Vincent Lannurien
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133 lines
6.8 KiB
Markdown
133 lines
6.8 KiB
Markdown
---
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title: FGCS 2025 Digest
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venue: FGCS
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year: 2025
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date: '2025-01-01'
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tags: []
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paper_count: 12
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draft: false
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---
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12 papers selected.
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### Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing
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*Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt *et al.**
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**TL;DR** — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.
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**Why notable** — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements.
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### Efficient distributed continual learning for steering experiments in real-time
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*Thomas Bouvier, Bogdan Nicolae, Alexandru Costan, Tekin Bicer *et al.**
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**TL;DR** — Proposes a distributed continual learning architecture that keeps deep learning models synchronized with a running scientific experiment by streaming lightweight updates across edge detectors and HPC backends.
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**Why notable** — One of the first systems to close the loop between experimental data streams and model adaptation in real-time without full retraining, validated on synchrotron detector workloads.
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### SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum
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*Juan Aznar-Poveda, Maximilian Franz Ebner, Thomas Fahringer, Zahra Najafabadi Samani *et al.**
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**TL;DR** — Introduces SmartKV, a geo-distributed key-value store that uses latency-aware replication policies to deliver consistent low-latency reads across edge, fog, and cloud tiers of the computing continuum.
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**Why notable** — Provides a concrete, benchmarked storage primitive for the computing continuum that fills the gap between single-datacenter stores and high-latency cloud object storage.
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### Scalable compute continuum
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*Valeria Cardellini, Patrizio Dazzi, Gabriele Mencagli, Matteo Nardelli 0001 *et al.**
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**TL;DR** — Defines a programming and deployment model for the compute continuum that abstracts resource heterogeneity from edge to cloud, enabling applications to scale dynamically across tiers.
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**Why notable** — Provides a principled architectural reference for the continuum that can guide system designers building next-generation distributed runtime environments.
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### A comparative study of ad-hoc file systems for extreme scale computing
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*Njoud O. Almaaitah, Francisco Javier García Blas, Genaro Sanchez-Gallegos, Jesús Carretero 0001 *et al.**
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**TL;DR** — Benchmarks GekkoFS, BeeGFS, and similar ad-hoc file systems under diverse HPC I/O patterns, characterizing their throughput, metadata performance, and suitability for burst-buffer scenarios.
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**Why notable** — The most systematic evaluation of ad-hoc parallel file systems available, giving HPC centers clear data to choose or configure temporary storage for large scientific workflows.
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### Advancing anomaly detection in computational workflows with active learning
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*Krishnan Raghavan, George Papadimitriou 0002, Hongwei Jin, Anirban Mandal *et al.**
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**TL;DR** — Applies active learning to reduce the labeling burden for workflow anomaly detection, selectively querying an oracle for the most informative execution traces within a Pegasus workflow framework.
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**Why notable** — Demonstrates that active learning can make anomaly detection practical in real scientific workflows where labeled failure data is scarce, with experiments on production workloads.
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### MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade
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*Shreyas Sunil Gaikwad, Sri Hari Krishna Narayanan, Laurent Hascoët, Jean-Michel Campin *et al.**
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**TL;DR** — Describes MITgcm-AD v2, a production-quality adjoint of the MITgcm ocean-atmosphere model generated with Tapenade, enabling global sensitivity analyses and data assimilation at scale.
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**Why notable** — A landmark in scientific computing software sustainability: a fully open, differentiable climate model that enables gradient-based inversion for ocean state estimation.
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### zCeph: Design and implementation of a ZNS-friendly distributed file system
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*Jinyong Ha 0001, Yongseok Son*
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**TL;DR** — Redesigns the Ceph distributed file system to exploit Zoned Namespace SSDs natively, eliminating write amplification and improving throughput by aligning file system semantics with ZNS zone constraints.
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**Why notable** — Demonstrates how next-generation storage hardware (ZNS SSDs) demands rethinking distributed storage stack designs, with significant performance gains on real hardware.
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### RADiCe: A Risk Analysis Framework for Data Centers
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*Fabian Mastenbroek, Tiziano De Matteis, Vincent van Beek, Alexandru Iosup*
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**TL;DR** — Provides a quantitative risk analysis framework for data centers that models cascading failures across power, cooling, and compute subsystems using simulation to estimate availability and cost trade-offs.
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**Why notable** — Fills a practical gap for data center operators who need principled tools to evaluate infrastructure resilience beyond simple redundancy rules.
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### Deadline-constrained security-aware workflow scheduling in hybrid cloud architecture
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*Somayeh Abdi, Mohammad Ashjaei, Saad Mubeen*
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**TL;DR** — Formulates workflow scheduling in hybrid clouds as a multi-objective problem that jointly minimizes cost and execution time while meeting both deadline and data-security placement constraints.
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**Why notable** — One of the few scheduling works that treats security classification of tasks as a first-class constraint alongside performance, with practical validation on scientific workflow benchmarks.
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### Regen: An object layout regenerator on large-scale production HPC systems
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*Dong Kyu Sung, Sunggon Kim, Sangjin Lee 0003, Houjun Tang *et al.**
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**TL;DR** — Regen transparently reorganizes the on-disk layout of HDF5 and NetCDF objects in parallel file systems to match actual access patterns, improving I/O performance without application changes.
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**Why notable** — Deployed and validated on a production HPC system, showing significant I/O speedups for real scientific datasets, making it immediately relevant to storage administrators.
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### Formal definition and implementation of reproducibility tenets for computational workflows
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*Nicholas J. Pritchard, Andreas Wicenec*
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**TL;DR** — Formalizes a set of reproducibility requirements for scientific workflows and implements a verification layer within the DALIUGE workflow engine that checks compliance at design and execution time.
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**Why notable** — Provides the community with a concrete, tool-supported definition of workflow reproducibility, moving beyond aspirational guidelines to enforceable runtime checks.
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