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The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

Scientific workflows are used to analyze large amounts of data. These workflows comprise numerous tasks, many of which are executed repeatedly, running the same custom program on different inputs. Users specify resource allocations for each task, which must be sufficient for all inputs to prevent task failures. As a result, task memory allocations tend to be overly conservative, wasting precious cluster resources, limiting overall parallelism, and increasing workflow makespan. In this paper, we first benchmark a state-of-the-art method on four real-life workflows from the nf-core workflow repository. This analysis reveals that certain assumptions underlying current prediction methods, which typically were evaluated only on simulated workflows, cannot generally be confirmed for real workflows and executions. We then present Ponder, a new online task-sizing strategy that considers and chooses between different methods to cater to different memory demand patterns. We implemented Ponder for Nextflow and made the code publicly available. In an experimental evaluation that also considers the impact of memory predictions on scheduling, Ponder improves Memory Allocation Quality on average by 71.0% and makespan by 21.8% in comparison to a state-of-the-art method. Moreover, Ponder produces 93.8% fewer task failures.

As the amount of available data continues to grow in fields as diverse as bioinformatics, physics, and remote sensing, the importance of scientific workflows in the design and implementation of reproducible data analysis pipelines increases. When developing workflows, resource requirements must be defined for each type of task in the workflow. Typically, task types vary widely in their computational demands because they are simply wrappers for arbitrary black-box analysis tools. Furthermore, the resource consumption for the same task type can vary considerably as well due to different inputs. Since underestimating memory resources leads to bottlenecks and task failures, workflow developers tend to overestimate memory resources. However, overprovisioning of memory wastes resources and limits cluster throughput. Addressing this problem, we propose Sizey, a novel online memory prediction method for workflow tasks. During workflow execution, Sizey simultaneously trains multiple machine learning models and then dynamically selects the best model for each workflow task. To evaluate the quality of the model, we introduce a novel resource allocation quality (RAQ) score based on memory prediction accuracy and efficiency. Sizey's prediction models are retrained and re-evaluated online during workflow execution, continuously incorporating metrics from completed tasks. Our evaluation with a prototype implementation of Sizey uses metrics from six real-world scientific workflows from the popular nf-core framework and shows a median reduction in memory waste over time of 24.68% compared to the respective best-performing state-of-the-art baseline., Comment: Paper accepted in 2024 IEEE International Conference on Cluster Computing (CLUSTER)

We develop the deformation theory of Calabi-Yau threefolds, by which we mean 3-dimensional complex manifolds with a nowhere-vanishing holomorphic 3-form, on manifolds with boundary. The boundary data is a closed, real 3-form on the 5-dimensional boundary. In the case of strongly pseudoconvex boundary, we obtain an analogue of Hitchin's local Torelli Theorem for compact manifolds, modulo a finite dimensional obstruction space, which we show is zero in many cases of interest.

Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters. However, implementing workflows is difficult due to the involvement of many black-box tools and the deep infrastructure stack necessary for their execution. Simultaneously, user-supporting tools are rare, and the number of available examples is much lower than in classical programming languages. To address these challenges, we investigate the efficiency of Large Language Models (LLMs), specifically ChatGPT, to support users when dealing with scientific workflows. We performed three user studies in two scientific domains to evaluate ChatGPT for comprehending, adapting, and extending workflows. Our results indicate that LLMs efficiently interpret workflows but achieve lower performance for exchanging components or purposeful workflow extensions. We characterize their limitations in these challenging scenarios and suggest future research directions.

Many resource management techniques for task scheduling, energy and carbon efficiency, and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime prediction models on historical data is often not feasible in practice as workflows, their input data, and the cluster infrastructure change. Online methods, on the other hand, which estimate task runtimes on specific machines while the workflow is running, have to cope with a lack of measurements during start-up. Frequently, scientific workflows are executed on heterogeneous infrastructures consisting of machines with different CPU, I/O, and memory configurations, further complicating predicting runtimes due to different task runtimes on different machine types. This paper presents Lotaru, a method for locally predicting the runtimes of scientific workflow tasks before they are executed on heterogeneous compute clusters. Crucially, our approach does not rely on historical data and copes with a lack of training data during the start-up. To this end, we use microbenchmarks, reduce the input data to quickly profile the workflow locally, and predict a task's runtime with a Bayesian linear regression based on the gathered data points from the local workflow execution and the microbenchmarks. Due to its Bayesian approach, Lotaru provides uncertainty estimates that can be used for advanced scheduling methods on distributed cluster infrastructures. In our evaluation with five real-world scientific workflows, our method outperforms two state-of-the-art runtime prediction baselines and decreases the absolute prediction error by more than 12.5%. In a second set of experiments, the prediction performance of our method, using the predicted runtimes for state-of-the-art scheduling, carbon reduction, and cost prediction, enables results close to those achieved with perfect prior knowledge of runtimes.

