High-Performance Computing

Also known as HPC

High-performance computing studies how to make large computations run efficiently on multicore CPUs, GPUs, and distributed clusters, supporting applications from scientific simulation to large-scale AI. Common approaches include parallel programming with MPI and OpenMP, GPU kernels and compiler optimization, and algorithms that reduce data movement and communication. Scheduling, load balancing, and collective communication help coordinate work across heterogeneous machines and facilities. A central challenge is scaling performance without sacrificing correctness, portability, or efficient use of memory and network resources.

Latest papers

143 papers10 papers in the last four weeks, down 41% on the four weeks before. 0.1% of all new papers.

Parallelizing Counterfactual Regret Minimization
May 14, 2026Juho Kim, Tuomas SandholmGPU Acceleration Imperfect-Information Games
Computer Science Department Carnegie Mellon University · Computer Science Department, CMU · Strategic Machine, Inc. +2
A Neural Hierarchical-Matrix Preconditioner for Real-Time GPU Solves
May 13, 2026Carl Osborne, Minghao Guo, Crystal Owens +1Graph Neural Networks High-Performance Computing
MIT CSAIL, Cambridge, USA
PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization
May 13, 2026Huihao Jing, Wenbin Hu, Shaojin Chen +5Benchmark Design High-Performance Computing
HKUST · NYU · SWUPL
Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
May 10, 2026Víctor GallegoGPU Kernel Optimization Benchmark Design
Komorebi AI Technologies, Madrid, Spain
DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism
May 10, 2026Zhichen Zeng, Chi-Chih Chang, Jiayi Wang +10Expert Parallelism Communication-Efficient Distributed Training
ByteDance Seed · University of Washington · Cornell University
CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
May 8, 2026Shiyang Li, Zijian Zhang, Guangyan Sun +5LLM Evaluation GPU Kernel Optimization
University of Minnesota · Northeastern University
HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support
May 8, 2026Nourin Shahin, Izzat AlsmadiRetrieval-Augmented Generation Domain Adaptation
Texas A&M University San Antonio
Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism
May 6, 2026Sajal Dash, Feiyi WangCommunication-Efficient Distributed Training High-Performance Computing
Oak Ridge National Laboratory Oak Ridge, USA
comokit4py : a python package to ease COMOKIT agent based model simulation integration into a high performance computing workflow
May 6, 2026Arthur Brugière, Kévin ChapuisHigh-Performance Computing Agent-Based Modeling
UMMISCO UMI 209, SU/IRD Bondy, France · Thuyloi University 175 Tay Son, Dong Da, Hanoi, Vietnam · ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ R´eunion Montpellier, France
Tile-Level Activation Overlap for Efficient LLM Inference
May 5, 2026Abhinav Jangda, Tyler Sorensen, Sebastian Burckhardt +3LLM Inference Efficiency GPU Kernel Optimization
Microsoft Research · Microsoft
A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments
May 5, 2026Sergio Mendoza, Cedric Bhihe, Natalia Zamora +5High-Performance Computing Human-in-the-Loop AI
1Barcelona Supercomputing Center, Spain · 2NTT DATA, Spain
FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
May 4, 2026Yijiang Li, Emon Dey, Zilinghan Li +3Federated Learning Aggregation High-Performance Computing
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA · Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
VkSplat: High-Performance 3DGS Training in Vulkan Compute
Apr 30, 2026Jingxiang Chen, Mohamed Ibrahim, Yang Liu3D Gaussian Splatting 3DGS Acceleration
Huawei Canada
A High-Throughput Compute-Efficient POMDP Hide-And-Seek-Engine (HASE) for Multi-Agent Operations
Apr 29, 2026Timothy Flavin, Sandip SenHigh-Performance Computing Partially Observable Markov Decision Processes
FloatSOM: GPU-Accelerated, Distributed, Topology-Flexible Self-Organizing Maps
Apr 29, 2026Tony Xu, Sarah Klamt, Katherine Turner +3GPU Acceleration High-Performance Computing
John Curtin School of Medical Research, Australian National University · Department of Information Technology and Electrical Engineering, ETH Zürich · Mathematical Data Science Centre, Australian National University +2
RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts
Apr 28, 2026Vyom Sharma, Debajyoti DattaGPU Kernel Optimization GPU Acceleration
Hippocratic AI
ELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers
