publish-assistant/site/data/cloud-edge/papers/SC-2024-digest.yaml

venue: SC
year: 2024
papers:
- title: "Pushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of\
    \ a Biological System with 100 Million Atoms"
  authors:
  - Honghui Shang
  - Ying Liu 0055
  - Zhikun Wu
  - Zhenchuan Chen
  - Jinfeng Liu 0004
  - Meiyue Shao
  - Yingzhou Li
  - Bowen Kan
  - Huimin Cui
  - Xiaobing Feng 0002
  - Yunquan Zhang
  - Donald G. Truhlar
  - Hong An
  - Xiao He 0004
  - Jinlong Yang 0003
  reason: "Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits."

- title: "Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System"
  authors:
  - Kylee Santos
  - Stan G. Moore
  - Tomas Oppelstrup
  - Amirali Sharifian
  - Ilya Sharapov
  - Aidan P. Thompson
  - Delyan Z. Kalchev
  - Danny Perez
  - Robert Schreiber
  - Scott Pakin
  - Edgar A. Leon
  - James H. Laros III
  - Michael James 0002
  - Sivasankaran Rajamanickam
  reason: "Demonstrates how a Cerebras wafer-scale engine shatters the classical MD timescale barrier, enabling microsecond-regime atomistic simulation at unprecedented speed."

- title: "Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per\
    \ Day"
  authors:
  - Jianxiong Li
  - Boyang Li
  - Zhuoqiang Guo
  - Mingzhen Li 0001
  - Enji Li
  - Lijun Liu
  - Guojun Yuan
  - Zhan Wang 0003
  - Guangming Tan
  - Weile Jia
  reason: "Achieves 149 ns/day for large-scale deep-potential MD, combining neural-network potentials and HPC engineering to approach DFT accuracy at AIMD-like scale."

- title: "Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale\
    \ Ab Initio Molecular Dynamics Using MP2 Potentials"
  authors:
  - Ryan Stocks
  - Jorge L. Galvez Vallejo
  - Fiona C. Y. Yu
  - Calum Snowdon
  - Elise Palethorpe
  - Jakub Kurzak
  - Dmytro Bykov
  - Giuseppe M. J. Barca
  reason: "First demonstration of MP2-level AIMD at the million-electron and exaFLOP/s scale, a landmark in quantum chemistry on supercomputers."

- title: "Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep\
    \ Learning"
  authors:
  - Wei An
  - Xiao Bi
  - Guanting Chen 0002
  - Shanhuang Chen
  - Chengqi Deng
  - Honghui Ding
  - Kai Dong 0003
  - Qiushi Du
  - Wenjun Gao
  - Kang Guan
  - Jianzhong Guo
  - Yongqiang Guo
  - Zhe Fu 0009
  - Ying He 0018
  - Panpan Huang
  - Jiashi Li
  - Wenfeng Liang
  - Xiaodong Liu 0021
  - Xin Liu 0126
  - Yiyuan Liu
  - Yuxuan Liu 0019
  - Shanghao Lu
  - Xuan Lu
  - Xiaotao Nie
  - Tian Pei
  - Junjie Qiu
  - Hui Qu
  - Zehui Ren
  - Zhangli Sha
  - Xuecheng Su
  - Xiaowen Sun
  - Yixuan Tan
  - Minghui Tang
  - Shiyu Wang
  - Yaohui Wang
  - Yongji Wang
  - Ziwei Xie
  - Yiliang Xiong
  - Yanhong Xu
  - Shengfeng Ye
  - Shuiping Yu
  - Yukun Zha
  - Liyue Zhang
  - Haowei Zhang
  - Mingchuan Zhang
  - Wentao Zhang
  - Yichao Zhang 0004
  - Chenggang Zhao
  - Yao Zhao 0005
  - Shangyan Zhou
  - Shunfeng Zhou
  - Yuheng Zou
  reason: "Full system co-design report from DeepSeek's AI-HPC cluster showing 40% cost reduction vs. NVIDIA DGX through network/software optimizations, with production evidence at scale."

- title: "MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows\
    \ with Direct Preference Optimization"
  authors:
  - Gautham Dharuman
  - Kyle Hippe
  - Alexander Brace
  - Sam Foreman
  - Väinö Hatanpää
  - Varuni Katti Sastry
  - Huihuo Zheng
  - Logan T. Ward
  - Servesh Muralidharan
  - Archit Vasan
  - Bharat Kale
  - Carla M. Mann
  - Heng Ma
  - Yun-Hsuan Cheng
  - Yuliana Zamora
  - Shengchao Liu
  - Chaowei Xiao
  - Murali Emani
  - Tom Gibbs
  - Mahidhar Tatineni
  - Deepak Canchi
  - Jerome Mitchell
  - Koichi Yamada
  - Maria Garzaran 0001
  - Michael E. Papka
  - Ian T. Foster
  - Rick Stevens
  - Anima Anandkumar
  - Venkatram Vishwanath
  - Arvind Ramanathan
  reason: "First exaFLOP/s AI science workflow, integrating multimodal protein design with DPO alignment at supercomputing scale across Frontier and Aurora."

