Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2304.00019

Workflows Community Summit 2022: A Roadmap Revolution

Rafael Ferreira da Silva·Rosa M. Badia·Venkat Bala·Debbie Bard·Peer-Timo Bremer·Ian Buckley·Silvina Caino-Lores·Kyle Chard·Carole Goble·Shantenu Jha·Daniel S. Katz·Daniel Laney·Manish Parashar·Frederic Suter·Nick Tyler·Thomas Uram·Ilkay Altintas·Stefan Andersson·William Arndt·Juan Aznar·Jonathan Bader·Bartosz Balis·Chris Blanton·Kelly Rosa Braghetto·Aharon Brodutch·Paul Brunk·Henri Casanova·Alba Cervera Lierta·Justin Chigu·Taina Coleman·Nick Collier·Iacopo Colonnelli·Frederik Coppens·Michael Crusoe·Will Cunningham·Bruno de Paula Kinoshita·Paolo Di Tommaso·Charles Doutriaux·Matthew Downton·Wael Elwasif·Bjoern Enders·Chris Erdmann·Thomas Fahringer·Ludmilla Figueiredo·Rosa Filgueira·Martin Foltin·Anne Fouilloux·Luiz Gadelha·Andy Gallo·Artur Garcia Saez·Daniel Garijo·Roman Gerlach·Ryan Grant·Samuel Grayson·Patricia Grubel·Johan Gustafsson·Valerie Hayot-Sasson·Oscar Hernandez·Marcus Hilbrich·AnnMary Justine·Ian Laflotte·Fabian Lehmann·Andre Luckow·Jakob Luettgau·Ketan Maheshwari·Motohiko Matsuda·Doriana Medic·Pete Mendygral·Marek Michalewicz·Jorji Nonaka·Maciej Pawlik·Loic Pottier·Line Pouchard·Mathias Putz·Santosh Kumar Radha·Lavanya Ramakrishnan·Sashko Ristov·Paul Romano·Daniel Rosendo·Martin Ruefenacht·Katarzyna Rycerz·Nishant Saurabh·Volodymyr Savchenko·Martin Schulz·Christine Simpson·Raul Sirvent·Tyler Skluzacek·Stian Soiland-Reyes·Renan Souza·Sreenivas Rangan Sukumar·Ziheng Sun·Alan Sussman·Douglas Thain·Mikhail Titov·Benjamin Tovar·Aalap Tripathy·Matteo Turilli·Bartosz Tuznik·Hubertus van Dam·Aurelio Vivas
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.DC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala, Debbie Bard, Peer-Timo Bremer, Ian Buckley, Silvina Caino-Lores, Kyle Chard, Carole Goble, Shantenu Jha, Daniel S. Katz, Daniel Laney, Manish Parashar, Frederic Suter, Nick Tyler, Thomas Uram, Ilkay Altintas, Stefan Andersson, William Arndt, Juan Aznar, Jonathan Bader, Bartosz Balis, Chris Blanton, Kelly Rosa Braghetto, Aharon Brodutch, Paul Brunk, Henri Casanova, Alba Cervera Lierta, Justin Chigu, Taina Coleman, Nick Collier, Iacopo Colonnelli, Frederik Coppens, Michael Crusoe, Will Cunningham, Bruno de Paula Kinoshita, Paolo Di Tommaso, Charles Doutriaux, Matthew Downton, Wael Elwasif, Bjoern Enders, Chris Erdmann, Thomas Fahringer, Ludmilla Figueiredo, Rosa Filgueira, Martin Foltin, Anne Fouilloux, Luiz Gadelha, Andy Gallo, Artur Garcia Saez, Daniel Garijo, Roman Gerlach, Ryan Grant, Samuel Grayson, Patricia Grubel, Johan Gustafsson, Valerie Hayot-Sasson, Oscar Hernandez, Marcus Hilbrich, AnnMary Justine, Ian Laflotte, Fabian Lehmann, Andre Luckow, Jakob Luettgau, Ketan Maheshwari, Motohiko Matsuda, Doriana Medic, Pete Mendygral, Marek Michalewicz, Jorji Nonaka, Maciej Pawlik, Loic Pottier, Line Pouchard, Mathias Putz, Santosh Kumar Radha, Lavanya Ramakrishnan, Sashko Ristov, Paul Romano, Daniel Rosendo, Martin Ruefenacht, Katarzyna Rycerz, Nishant Saurabh, Volodymyr Savchenko, Martin Schulz, Christine Simpson, Raul Sirvent, Tyler Skluzacek, Stian Soiland-Reyes, Renan Souza, Sreenivas Rangan Sukumar, Ziheng Sun, Alan Sussman, Douglas Thain, Mikhail Titov, Benjamin Tovar, Aalap Tripathy, Matteo Turilli, Bartosz Tuznik, Hubertus van Dam, Aurelio Vivas. 2023-03-31. Workflows Community Summit 2022: A Roadmap Revolution. https://doi.org/10.5281/zenodo.7750670