We study an intrinsic volume form defined on a pseudoconvex hypersurface in a complex Calabi-Yau manifold. We compute first and second variation formulae and discuss possible analogues of the affine isoperimetric inequality. In the last section of the paper we explore infinite dimensional aspects, including moment maps for diffeomorphism group actions.

Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying some logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VC can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behaviour (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a first sketch for VCs' implementation into existing DAW infrastructures.

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enabling the publication of workflows and their associated products according to the FAIR principles. This document reports on discussions and findings from the 2022 international edition of the Workflows Community Summit that took place on November 29 and 30, 2022.

Scientific workflow management systems (SWMSs) and resource managers together ensure that tasks are scheduled on provisioned resources so that all dependencies are obeyed, and some optimization goal, such as makespan minimization, is achieved. In practice, however, there is no clear separation of scheduling responsibilities between an SWMS and a resource manager because there exists no agreed-upon separation of concerns between their different components. This has two consequences. First, the lack of a standardized API to exchange scheduling information between SWMSs and resource managers hinders portability. It incurs costly adaptations when a component should be replaced by a different one (e.g., an SWMS with another SWMS on the same resource manager). Second, due to overlapping functionalities, current installations often actually have two schedulers, both making partial scheduling decisions under incomplete information, leading to suboptimal workflow scheduling. In this paper, we propose a simple REST interface between SWMSs and resource managers, which allows any SWMS to pass dynamic workflow information to a resource manager, enabling maximally informed scheduling decisions. We provide an implementation of this API as an example, using Nextflow as an SWMS and Kubernetes as a resource manager. Our experiments with nine real-world workflows show that this strategy reduces makespan by up to 25.1% and 10.8% on average compared to the standard Nextflow/Kubernetes configuration. Furthermore, a more widespread implementation of this API would enable leaner code bases, a simpler exchange of components of workflow systems, and a unified place to implement new scheduling algorithms.

Scientific workflows consist of thousands of highly parallelized tasks executed in a distributed environment involving many components. Automatic tracing and investigation of the components' and tasks' performance metrics, traces, and behavior are necessary to support the end user with a level of abstraction since the large amount of data cannot be analyzed manually. The execution and monitoring of scientific workflows involves many components, the cluster infrastructure, its resource manager, the workflow, and the workflow tasks. All components in such an execution environment access different monitoring metrics and provide metrics on different abstraction levels. The combination and analysis of observed metrics from different components and their interdependencies are still widely unregarded. We specify four different monitoring layers that can serve as an architectural blueprint for the monitoring responsibilities and the interactions of components in the scientific workflow execution context. We describe the different monitoring metrics subject to the four layers and how the layers interact. Finally, we examine five state-of-the-art scientific workflow management systems (SWMS) in order to assess which steps are needed to enable our four-layer-based approach., Comment: Paper accepted in 2022 IEEE International Conference on Big Data Workshop SCDM 2022

We consider the question if a five dimensional manifold can be embedded into a Calabi-Yau manifold of complex dimension three such that the real part of the holomorphic volume form induces a given closed 3-form on the 5-manifold. We define an open set of 3-forms in dimension five which we call strongly pseudoconvex, and show that for closed strongly pseudoconvex 3-forms the perturbative version of this embedding problem can be solved if a finite dimensional vector space of obstructions vanishes.

