content updates, various fixes

site/data/papers/TPDS-2025-digest.yaml

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+venue: TPDS
+year: 2025
+selected:
+  - title: "HARMONIC: Uncertainty-Aware Multi-Objective Optimization for Energy-Efficient
+      HPC Resource Management"
+    authors:
+    - Kyrian Adimora
+    - Hongyang Sun 0001
+    tldr: "HARMONIC applies uncertainty-aware multi-objective optimization to jointly minimize energy consumption and maximize performance for HPC resource allocation."
+    why_notable: "It directly addresses the growing demand for energy-proportional HPC scheduling with principled probabilistic models, making it relevant to both system designers and green-computing researchers."
+
+  - title: "MIST: Towards MPI Instant Startup and Termination on Tianhe HPC Systems"
+    authors:
+    - Yiqin Dai
+    - Ruibo Wang
+    - Yong Dong
+    - Min Xie
+    - Juan Chen 0001
+    - Wenzhe Zhang
+    - Huijun Wu 0001
+    - Mingtian Shao
+    - Kai Lu 0001
+    tldr: "MIST reduces MPI job startup and termination latency to near-instant on the Tianhe supercomputer by redesigning the process-management and communication-bootstrap path."
+    why_notable: "Startup overhead is a significant fraction of short-job turnaround time at scale; MIST's results on a top-ranked system provide a concrete reference for HPC runtime developers."
+
+  - title: "Scheduling With Lightweight Predictions in Power-Constrained HPC Platforms"
+    authors:
+    - Danilo Carastan-Santos
+    - Georges Da Costa
+    - Igor Fontana De Nardin
+    - Millian Poquet
+    - Krzysztof Rzadca
+    - Patricia Stolf
+    - Denis Trystram
+    tldr: "This paper develops a scheduling framework that uses lightweight runtime predictions to respect power caps on HPC systems while minimizing job slowdown."
+    why_notable: "Power capping is now a first-class constraint on modern supercomputers, and this work from leading European HPC scheduling researchers offers practical, deployable algorithms."
+
+  - title: "PipeMesh: Achieving Memory-Efficient Computation-Communication Overlap for
+      Training Large Language Models"
+    authors:
+    - Fanxin Li
+    - Shixiong Zhao
+    - Yuhao Qing
+    - Jianyu Jiang
+    - Xusheng Chen
+    - Heming Cui
+    tldr: "PipeMesh overlaps pipeline-parallel computation and communication for LLM training while carefully managing memory to avoid out-of-memory failures."
+    why_notable: "Communication-computation overlap is one of the most impactful levers for LLM training efficiency, and PipeMesh's memory-awareness addresses the key practical constraint."
+
+  - title: "EfficientMoE: Optimizing Mixture-of-Experts Model Training With Adaptive Load
+      Balance"
+    authors:
+    - Yan Zeng
+    - Chengchuang Huang
+    - Yipeng Mei
+    - Lifu Zhang 0004
+    - Teng Su
+    - Wei Ye
+    - Wenqi Shi
+    - Shengnan Wang
+    tldr: "EfficientMoE introduces an adaptive load-balancing strategy for Mixture-of-Experts training that equalizes expert utilization and reduces communication bottlenecks."
+    why_notable: "MoE models are central to frontier LLM architectures, and load imbalance is their primary training inefficiency; this work provides both analysis and a practical solution."
+
+  - title: "SSpMM: Efficiently Scalable SpMM Kernels Across Multiple Generations of Tensor
+      Cores"
+    authors:
+    - Zeyu Xue
+    - Mei Wen
+    - Jianchao Yang
+    - Minjin Tang
+    - Zhongdi Luo
+    - Jing Feng
+    - Yang Shi 0008
+    - Zhaoyun Chen
+    - Junzhong Shen
+    - Johannes Langguth
+    tldr: "SSpMM delivers portable, high-performance sparse-matrix dense-matrix multiplication kernels that scale efficiently across Ampere, Hopper, and future Tensor Core generations."
