stochastic ES approaches, relativistic effects and semiempirical ES theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the ES, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for ES based modeling from the vantage point of industry environments. Overall, the field of ES software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, the A-Lab, with around 3500 samples synthesized over 1.5 years.

Context: The adoption of remote work models and the global nature of software projects have significantly transformed collaboration and communication within the software development industry. Remote meetings have become a common means of collaboration for software development teams. Objective: This study seeks to enhance our understanding of remote meeting practices in software teams. It identifies the benefits of remote meetings, the problems associated with remote meetings, tools used to facilitate remote meetings and provides recommended good practices. The study employs a systematic literature review to assist remote teams in improving their meeting practices and identifying areas for future research. Methods: We conducted a systematic literature review that involved searching multiple databases and employing quantitative and qualitative analysis techniques on the identified set of studies to answer our research questions. Results: The search yielded 30 papers offering valuable insights into remote meeting practices in software teams. Remote meetings offer advantages over traditional in-person meetings such as increased effectiveness and ease of attendance. However, challenges exist such as technological issues, ineffective collaboration, and reduced team socialization. Identified good practices to mitigate the challenges include inserting breaks in longer meetings, catch-up time at the start of meeting, communicating goals in advance of the meeting, and pre-recording demos. Conclusion: The study explored remote meetings in software teams. We identified advantages that remote meetings have in comparison to in-person meetings, challenges to remote meetings, and good practices along with supportive tooling. While the practices help in promoting effective meetings, additional research is required to further improve remote meeting experiences. Researching topics such as investigating different types of meetings common to software development teams along with the potential for novel tools to better support meetings will help identify additional practices and tools that can benefit remote teams.

Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using highperformance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees. However, effective use of these distributed structures in analysis and visualization requires subsequent computation of geometric properties and branch decomposition to support contour extraction and exploration. In this work, we introduce distributed algorithms for augmentation, hypersweeps, and branch decomposition that enable parallel computation of geometric properties, and support the use of distributed contour trees as query structures for scientific exploration. We evaluate the parallel performance of these algorithms and apply them to identify and extract important contours for scientific visualization.

In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments.

Here we present the UCI Fluxtron, a cost-effective multi-enclosure dynamic gas exchange system that provides an adequate level of control of the experimental conditions for investigating biosphere-atmosphere exchange of trace gases. We focus on the hardware and software used to monitor, control, and record the air flows, temperatures, and valve switching, and on the software that processes the collected data to calculate the exchange flux of trace gases. We provide the detailed list of commercial materials used and also the software code developed for the Fluxtron, so that similar dynamic enclosure systems can be quickly adopted by interested researchers. Furthermore, the two software components -Fluxtron Control and Fluxtron Process- work independently of each other, thus being highly adaptable for other experimental designs. Beyond plants, the same experimental setup can be applied to the study of trace gas exchange by animals, microbes, soil, or any materials that can be enclosed in a suitable container.

We propose a rich foundational theory of typed data streams and stream transformers, motivated by two high-level goals. First, the type of a stream should be able to express complex sequential patterns of events over time. And second, it should describe the internal parallel structure of the stream, to support deterministic stream processing on parallel and distributed systems. To these ends, we introduce stream types, with operators capturing sequential composition, parallel composition, and iteration, plus a core calculus λST of transformers over typed streams that naturally supports a number of common streaming idioms, including punctuation, windowing, and parallel partitioning, as first-class constructions. λST exploits a Curry-Howard-like correspondence with an ordered variant of the Logic of Bunched Implication to program with streams compositionally and uses Brzozowski-style derivatives to enable an incremental, prefix-based operational semantics. To illustrate the programming style supported by the rich types of λST, we present a number of examples written in Delta, a prototype high-level language design based on λST.

I/O performance monitoring tools such as Darshan and Recorder collect I/O-related metrics on production systems and help understand the applications' behavior. However, some gaps prevent end-users from seeing the whole picture when it comes to detecting and drilling down to the root causes of I/O performance slowdowns and where those problems originate. These gaps arise from limitations in the available metrics, their collection strategy, and the lack of translation to actionable items that could advise on optimizations. This paper highlights such gaps and proposes solutions to drill down to the source code level to pinpoint the root causes of I/O bottlenecks scientific applications face by relying on cross-layer analysis combining multiple performance metrics related to I/O software layers. We demonstrate with two real applications how metrics collected in high-level libraries (which are closer to the data models used by an application), enhanced by source-code insights and natural language translations, can help streamline the understanding of I/O behavior and provide guidance to end-users, developers, and supercomputing facilities on how to improve I/O performance. Using this cross-layer analysis and the heuristic recommendations, we attained up to 6.9× speedup from run-as-is executions.

Over the academic year 2022-23, we discussed the teaching of software performance engineering with more than a dozen faculty across North America and beyond. Our outreach was centered on research-focused faculty with an existing interest in this course material. These discussions revealed an enthusiasm for making software performance engineering a more prominent part of a curriculum for computer scientists and engineers. Here, we discuss how MIT's longstanding efforts in this area may serve as a launching point for community development of a software performance engineering curriculum, challenges in and solutions for providing the necessary infrastructure to universities, and future directions.