Vincent Lannurien
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133 lines
6.3 KiB
Markdown
133 lines
6.3 KiB
Markdown
---
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title: TPDS 2024 Digest
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venue: TPDS
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year: 2024
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date: '2024-01-01'
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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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### Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning
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*Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz *et al.**
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**TL;DR** — An active-learning framework automatically diagnoses runtime performance anomalies in production HPC systems by querying targeted job profiles to minimize labeling effort.
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**Why notable** — It bridges ML-based anomaly detection and operational HPC monitoring, demonstrating scalable root-cause identification on real supercomputer workloads.
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### AutoDDL: Automatic Distributed Deep Learning With Near-Optimal Bandwidth Cost
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*Jinfan Chen, Shigang Li 0002, Ran Guo, Jinhui Yuan *et al.**
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**TL;DR** — AutoDDL automatically searches for the distributed DNN training strategy that minimizes communication bandwidth cost while meeting performance targets.
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**Why notable** — Combining Hoefler's communication-model expertise with automatic strategy search, this paper is essential reading for practitioners scaling DNN training across large clusters.
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### PeakFS: An Ultra-High Performance Parallel File System via Computing-Network-Storage Co-Optimization for HPC Applications
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*Yixiao Chen, Haomai Yang, Kai Lu 0002, Wenlve Huang *et al.**
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**TL;DR** — PeakFS co-optimizes compute, network, and storage layers of a parallel file system to deliver ultra-high I/O throughput for HPC workloads.
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**Why notable** — Its holistic co-design perspective sets a new performance baseline for HPC storage and provides actionable insights for next-generation parallel file system architects.
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### Formal Definitions and Performance Comparison of Consistency Models for Parallel File Systems
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*Chen Wang 0004, Kathryn M. Mohror, Marc Snir*
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**TL;DR** — This paper formalizes consistency models used by parallel file systems and provides the first systematic empirical comparison of their performance trade-offs.
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**Why notable** — Rigorous formal treatment from Snir and Mohror clarifies long-standing ambiguities in HPC storage semantics, making it an important reference for storage system designers.
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### Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities
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*Ahmad Tarraf, Martin Schreiber 0001, Alberto Cascajo, Jean-Baptiste Besnard *et al.**
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**TL;DR** — A comprehensive survey of dynamic resource malleability in HPC, covering runtime systems, job schedulers, and application-level support with lessons from production systems.
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**Why notable** — As energy-aware and burst-resilient HPC scheduling becomes critical, this broad community-driven survey is the definitive starting point for research on malleable HPC runtimes.
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### Pyxis: Scheduling Mixed Tasks in Disaggregated Datacenters
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*Sheng Qi, Chao Jin, Mosharaf Chowdhury, Zhenming Liu *et al.**
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**TL;DR** — Pyxis is a scheduler for disaggregated datacenters that jointly manages latency-sensitive and batch tasks by exploiting flexible resource pooling across the disaggregated fabric.
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**Why notable** — It tackles one of the central open problems in cloud scheduling—multi-tenancy under disaggregation—with rigorous analysis and demonstrated gains on real workloads.
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### Swift: Expedited Failure Recovery for Large-Scale DNN Training
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*Yuchen Zhong, Guangming Sheng, Juncheng Liu, Jinhui Yuan *et al.**
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**TL;DR** — Swift dramatically reduces checkpoint and recovery overhead for large-scale DNN training by combining lightweight in-memory snapshots with selective recomputation.
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**Why notable** — As training runs on hundreds of GPUs grow longer and failures become inevitable, Swift's fault-tolerance approach directly addresses a practical bottleneck in modern deep-learning infrastructure.
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### FastLoad: Speeding Up Data Loading of Both Sparse Matrix and Vector for SpMV on GPUs
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*Jinyu Hu, Huizhang Luo, Hong Jiang 0001, Guoqing Xiao 0001 *et al.**
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**TL;DR** — FastLoad optimizes the memory-access pattern for loading both the sparse matrix and the dense vector in SpMV on GPUs, yielding significant throughput improvements.
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**Why notable** — SpMV is a foundational kernel for scientific computing and graph analytics; this paper's memory-access analysis and optimizations benefit a wide class of GPU applications.
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### KLNK: Expanding Page Boundaries in a Distributed Shared Memory System
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*Yiwei Ci, Michael R. Lyu, Zhan Zhang 0002, De-Cheng Zuo *et al.**
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**TL;DR** — KLNK extends distributed shared memory page granularity to reduce false sharing and improve throughput for irregular access patterns.
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**Why notable** — It addresses a classic but unsolved bottleneck in DSM systems with a practical, page-table-level mechanism applicable to emerging disaggregated memory architectures.
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### Enabling Efficient Erasure Coding in Disaggregated Memory Systems
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*Qiliang Li, Liangliang Xu, Yongkun Li 0001, Min Lyu *et al.**
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**TL;DR** — This paper designs an erasure-coding scheme tailored to disaggregated memory, exploiting its unique bandwidth topology to achieve fault tolerance with low overhead.
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**Why notable** — Fault tolerance in disaggregated memory is an open problem of growing importance; this work provides concrete mechanisms and strong performance results.
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### Simple, Fast and Widely Applicable Concurrent Memory Reclamation via Neutralization
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*Ajay Singh 0002, Trevor Alexander Brown, Ali José Mashtizadeh*
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**TL;DR** — Neutralization is a new mechanism for safe memory reclamation in lock-free data structures that is simpler, faster, and more portable than prior approaches.
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**Why notable** — Safe memory reclamation is a pervasive challenge in concurrent programming; this algorithm's breadth of applicability and performance improvements make it highly reusable.
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### DeepTM: Efficient Tensor Management in Heterogeneous Memory for DNN Training
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*Haoran Zhou, Wei Rang, Hongyang Chen 0001, Xiaobo Zhou 0002 *et al.**
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**TL;DR** — DeepTM dynamically manages tensor placement across DRAM and NVM during DNN training to reduce memory pressure and improve throughput.
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**Why notable** — As model sizes outpace GPU memory, heterogeneous memory management becomes critical; DeepTM provides a practical, training-aware solution with measurable benefits.
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