From 1bca3484c900f59aeb0c4517c71aa0e81c580b65 Mon Sep 17 00:00:00 2001 From: Justin Wozniak Date: Mon, 19 May 2025 15:32:15 -0500 Subject: [PATCH 1/2] Add 2 papers --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 890d7c3..f6070c6 100644 --- a/README.md +++ b/README.md @@ -17,9 +17,13 @@ Some recent project publications: * [Employing artificial intelligence to steer exascale workflows with Colmena](https://journals.sagepub.com/doi/pdf/10.1177/10943420241288242), L. Ward et al., 2024: Describes the Colmena framework that we use in developing AI-guided simulation campaign applications. * [Octopus: Experiences with a hybrid event-driven architecture for distributed scientific computing](https://arxiv.org/pdf/2407.11432), H. Pan et al., 2024: Introduces the Octopus framework that we are developing for scalable distributed event delivery. +* [Tools for predicting and responding to anomalies in experiment data streams](https://indico.esrf.fr/event/114/sessions/169/#20240926) +J. M. Wozniak et al., NOBUGS at European Synchrotron Radiation Facility, 2024: Presentation about initial Diaspora work in managing reliable data streams for scientific experiments. * [Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows](https://ieeexplore.ieee.org/abstract/document/10678669), B. Nicolae et al., 2024: Project overview paper. * [Efficient distributed continual learning for steering experiments in real-time](https://www.sciencedirect.com/science/article/pii/S0167739X24003820), T. Bouvier et al., 2025. Rehearsal-based continual learning for scientific applications. * [MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow](https://arxiv.org/abs/2501.10651), X. Yan et al., 2025: A real-time workflow combining generative AI and HPC simulations to discover novel metal-organic frameworks (MOFs) for carbon capture. +* [Impacts of shared filesystem performance on real-time data acquisition and analysis](https://web.cels.anl.gov/~woz/papers/FS_Impacts_2025.pdf) +J. M. Wozniak et al., Workshop on Near Real-Time Data Processing for Interconnected Scientific Instruments at SuperComputingAsia, 2025: Preliminary study of a 3-filesystem data movement workflow that mimics a light source science use case. * [D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage](tbd), M. Gonthier et al., 2025: A resilient solution for chunking and placing erasure-coded data on heterogeneous storage nodes. * [WRATH: Workload Resilience Across Task Hierarchies in Task-based Parallel Programming Frameworks](https://arxiv.org/abs/2503.12752), S. Zhou et al., 2025: A resilience module that categorizes and handles failures in task-based parallel programming frameworks. From ae02b69895dc9f8c2b3dea161d7721f2a30f4a63 Mon Sep 17 00:00:00 2001 From: Justin Wozniak Date: Mon, 19 May 2025 15:42:25 -0500 Subject: [PATCH 2/2] Better link to talk --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f6070c6..196ba17 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ Some recent project publications: * [Employing artificial intelligence to steer exascale workflows with Colmena](https://journals.sagepub.com/doi/pdf/10.1177/10943420241288242), L. Ward et al., 2024: Describes the Colmena framework that we use in developing AI-guided simulation campaign applications. * [Octopus: Experiences with a hybrid event-driven architecture for distributed scientific computing](https://arxiv.org/pdf/2407.11432), H. Pan et al., 2024: Introduces the Octopus framework that we are developing for scalable distributed event delivery. -* [Tools for predicting and responding to anomalies in experiment data streams](https://indico.esrf.fr/event/114/sessions/169/#20240926) +* [Tools for predicting and responding to anomalies in experiment data streams](https://indico.esrf.fr/event/114/contributions/779) J. M. Wozniak et al., NOBUGS at European Synchrotron Radiation Facility, 2024: Presentation about initial Diaspora work in managing reliable data streams for scientific experiments. * [Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows](https://ieeexplore.ieee.org/abstract/document/10678669), B. Nicolae et al., 2024: Project overview paper. * [Efficient distributed continual learning for steering experiments in real-time](https://www.sciencedirect.com/science/article/pii/S0167739X24003820), T. Bouvier et al., 2025. Rehearsal-based continual learning for scientific applications.