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site/content/digests/FGCS-2024/index.md

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+---
+title: FGCS 2024 Digest
+venue: FGCS
+year: 2024
+date: '2024-01-01'
+tags: []
+paper_count: 12
+draft: false
+---
+
+12 papers selected.
+
+---
+
+### Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
+
+*Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini *et al.**
+
+**TL;DR** — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.
+
+**Why notable** — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale.
+
+---
+
+### Integrating quantum computing resources into scientific HPC ecosystems
+
+*Thomas L. Beck, Alessandro Baroni 0003, Ryan S. Bennink, Gilles Buchs *et al.**
+
+**TL;DR** — Describes the architecture and middleware decisions made at Oak Ridge National Laboratory to expose quantum devices as first-class resources within an existing HPC facility.
+
+**Why notable** — One of the first concrete descriptions of a production-scale quantum-HPC integration, providing a template other facilities can follow.
+
+---
+
+### Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures
+
+*Jonathan Bader, Fabian Lehmann, Lauritz Thamsen, Ulf Leser *et al.**
+
+**TL;DR** — Lotaru learns lightweight per-workflow runtime prediction models locally on each node using micro-benchmarks, eliminating the need for a centralized profiling service on heterogeneous clusters.
+
+**Why notable** — Addresses a core bottleneck in scientific workflow scheduling with a practical, evaluated approach that works without historical traces.
+
+---
+
+### The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud
+
+*André Bauer 0001, Haochen Pan, Ryan Chard, Yadu N. Babuji *et al.**
+
+**TL;DR** — Releases a large real-world dataset of function invocations across edge, campus, and cloud resources collected from the Globus Compute FaaS platform, along with workload analysis.
+
+**Why notable** — Provides the community with a rare, richly annotated dataset for benchmarking distributed FaaS schedulers and studying edge-to-cloud task patterns at scale.
+
+---
+
+### A survey on checkpointing strategies: Should we always checkpoint à la Young/Daly?
+
+*Leonardo Bautista-Gomez, Anne Benoit, Sheng Di, Thomas Hérault *et al.**
+
+**TL;DR** — Surveys decades of checkpointing research and rigorously examines when the classic Young/Daly formula is optimal versus when multi-level, coordinated, or application-aware strategies outperform it.
+
+**Why notable** — A definitive reference for HPC fault tolerance that unifies scattered results and provides clear guidance on choosing a checkpointing strategy for modern exascale workloads.
+
+---
+
+### Scalable I/O aggregation for asynchronous multi-level checkpointing
+
+*Mikaila J. Gossman, Bogdan Nicolae, Jon C. Calhoun*
+
+**TL;DR** — Proposes an aggregation layer that pipelines writes across multiple memory and storage tiers asynchronously, reducing checkpoint overhead for large-scale MPI applications.
+
+**Why notable** — Delivers measurable improvements in checkpoint throughput on realistic HPC applications, directly addressing the I/O bottleneck at exascale.
+
+---
+
+### StructMesh: A storage framework for serverless computing continuum
+
+*Diana Carrizales-Espinoza, Dante D. Sánchez-Gallegos, José Luis González Compeán, Jesús Carretero 0001*
+
+**TL;DR** — Introduces a hierarchical storage abstraction that unifies data management across edge, fog, and cloud tiers for serverless workflows, supporting structured data access patterns.
+
+**Why notable** — Offers a practical, evaluated solution to the data management gap in cloud-edge serverless architectures, relevant to scientific and industrial workflow deployment.
+
+---
+
+### Paving the way to hybrid quantum-classical scientific workflows
+
+*Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Ewa Deelman*
+
+**TL;DR** — Defines a taxonomy and reference architecture for hybrid quantum-classical workflows, mapping quantum circuit execution onto existing scientific workflow management system abstractions.
+
+**Why notable** — Provides the conceptual foundations needed to extend tools like Pegasus or Swift to orchestrate quantum subroutines within larger scientific pipelines.
+
+---
+
+### Online learning and continuous model upgrading with data streams through the Kafka-ML framework
+
+*Alejandro Carnero, Cristian Martín 0002, Gwanggil Jeon, Manuel Díaz*
+
+**TL;DR** — Extends Kafka-ML to support incremental online learning directly from streaming data topics, enabling continuous model updates without retraining from scratch in edge-cloud deployments.
+
+**Why notable** — Demonstrates a full open-source framework that bridges stream processing and ML model lifecycle management, with relevance to IoT and real-time analytics pipelines.
+
+---
+
+### GRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC systems
+
+*Martin Molan, Mohsen Seyedkazemi Ardebili, Junaid Ahmed Khan, Francesco Beneventi *et al.**
+
+**TL;DR** — Uses graph neural networks trained on node telemetry to predict imminent failures in exascale HPC clusters before they occur, enabling proactive maintenance and job migration.
+
+**Why notable** — Shows that temporal graph models over system topology substantially outperform per-node anomaly detection, with validation on a real pre-exascale machine.
+
+---
+
+### QFaaS: A Serverless Function-as-a-Service framework for Quantum computing
+
+*Hoa T. Nguyen, Muhammad Usman 0009, Rajkumar Buyya*
+
+**TL;DR** — Proposes QFaaS, a broker-based FaaS platform that abstracts heterogeneous quantum hardware providers behind a unified serverless interface with automatic circuit compilation and resource selection.
+
+**Why notable** — Addresses the pressing need for a cloud-agnostic quantum execution layer, laying groundwork for portable quantum applications across IBM, IonQ, and similar backends.
+
+---
+
+### Enabling federated learning across the computing continuum: Systems, challenges and future directions
+
+*Cèdric Prigent, Alexandru Costan, Gabriel Antoniu, Loïc Cudennec*
+
+**TL;DR** — Systematically surveys the technical barriers to training federated learning models that span IoT devices, edge servers, and cloud data centers, and proposes a reference architecture addressing heterogeneity and mobility.
+
+**Why notable** — A timely synthesis that clarifies open problems at the intersection of federated learning and the compute continuum, useful as a roadmap for system builders.
+