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Profiling Concurrent Vision Inference Workloads on NVIDIA Jetson -- Extended

The proliferation of IoT devices and advancements in network technologies have intensified the demand for real-time data processing at the network edge. To address these demands, low-power AI accelerators, particularly GPUs, are increasingly deployed for inference tasks, enabling efficient computation while mitigating the latency and bandwidth limitations of cloud-based systems. Despite their growing deployment, GPUs remain underutilised even in computationally intensive workloads. This underutilisation stems from the limited understanding of GPU resource sharing, particularly in edge computing scenarios. In this work, we conduct a detailed analysis of both high- and low-level metrics, including GPU utilisation, memory usage, streaming multiprocessor (SM) utilisation, and tensor core usage, to identify bottlenecks and guide hardware-aware optimisations. By integrating traces from multiple profiling tools, we provide a comprehensive view of resource behaviour on NVIDIA Jetson edge devices under concurrent vision inference workloads. Our findings indicate that while GPU utilisation can reach $100\%$ with specific optimisations, critical low-level resources, such as SMs and tensor cores, often operate at only $15\%$ to $30\%$ utilisation. Moreover, we observe that certain CPU-side events, such as thread scheduling and context switching, frequently become bottlenecks, further constraining overall GPU performance. We provide several key observations for users of vision inference workloads on NVIDIA edge devices.

cs.DC↗
arXiv2026

Mask-Aware Execution for Efficient JEPA Training

Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models across vision, video, audio, brain dynamics, and time series. Despite (potential of) wide deployment, current JEPA training pipelines are inefficient: each input is executed through multiple mask-specific branches, with redundant target-side work, and memory-bound token routing. These costs grow with the number of masks and limit GPU efficiency. We present M-JEPA, a mask-aware execution architecture that restructures JEPA training without changing the learning objective. M-JEPA separates mask-independent computation from mask-dependent routing, enabling shared context encoder execution, fused token routing and slicing with backward support, sparse target encoder execution over the union of target tokens, and masked patch embedding for sparse inputs. The resulting pipeline preserves training semantics while reducing computation, memory traffic, and synchronization overhead. We implement M-JEPA for five JEPA variants and evaluate it on NVIDIA A100 GPUs. Compared against the state-of-the-art baselines, M-JEPA achieves up to 1.7x end-to-end training speedup for 2-10 masks. Separately, with masked patch embedding, 4.75x patch-embedding speedup at high sparsity. These results show that execution restructuring, rather than changes to the JEPA objective, is a key lever for efficient JEPA training.

cs.DC↗
arXiv2026

Multimmit: Extending Blocks for Faster Finality

To meet the throughput demands of modern blockchain systems, protocols for State Machine Replication (SMR) increasingly have many processors disseminate blocks of transactions in parallel, with consensus then establishing a total ordering on the blocks of all producers. Such designs face a choice as to when a block may enter the ordering. Certified approaches wait for a quorum to attest a block's availability, which is robust but adds message delays to every transaction. Uncertified approaches let proposals reference blocks immediately, which is fast but degrades rapidly when referenced data must be fetched on the critical path. Raptr, the state of the art, takes a middle course, finalising the longest prefix of the leader's proposal that a quorum holds, so that no processor ever blocks or fetches. The remaining weakness is sensitivity to order: if the data behind a single early batch is withheld, the proposal finalises little or nothing, so individual faulty producers can still deny the system its optimistic path. We present Multimmit, a protocol for $n \ge 5f+1$ processors combining a consensus layer requiring one round of voting per view with multi-chain data dissemination. Votes are cast relative to the leader's proposal, reporting per chain how far the voter can support it, and may themselves attest fresh blocks beyond it. A transaction block disseminated at time $t$ is ordered by $t+3δ$ in expectation and $t+2δ$ at best, measured from the block's dissemination rather than the leader's proposal. Degradation under faults is graceful: a faulty producer delays only its own chain's blocks, costing other chains at most a one-view wait for placement. No leader can both finalise its leader block and exclude a fresh, well-circulated block of an honest chain. Consensus traffic is tens of kilobytes per view, independent of transaction volume.

cs.DC↗