multi-topic, publish from gh-pages branch

site/data/cloud-edge/papers/SC-2025-digest.yaml

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+venue: SC
+year: 2025
+papers:
+- title: 'Cosmological Hydrodynamics at Exascale: A Trillion-Particle Leap in Capability'
+  authors:
+  - Nicholas Frontiere
+  - J. D. Emberson
+  - Michael Buehlmann
+  - Esteban M. Rangel
+  - Salman Habib 0002
+  - Katrin Heitmann
+  - Patricia Larsen
+  - Vitali A. Morozov
+  - Adrian Pope
+  - Claude-André Faucher-Giguère
+  - Antigoni Georgiadou
+  - Damien Lebrun-Grandié
+  - Andrey Prokopenko
+  reason: "Delivers the first trillion-particle cosmological hydrodynamics simulation on exascale hardware, demonstrating sustained petaflop-scale performance on a flagship scientific application."
+
+- title: Ab-initio Quantum Transport with the GW Approximation, 42, 240 Atoms, and Sustained Exascale Performance
+  authors:
+  - Nicolas Vetsch
+  - Alexander Maeder
+  - Vincent Maillou
+  - Anders Winka
+  - Jiang Cao
+  - Grzegorz Kwasniewski
+  - Leonard Deuschle
+  - Torsten Hoefler
+  - Alexandros Nikolaos Ziogas
+  - Mathieu Luisier
+  reason: "Achieves sustained exascale performance for first-principles quantum transport at 42,240 atoms, establishing a new scale record for the GW many-body perturbation method."
+
+- title: Kilometer-Scale AI-Powered and Performance-Portable Earth System Model (AP3ESM) to Achieve Year-Scale Simulation Speed on Heterogeneous Supercomputers
+  authors:
+  - Kai Xu
+  - Maoxue Yu
+  - Yuhu Chen
+  - Jie Gao
+  - Shuang Wang
+  - Jiaying Song
+  - Xiaohui Duan
+  - Junwei Wei
+  - Jiangfeng Yu
+  - Hailong Liu 0007
+  - Jinrong Jiang
+  - Yi Zhang 0127
+  - Pengfei Lin 0004
+  - Tianyi Wang
+  - Pengfei Wang
+  - Weipeng Zheng
+  - Jingwei Xie
+  - Jiakang Zhang
+  - Zilu Liu
+  - Xiaoyu Jin
+  - Jilin Wei
+  - Qixin Chang
+  - Qingxia Lin
+  - Yanzhi Zhou
+  - Weiguo Liu
+  - Wei Xue 0003
+  - Yiwen Li
+  - Haohuan Fu
+  - Yue Yu 0001
+  - Xuebin Chi
+  - Lixin Wu
+  reason: "Integrates AI acceleration into a kilometer-scale climate model to reach year-scale simulation throughput, showing how ML–physics hybrid approaches can redefine climate modeling at supercomputer scale."
+
+- title: 'Uno: A One-Stop Solution for Inter- and Intra-Data Center Congestion Control and Reliable Connectivity'
+  authors:
+  - Tommaso Bonato
+  - Sepehr Abdous
+  - Abdul Kabbani
+  - Ahmad Ghalayini
+  - Nadeen Gebara
+  - Terry Lam
+  - Anup Agarwal
+  - Tiancheng Chen
+  - Zhuolong Yu
+  - Konstantin Taranov
+  - Mahmoud Elhaddad
+  - Daniele De Sensi
+  - Soudeh Ghorbani
+  - Torsten Hoefler
+  reason: "Proposes a unified congestion control and reliable transport architecture spanning intra- and inter-datacenter links, with strong throughput and latency results relevant to AI and HPC clusters."
+
+- title: 'SDR-RDMA: Software-Defined Reliability Architecture for Planetary Scale RDMA Communication'
+  authors:
+  - Mikhail Khalilov
+  - Siyuan Shen
+  - Marcin Chrapek
+  - Tiancheng Chen
+  - Kenji Nakano
+  - Nicola Mazzoletti
+  - Peter-Jan Gootzen
+  - Salvatore Di Girolamo
+  - Rami Nudelman
+  - Gil Bloch
+  - Jithin Jose
+  - Abdul Kabbani
+  - Sreevatsa Anantharamu
+  - Jie Zhang
+  - Konstantin Taranov
+  - Zhuolong Yu
+  - Scott Moe
+  - Mahmoud Elhaddad
+  - Torsten Hoefler
+  reason: "Introduces software-defined reliability for RDMA at global scale, decoupling reliability policies from hardware to dramatically improve fault tolerance and reconfigurability in large-scale HPC networks."
+
+- title: 'Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality'
+  authors:
+  - Daniele De Sensi
+  - Saverio Pasqualoni
+  - Lorenzo Piarulli
+  - Tommaso Bonato
+  - Seydou Ba
+  - Matteo Turisini
+  - Jens Domke
+  - Torsten Hoefler
+  reason: "Presents bine tree topologies for MPI collective operations that exploit communication locality, yielding significant latency and bandwidth improvements over standard binomial trees on modern HPC interconnects."