Scientific workflows typically comprise a multitude of different processing steps which often are executed in parallel on different partitions of the input data. These executions, in turn, must be scheduled on the compute nodes of the computational infrastructure at hand. This assignment is complicated by the facts that (a) tasks typically have highly heterogeneous resource requirements and (b) in many infrastructures, compute nodes offer highly heterogeneous resources. In consequence, predictions of the runtime of a given task on a given node, as required by many scheduling algorithms, are often rather imprecise, which can lead to sub-optimal scheduling decisions. We propose Reshi, a method for recommending task-node assignments during workflow execution that can cope with heterogeneous tasks and heterogeneous nodes. Reshi approaches the problem as a regression task, where task-node pairs are modeled as feature vectors over the results of dedicated micro benchmarks and past task executions. Based on these features, Reshi trains a regression tree model to rank and recommend nodes for each ready-to-run task, which can be used as input to a scheduler. For our evaluation, we benchmarked 27 AWS machine types using three representative workflows. We compare Reshi's recommendations with three state-of-the-art schedulers. Our evaluation shows that Reshi outperforms HEFT by a mean makespan reduction of 7.18% and 18.01% assuming a mean task runtime prediction error of 15%., Comment: Paper accepted in 41st IEEE International Performance Computing and Communications Conference (IPCCC 2022)

Many scientific workflow scheduling algorithms need to be informed about task runtimes a-priori to conduct efficient scheduling. In heterogeneous cluster infrastructures, this problem becomes aggravated because these runtimes are required for each task-node pair. Using historical data is often not feasible as logs are typically not retained indefinitely and workloads as well as infrastructure changes. In contrast, online methods, which predict task runtimes on specific nodes while the workflow is running, have to cope with the lack of example runs, especially during the start-up. In this paper, we present Lotaru, a novel online method for locally estimating task runtimes in scientific workflows on heterogeneous clusters. Lotaru first profiles all nodes of a cluster with a set of short-running and uniform microbenchmarks. Next, it runs the workflow to be scheduled on the user's local machine with drastically reduced data to determine important task characteristics. Based on these measurements, Lotaru learns a Bayesian linear regression model to predict a task's runtime given the input size and finally adjusts the predicted runtime specifically for each task-node pair in the cluster based on the micro-benchmark results. Due to its Bayesian approach, Lotaru can also compute robust uncertainty estimates and provides them as an input for advanced scheduling methods. Our evaluation with five real-world scientific workflows and different datasets shows that Lotaru significantly outperforms the baselines in terms of prediction errors for homogeneous and heterogeneous clusters., Comment: paper accepted in 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022)

We consider the moduli space $\mathcal{M}_{\nu}$ of torsion-free, asymptotically conical (AC) Spin(7)-structures which are defined on the same manifold and asymptotic to the same Spin(7)-cone with decay rate $\nu<0$. We show that $\mathcal{M}_{\nu}$ is an orbifold if $\nu$ is a generic rate in the non-$L^2$ regime $(-4,0)$. Infinitesimal deformations are given by topological data and solutions to a non-elliptic first-order PDE system on the compact link of the asymptotic cone. As an application, we show that the classical Bryant-Salamon metric on the bundle of positive spinors on $S^4$ has no continuous deformations as an AC Spin(7)-metric., Comment: 42 pages

We clarify the global geometry of two 1-parameter families of cohomogeneity one Spin(7) holonomy metrics with generic orbit the Aloff--Wallach space $N(1,-1) \cong \mathrm{SU}(3)/\mathrm{U}(1)$ and singular orbits $S^5$ and $\mathbb{C}P^2$, which at short distance were shown to exist by Reidegeld. The two families fit into the geography of previously known families of cohomogeneity one metrics with exceptional holonomy and provide a Spin(7) analogue of the well-known conifold transition in the setting of Calabi--Yau 3-folds. Furthermore, we discover that there is another transition to families of Spin(7) holonomy metrics which have a similar asymptotic behaviour on one end, but are singular on the other end. We obtain our results by relating the Spin(7)-equations to a simple dynamical system on a 3-dimensional cube., Comment: 44 pages, 5 figures

We probe the interface between a phase-separated Bose-Fermi mixture consisting of a small BEC of $^{41}$K residing in a large Fermi sea of $^6$Li. We quantify the residual spatial overlap between the two components by measuring three-body recombination losses for variable strength of the interspecies repulsion. A comparison with a numerical mean-field model highlights the importance of the kinetic energy term for the condensed bosons in maintaining the thin interface far into the phase-separated regime. Our results demonstrate a corresponding smoothing of the phase transition in a system of finite size., Comment: The Supplemental Materials are located in the Ancillary files