--- 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.