Vincent Lannurien
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140 lines
6.6 KiB
Markdown
140 lines
6.6 KiB
Markdown
---
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title: HPDC 2025 Digest
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venue: HPDC
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year: 2025
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date: '2025-07-20'
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tags:
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- hpc
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- distributed-systems
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- networking
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- storage
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- scheduling
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- cloud-hpc
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- performance
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paper_count: 10
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draft: false
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---
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10 papers selected.
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---
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### Parameterized Algorithms for Non-uniform All-to-all
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*Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar*
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**TL;DR** — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.
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**Why notable** — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.
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[→ Read paper](https://doi.org/10.1145/3731545.3731590)
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---
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### DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge
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*Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001*
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**TL;DR** — Offloads key-value store operations to Data Processing Units (DPUs) over RDMA to reduce CPU overhead and tail latency in edge deployments.
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**Why notable** — DPU offloading is an emerging paradigm for network-attached smart NICs in HPC clusters; this work provides concrete performance analysis showing when and how much offloading helps, informing future RDMA-based storage designs.
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[→ Read paper](https://doi.org/10.1145/3731545.3731571)
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---
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### TSUE: A Two-Stage Data Update Method for an Erasure Coded Cluster File System
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*Zheng Wei, Jing Xing, Yida Gu, Wenjing Huang 0002 *et al.**
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**TL;DR** — Proposes a two-stage update scheme for erasure-coded parallel file systems that decouples the logging and parity-update phases to cut write amplification and I/O latency.
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**Why notable** — Erasure coding is increasingly used in large-scale HPC storage to replace replication, but update overhead remains a bottleneck; TSUE addresses a core pain point for Lustre- and GPFS-class parallel file systems.
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[→ Read paper](https://doi.org/10.1145/3731545.3731577)
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---
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### LegoIndex: A Scalable and Modular Indexing Framework for Efficient Analysis of Extreme-Scale Particle Data
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*Chang Guo, Ning Yan 0002, Lipeng Wan 0001, Zhichao Cao 0002*
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**TL;DR** — Presents a composable, multi-level indexing framework for particle simulation datasets that enables efficient query processing at extreme scale without requiring full dataset scans.
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**Why notable** — Scientific particle simulations at exascale generate data volumes that overwhelm traditional post-processing pipelines; LegoIndex's modular design allows it to be adapted across different storage backends and query patterns commonly seen in DOE workloads.
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[→ Read paper](https://doi.org/10.1145/3731545.3731591)
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---
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### IPComp: Interpolation Based Progressive Lossy Compression for Scientific Applications
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*Zhuoxun Yang, Sheng Di, Longtao Zhang, Ruoyu Li *et al.**
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**TL;DR** — Introduces interpolation-driven progressive lossy compression that lets users trade accuracy for compression ratio at query time rather than at write time, without re-compressing stored data.
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**Why notable** — Progressive reconstruction is a long-sought capability for HPC I/O; IPComp achieves it with competitive compression ratios and builds on the widely used SZ/ZFP lineage, making adoption in existing scientific workflows straightforward.
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[→ Read paper](https://doi.org/10.1145/3731545.3731578)
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---
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### Advancing Scientific Data Compression via Cross-Field Prediction
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*Youyuan Liu, Wenqi Jia 0003, Taolue Yang, Bo Jiang *et al.**
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**TL;DR** — Exploits correlations between different physical fields in multi-field scientific datasets to improve lossy compression ratios beyond what single-field methods can achieve.
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**Why notable** — Multi-field simulations (climate, combustion, fusion) dominate HPC storage consumption; cross-field prediction represents a principled, generally applicable step change in compression efficiency for these workloads.
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[→ Read paper](https://doi.org/10.1145/3731545.3731592)
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---
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### Flux Emulator: First Insights into Optimizing Scheduling for Exascale HPC
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*W. Jay Ashworth, Ian Lumsden, Jim Garlick, Mark Grondona *et al.**
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**TL;DR** — Presents an emulation infrastructure for the Flux workload manager that enables scheduling algorithm evaluation at exascale node counts without requiring access to a full exascale machine.
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**Why notable** — Validating schedulers at exascale is otherwise infeasible before systems exist; Flux Emulator directly supports the scheduling research needed to maximize utilization of Frontier- and Aurora-class systems.
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[→ Read paper](https://doi.org/10.1145/3731545.3735121)
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---
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### HYPERF: End-to-End Autotuning Framework for High-Performance Computing
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*Juseong Park, Yongwon Shin, Junghyun Lee, Junseo Lee *et al.**
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**TL;DR** — Delivers an end-to-end autotuning framework that jointly optimizes compiler flags, runtime parameters, and problem-specific configurations for HPC applications through structured search.
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**Why notable** — Manual tuning of HPC codes for new architectures is expensive and error-prone; HYPERF's end-to-end scope distinguishes it from prior tools that target only one layer of the software stack, offering broader applicability across the HPC software ecosystem.
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[→ Read paper](https://doi.org/10.1145/3731545.3731588)
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---
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### Efficient and Cost-Effective HPC on the Cloud
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*Aditya Bhosale, Laxmikant V. Kalé, Sara Kokkila Schumacher*
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**TL;DR** — Demonstrates how Charm++-based adaptive runtime techniques—load balancing, dynamic over-decomposition, and message-driven execution—can recover near-on-premises HPC performance on cloud instances despite higher network variability.
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**Why notable** — Cloud-HPC convergence is a major community priority as on-premises clusters face procurement delays; this paper provides a practitioner-oriented analysis of which runtime adaptations deliver the best performance-per-dollar on AWS and Azure.
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[→ Read paper](https://doi.org/10.1145/3731545.3744667)
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---
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### Bringing Differential Privacy to HPC: Privacy-Preserving Transformations of HPC Traces
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*Ana Luisa Veroneze Solórzano, Rohan Basu Roy, Benjamin Schwaller, Sara Petra Walton *et al.**
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**TL;DR** — Applies differential privacy mechanisms to HPC job and performance traces, enabling centers to share workload data for research without exposing sensitive user or application information.
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**Why notable** — Sharing HPC traces is critical for reproducible scheduling and performance research but is often blocked by privacy concerns; this work provides a rigorous, deployable solution that could unlock a significant new supply of public HPC datasets.
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[→ Read paper](https://doi.org/10.1145/3731545.3731573)
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