content updates, various fixes

site/data/papers/TPDS-2024-digest.yaml

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+venue: TPDS
+year: 2024
+selected:
+  - title: "Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active
+      Learning"
+    authors:
+    - Burak Aksar
+    - Efe Sencan
+    - Benjamin Schwaller
+    - Omar Aaziz
+    - Vitus J. Leung
+    - Jim M. Brandt
+    - Brian Kulis
+    - Manuel Egele
+    - Ayse K. Coskun
+    tldr: "An active-learning framework automatically diagnoses runtime performance anomalies in production HPC systems by querying targeted job profiles to minimize labeling effort."
+    why_notable: "It bridges ML-based anomaly detection and operational HPC monitoring, demonstrating scalable root-cause identification on real supercomputer workloads."
+
+  - title: "AutoDDL: Automatic Distributed Deep Learning With Near-Optimal Bandwidth
+      Cost"
+    authors:
+    - Jinfan Chen
+    - Shigang Li 0002
+    - Ran Guo
+    - Jinhui Yuan
+    - Torsten Hoefler
+    tldr: "AutoDDL automatically searches for the distributed DNN training strategy that minimizes communication bandwidth cost while meeting performance targets."
+    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."
+
+  - title: "PeakFS: An Ultra-High Performance Parallel File System via Computing-Network-Storage
+      Co-Optimization for HPC Applications"
+    authors:
+    - Yixiao Chen
+    - Haomai Yang
+    - Kai Lu 0002
+    - Wenlve Huang
+    - Jibin Wang
+    - Jiguang Wan 0001
+    - Jian Zhou 0004
+    - Fei Wu 0005
+    - Changsheng Xie 0001
+    tldr: "PeakFS co-optimizes compute, network, and storage layers of a parallel file system to deliver ultra-high I/O throughput for HPC workloads."
+    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."
+
+  - title: "Formal Definitions and Performance Comparison of Consistency Models for Parallel
+      File Systems"
+    authors:
+    - Chen Wang 0004
+    - Kathryn M. Mohror
+    - Marc Snir
+    tldr: "This paper formalizes consistency models used by parallel file systems and provides the first systematic empirical comparison of their performance trade-offs."
+    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."
+
+  - title: "Malleability in Modern HPC Systems: Current Experiences, Challenges, and
+      Future Opportunities"
+    authors:
+    - Ahmad Tarraf
+    - Martin Schreiber 0001
+    - Alberto Cascajo
+    - Jean-Baptiste Besnard
+    - Marc-André Vef
+    - Dominik Huber
+    - Sonja Happ
+    - André Brinkmann
+    - David E. Singh
+    - Hans-Christian Hoppe
+    - Alberto Miranda
+    - Antonio J. Peña
+    - Rui Machado
+    - Marta Garcia-Gasulla
+    - Martin Schulz 0001
+    - Paul M. Carpenter
+    - Simon Pickartz
+    - Tiberiu Rotaru
+    - Sergio Iserte
+    - Víctor López 0003
+    - Jorge Ejarque
+    - Heena Sirwani
+    - Jesús Carretero 0001
+    - Felix Wolf 0001
+    tldr: "A comprehensive survey of dynamic resource malleability in HPC, covering runtime systems, job schedulers, and application-level support with lessons from production systems."
+    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."
+
+  - title: "Pyxis: Scheduling Mixed Tasks in Disaggregated Datacenters"
+    authors:
+    - Sheng Qi
+    - Chao Jin
+    - Mosharaf Chowdhury
+    - Zhenming Liu
+    - Xuanzhe Liu
+    - Xin Jin 0008
+    tldr: "Pyxis is a scheduler for disaggregated datacenters that jointly manages latency-sensitive and batch tasks by exploiting flexible resource pooling across the disaggregated fabric."
+    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."
+
+  - title: "Swift: Expedited Failure Recovery for Large-Scale DNN Training"
+    authors:
+    - Yuchen Zhong
+    - Guangming Sheng
+    - Juncheng Liu
+    - Jinhui Yuan
+    - Chuan Wu 0001
+    tldr: "Swift dramatically reduces checkpoint and recovery overhead for large-scale DNN training by combining lightweight in-memory snapshots with selective recomputation."
+    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."
+
+  - title: "FastLoad: Speeding Up Data Loading of Both Sparse Matrix and Vector for SpMV
+      on GPUs"
+    authors:
+    - Jinyu Hu
+    - Huizhang Luo
+    - Hong Jiang 0001
+    - Guoqing Xiao 0001
+    - Kenli Li 0001
+    tldr: "FastLoad optimizes the memory-access pattern for loading both the sparse matrix and the dense vector in SpMV on GPUs, yielding significant throughput improvements."
+    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."
+
+  - title: "KLNK: Expanding Page Boundaries in a Distributed Shared Memory System"
+    authors:
+    - Yiwei Ci
+    - Michael R. Lyu
+    - Zhan Zhang 0002
+    - De-Cheng Zuo
+    - Xiao-Zong Yang
+    tldr: "KLNK extends distributed shared memory page granularity to reduce false sharing and improve throughput for irregular access patterns."
+    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."
+
+  - title: "Enabling Efficient Erasure Coding in Disaggregated Memory Systems"
+    authors:
+    - Qiliang Li
+    - Liangliang Xu
+    - Yongkun Li 0001
+    - Min Lyu
+    - Wei Wang 0502
+    - Pengfei Zuo
+    - Yinlong Xu 0001
+    tldr: "This paper designs an erasure-coding scheme tailored to disaggregated memory, exploiting its unique bandwidth topology to achieve fault tolerance with low overhead."
+    why_notable: "Fault tolerance in disaggregated memory is an open problem of growing importance; this work provides concrete mechanisms and strong performance results."
+
+  - title: "Simple, Fast and Widely Applicable Concurrent Memory Reclamation via Neutralization"
+    authors:
+    - Ajay Singh 0002
+    - Trevor Alexander Brown
+    - Ali José Mashtizadeh
+    tldr: "Neutralization is a new mechanism for safe memory reclamation in lock-free data structures that is simpler, faster, and more portable than prior approaches."
+    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."
+
+  - title: "DeepTM: Efficient Tensor Management in Heterogeneous Memory for DNN Training"
+    authors:
+    - Haoran Zhou
+    - Wei Rang
+    - Hongyang Chen 0001
+    - Xiaobo Zhou 0002
+    - Dazhao Cheng
+    tldr: "DeepTM dynamically manages tensor placement across DRAM and NVM during DNN training to reduce memory pressure and improve throughput."
+    why_notable: "As model sizes outpace GPU memory, heterogeneous memory management becomes critical; DeepTM provides a practical, training-aware solution with measurable benefits."