- title: "ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability"
  authors:
  - Xiao Wang 0004
  - Siyan Liu
  - Aristeidis Tsaris
  - Jong-Youl Choi
  - Ashwin M. Aji
  - Ming Fan
  - Wei Zhang 0261
  - Junqi Yin
  - Moetasim Ashfaq
  - Dan Lu 0001
  - Prasanna Balaprakash
  reason: "Introduces a large foundation model for Earth system prediction trained on Frontier, demonstrating how exascale AI infrastructure enables climate-scale spatiotemporal modeling."

- title: "Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers"
  authors:
  - Siddharth Singh
  - Prajwal Singhania
  - Aditya K. Ranjan
  - John Kirchenbauer
  - Jonas Geiping
  - Yuxin Wen
  - Neel Jain
  - Abhimanyu Hans
  - Manli Shu
  - Aditya Tomar
  - Tom Goldstein
  - Abhinav Bhatele
  reason: "Presents an open-source framework for LLM training at thousands-of-GPU scale, systematically analyzing throughput, memory, and communication trade-offs on leadership supercomputers."

- title: "Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects"
  authors:
  - Daniele De Sensi
  - Lorenzo Pichetti
  - Flavio Vella
  - Tiziano De Matteis
  - Zebin Ren
  - Luigi Fusco
  - Matteo Turisini
  - Daniele Cesarini
  - Kurt Lust
  - Animesh Trivedi
  - Duncan Roweth
  - Filippo Spiga
  - Salvatore Di Girolamo
  - Torsten Hoefler
  reason: "Comprehensive empirical study of GPU-to-GPU communication across six major supercomputers, revealing bottlenecks and bandwidth characteristics relevant to all distributed AI/HPC workloads."

- title: "Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed\
    \ AI"
  authors:
  - Mikhail Khalilov
  - Salvatore Di Girolamo
  - Marcin Chrapek
  - Rami Nudelman
  - Gil Bloch
  - Torsten Hoefler
  reason: "Achieves bandwidth-optimal collective communication by offloading broadcast and allgather to SmartNICs, directly benefiting large-scale distributed deep learning."

- title: "A Workflow Roofline Model for End-to-End Workflow Performance Analysis"
  authors:
  - Nan Ding 0006
  - Brian Austin
  - Yang Liu 0179
  - Neil Mehta
  - Steven Farrell
  - Johannes P. Blaschke
  - Leonid Oliker
  - Hai Ah Nam
  - Nicholas J. Wright
  - Samuel Williams 0001
  reason: "Extends the Roofline model to full end-to-end HPC workflows, enabling systematic performance diagnosis across compute, I/O, and data movement stages."

- title: "GVARP: Detecting Performance Variance on Large-Scale Heterogeneous Systems"
  authors:
  - Xin You 0001
  - Zhibo Xuan
  - Hailong Yang 0002
  - Zhongzhi Luan
  - Yi Liu 0013
  - Depei Qian 0002
  reason: "Identifies and diagnoses GPU performance variance at scale on heterogeneous supercomputers, an increasingly critical issue for reproducibility and efficiency."

- title: "A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated\
    \ at Exascale"
  authors:
  - Wesley Brewer
  - Matthias Maiterth
  - Vineet Kumar
  - Rafal P. Wojda
  - Sedrick Bouknight
  - Jesse Hines
  - Woong Shin
  - Scott Greenwood
  - David Grant
  - Wesley Williams
  - Feiyi Wang
  reason: "First deployment of a digital twin for a liquid-cooled exascale system (Frontier), enabling real-time thermal and power management with validated empirical results."

- title: "Doubling Graph Traversal Efficiency to 198 TeraTEPS on the Supercomputer\
    \ Fugaku"
  authors:
  - Junya Arai
  - Masahiro Nakao
  - Yuto Inoue
  - Kanto Teranishi
  - Koji Ueno
  - Keiichiro Yamamura
  - Mitsuhisa Sato
  - Katsuki Fujisawa
  reason: "Sets a new world record for graph traversal at 198 TTEPS on Fugaku through novel communication and load-balancing techniques, a landmark Graph500 result."

- title: "MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive\
    \ Workloads"
  authors:
  - Luke Logan
  - Anthony Kougkas
  - Xian-He Sun
  reason: "Novel storage abstraction that transparently tiered memory and storage hierarchies, delivering near-DRAM performance for data-intensive HPC and AI workloads."