+    why_notable: "SpMM is a bottleneck in GNN training and scientific computing; cross-generation portability without performance loss is a significant contribution for the GPU computing community."
+
+  - title: "IceFrog: A Layer-Elastic Scheduling System for Deep Learning Training in GPU
+      Clusters"
+    authors:
+    - Wei Gao 0064
+    - Zhuoyuan Ouyang
+    - Peng Sun 0006
+    - Tianwei Zhang 0004
+    - Yonggang Wen 0001
+    tldr: "IceFrog dynamically adjusts the number of pipeline stages (layers) assigned to each GPU during training to adapt to cluster heterogeneity and improve utilization."
+    why_notable: "Layer elasticity is a novel dimension of flexibility in distributed DNN training; IceFrog's scheduler provides measurable throughput gains in realistic heterogeneous GPU clusters."
+
+  - title: "Elastic Relaxation of Concurrent Data Structures"
+    authors:
+    - Kåre von Geijer
+    - Philippas Tsigas
+    tldr: "This paper introduces a formal framework and concrete algorithms for elastic relaxation of concurrent data structures, allowing tunable trade-offs between consistency and throughput."
+    why_notable: "Tsigas's group advances concurrent data-structure theory with a unifying formalism that subsumes many ad-hoc relaxed designs and enables provable guarantees."
+
+  - title: "Approximation Algorithms for Scheduling With/Without Deadline Constraints
+      Where Rejection Costs are Proportional to Processing Times"
+    authors:
+    - Olivier Beaumont
+    - Rémi Bouzel
+    - Lionel Eyraud-Dubois
+    - Esragul Korkmaz
+    - Laércio Lima Pilla
+    - Alexandre van Kempen
+    tldr: "This paper derives new approximation algorithms with tight ratios for online and offline scheduling problems where rejected jobs incur costs proportional to their processing times."
+    why_notable: "The theoretical results close open gaps in parallel scheduling complexity and are directly applicable to cloud and HPC batch schedulers that must handle job rejection."
+
+  - title: "EdgeHydra: Fault-Tolerant Edge Data Distribution Based on Erasure Coding"
+    authors:
+    - Qiang He 0001
+    - Guobiao Zhang
+    - Jiawei Wang 0003
+    - Ruikun Luo
+    - Xiaohai Dai
+    - Yuchong Hu
+    - Feifei Chen 0001
+    - Hai Jin 0001
+    - Yun Yang 0001
+    tldr: "EdgeHydra uses erasure coding tailored to edge-node failure patterns to provide fault-tolerant data distribution with low redundancy overhead at the network edge."
+    why_notable: "Fault tolerance at the edge is an increasingly critical requirement, and this system's erasure-coding approach significantly outperforms replication in storage efficiency."
+
+  - title: "Two-Dimensional Balanced Partitioning and Efficient Caching for Distributed
+      Graph Analysis"
+    authors:
+    - Shuai Lin
+    - Rui Wang 0076
+    - Yongkun Li 0001
+    - Yinlong Xu 0001
+    - John C. S. Lui
+    tldr: "This paper proposes a 2D balanced graph partitioning scheme combined with a caching policy that jointly minimizes communication and replication costs in distributed graph systems."
+    why_notable: "Graph partitioning and caching are co-dependent problems rarely treated together; the combined optimization yields substantial performance improvements with strong theoretical backing."
+
+  - title: "Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU
+      Platforms"
+    authors:
+    - Zhongyi Lin
+    - Ning Sun
+    - Pallab Bhattacharya
+    - Xizhou Feng
+    - Louis Feng
+    - John D. Owens
+    tldr: "This work builds a platform-agnostic performance model for distributed ML training that accurately predicts training throughput across diverse multi-GPU configurations without per-system profiling."
+    why_notable: "A universal modeling framework from Owens's group removes the need for expensive empirical searches when tuning distributed training configurations, benefiting the entire ML systems community."