Vincent Lannurien
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56 lines
5.7 KiB
YAML
56 lines
5.7 KiB
YAML
venue: HPDC
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year: 2025
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date: "2025-07-20"
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tags: [hpc, distributed-systems, networking, storage, scheduling, cloud-hpc, performance]
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selected:
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- dblp_key: "conf/hpdc/FanDBK25"
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title: "Parameterized Algorithms for Non-uniform All-to-all"
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tldr: "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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- dblp_key: "conf/hpdc/KashyapLL25"
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title: "DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge"
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tldr: "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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- dblp_key: "conf/hpdc/WeiXGHDTT25"
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title: "TSUE: A Two-Stage Data Update Method for an Erasure Coded Cluster File System"
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tldr: "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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- dblp_key: "conf/hpdc/GuoYW025"
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title: "LegoIndex: A Scalable and Modular Indexing Framework for Efficient Analysis of Extreme-Scale Particle Data"
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tldr: "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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- dblp_key: "conf/hpdc/YangDZLL00C025"
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title: "IPComp: Interpolation Based Progressive Lossy Compression for Scientific Applications"
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tldr: "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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- dblp_key: "conf/hpdc/Liu0YJYJ25"
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title: "Advancing Scientific Data Compression via Cross-Field Prediction"
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tldr: "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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- dblp_key: "conf/hpdc/AshworthLGGPBYM25"
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title: "Flux Emulator: First Insights into Optimizing Scheduling for Exascale HPC"
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tldr: "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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- dblp_key: "conf/hpdc/ParkSLLKKS25"
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title: "HYPERF: End-to-End Autotuning Framework for High-Performance Computing"
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tldr: "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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- dblp_key: "conf/hpdc/BhosaleKS25"
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title: "Efficient and Cost-Effective HPC on the Cloud"
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tldr: "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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- dblp_key: "conf/hpdc/SolorzanoRSWBT25"
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title: "Bringing Differential Privacy to HPC: Privacy-Preserving Transformations of HPC Traces"
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tldr: "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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