+
+- title: 'STELLAR: Storage Tuning Engine Leveraging LLM Autonomous Reasoning for High Performance Parallel File Systems'
+  authors:
+  - Chris Egersdoerfer
+  - Philip H. Carns
+  - Shane Snyder
+  - Robert Ross
+  - Dong Dai 0001
+  reason: "Demonstrates that an LLM-driven autonomous reasoning engine can tune parallel file system parameters as effectively as expert hand-tuning, opening a new direction for self-optimizing HPC storage."
+
+- title: 'Phoenix: A Refactored I/O Stack for GPU Direct Storage without Phony Buffers'
+  authors:
+  - Jianqin Yan
+  - Shi Qiu 0012
+  - Yina Lv
+  - Yifan Hu
+  - Hao Chen
+  - Zhirong Shen
+  - Xin Yao
+  - Renhai Chen
+  - Jiwu Shu
+  - Gong Zhang 0001
+  - Yiming Zhang 0003
+  reason: "Redesigns the GPU direct storage I/O stack to eliminate staging buffers, achieving large bandwidth gains for GPU-to-SSD transfers critical to LLM training and scientific data workflows."
+
+- title: Breaking the System Noise Barrier at Exascale
+  authors:
+  - Edgar A. León
+  - Joseph Glenski
+  - Mark J. Stock
+  - Kim H. McMahon
+  - William Loewe
+  - Clark Snyder
+  - Larry Kaplan
+  - Srinath Vadlamani
+  - Timothy I. Mattox
+  - Trent D'Hooge
+  - Brian Behlendorf
+  - Nathan Hanford
+  - Ramesh Pankajakshan
+  - Matthew L. Leininger
+  reason: "Provides a rigorous characterization and mitigation of OS and hardware noise at exascale, demonstrating measurable improvements in collective communication performance on a real production system."
+
+- title: 'Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUs'
+  authors:
+  - Shengkun Cui
+  - Archit Patke
+  - Hung Nguyen
+  - Aditya Ranjan
+  - Ziheng Chen 0006
+  - Phuong Cao
+  - Gregory H. Bauer
+  - Brett M. Bode
+  - Catello Di Martino
+  - Saurabh Jha
+  - Chandra Narayanaswami
+  - Daby Sow
+  - Zbigniew T. Kalbarczyk
+  - Ravishankar K. Iyer
+  reason: "Delivers the first detailed side-by-side hardware fault-injection study of H100 and A100 GPUs, revealing how architecture changes in Hopper alter error propagation and resilience for HPC and AI workloads."
+
+- title: Exploring and Mitigating Failure Behavior of Large Language Model Training Workloads in HPC Systems
+  authors:
+  - Pengfei Yu 0002
+  - Jingjing Gu
+  - Hao Han
+  - Dazhong Shen
+  - Bao Wen
+  - Yang Liu 0390
+  reason: "Characterizes real-world failure modes of large-scale LLM training on HPC clusters and proposes targeted mitigation strategies, providing essential reliability insights for AI infrastructure operators."
+
+- title: 'XaaS Containers: Performance-Portable Representation With Source and IR Containers'
+  authors:
+  - Marcin Copik
+  - Eiman Alnuaimi
+  - Alok Kamatar
+  - Valérie Hayot-Sasson
+  - Alberto Madonna
+  - Todd Gamblin
+  - Kyle Chard
+  - Ian T. Foster
+  - Torsten Hoefler
+  reason: "Proposes source- and IR-level HPC containers that enable performance portability across heterogeneous architectures without recompilation, addressing a key deployment challenge for reproducible HPC software."
+
+- title: 'cMPI: Using CXL Memory Sharing for MPI One-Sided and Two-Sided Inter-Node Communications'
+  authors:
+  - Xi Wang 0027
+  - Bin Ma
+  - Jongryool Kim
+  - Byungil Koh
+  - Hoshik Kim
+  - Dong Li 0001
+  reason: "Exploits CXL memory semantics to implement MPI communication primitives with dramatically reduced software overhead, demonstrating a promising path for memory-centric supercomputer interconnects."
+
+- title: 'X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms'
+  authors:
+  - Yueming Yuan
+  - Ahan Gupta
+  - Jianping Li
+  - Sajal Dash
+  - Feiyi Wang
+  - Minjia Zhang
+  reason: "Addresses the communication and load-balance bottlenecks of sparse Mixture-of-Experts training at scale, achieving efficient utilization of large GPU clusters for next-generation LLM workloads."
+
+- title: Benchmark-driven Models for Energy Analysis and Attribution of GPU-Accelerated Supercomputing
+  authors:
+  - Oscar Antepara
+  - Zhengji Zhao
+  - Brian Austin
+  - Nan Ding 0006
+  - Leonid Oliker
+  - Nicholas J. Wright
+  - Samuel Williams 0001
+  reason: "Develops fine-grained benchmark-driven energy models for GPU supercomputers that attribute power consumption to individual components and workloads, enabling principled energy optimization at the facility level."