Apr 26, 2026Chih-Chung Hsu, Xin-Di Ma, Wo-Ting Liao +1Efficient Transformer Inference Softmax Attention
Advanced Computer Vision Laboratory, National Yang Ming Chiao Tung University
Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems
Apr 26, 2026Ziyu Wang, Fei Lei, Dezun DongHigh-Performance Computing
College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China
Decoupled DiLoCo for Resilient Distributed Pre-training
Apr 23, 2026Arthur Douillard, Keith Rush, Yani Donchev +14Language Model Pretraining Distributed Optimization
1Google DeepMind · 2Google Research
Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction
Apr 17, 2026Xiao Wang, Zezhong Zhang, Isaac Lyngaas +10Climate Science High-Performance Computing
Oak Ridge National Laboratory, Oak Ridge, TN, USA · Auburn University, Auburn, AL, USA · Florida State University, Tallahassee, FL, USA +2
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Apr 17, 2026Yuanchang Zhou, Hongyu Wang, Yiming Du +12Machine Learning Interatomic Potentials Communication-Efficient Distributed Training
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · Independent Researcher · Sun Yat-Sen University
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Apr 16, 2026Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5GPU Kernel Optimization GPU Acceleration
Intel Corporation
Nautilus: An Auto-Scheduling Tensor Compiler for Efficient Tiled GPU Kernels
Apr 16, 2026Yifan Zhao, Yuchen Yang, Matei Budiu +1GPU Kernel Optimization GPU Acceleration
University of Illinois Urbana-Champaign USA
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
Apr 15, 2026Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8Graph Foundation Models High-Performance Computing
Computational Sciences and Engineering Division, Oak Ridge National Laboratory · Computer Science and Mathematics Division, Oak Ridge National Laboratory · National Center for Computational Sciences Division, Oak Ridge National Laboratory +2
LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems
Apr 14, 2026Anne Lee, Gurudutt HosangadiLifelong Learning Agents Energy-Efficient ML
AI Research Lab, Nokia Bell Labs, Naperville, USA · AI Research Lab, Nokia Bell Labs, Murray Hill, USA
Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System
Apr 9, 2026Thang Duc Pham, Harikrishna Tummalapalli, Fakhrul Hasan Bhuiyan +5Multi-Agent Orchestration Scientific Workflow Automation
Computational Science (CPS) Division, Argonne National Laboratory, Lemont IL 60439, USA · Argonne Leadership Computing Facility (ALCF), Argonne National Laboratory, Lemont IL 60439, USA
Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS
Apr 8, 2026Luca Pennati, Andong Hu, Ivy Peng +2Molecular Dynamics Simulation High-Performance Computing
KTH Royal Institute of Technology, Stockholm, Sweden
Instruction-Tuned LLMs for Parsing and Mining Unstructured Logs on Leadership HPC Systems
Apr 6, 2026Ahmad Maroof Karimi, Jong Youl Choi, Charles Qing Cao +1Document Parsing LLM Information Extraction
Oak Ridge National Laboratory, Oak Ridge, USA · University of Tennessee, Knoxville, USA
AutoLALA: Automatic Loop Algebraic Locality Analysis for AI and HPC Kernels
Apr 6, 2026Yifan Zhu, Yekai Pan, Yanghui Wu +1High-Performance Computing
University of Rochester, USA
LP-GEMM: Integrating Layout Propagation into GEMM Operations
Apr 6, 2026César Guedes Carneiro, Lucas Alvarenga, Guido Araujo +1High-Performance Computing
Instituto de Computação, Universidade Estadual de Campinas, Campinas – SP, Brazil

May 14, 2026J/K move · Enter open · S save

Parallelizing Counterfactual Regret Minimization

Juho Kim, Tuomas Sandholm

Computer Science Department Carnegie Mellon University · Computer Science Department, CMU · Strategic Machine, Inc.+2

GPU Acceleration Imperfect-Information Games Game Theory High-Performance Computing Counterfactual Regret Minimization Regret Minimization

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Parallelization has played an instrumental role in the field of artificial intelligence (AI), drastically reducing the time taken to train and evaluate large AI models. In contrast to its impact in the broader field of AI, applying parallelization to computational game solving is relatively unexplored, despite its great potential. In this paper, we parallelize the family of counterfactual regret minimization (CFR) algorithms, which were central to important breakthroughs for solving large imperfect-information games. We present a generalized parallelization framework, reframing CFR as a series of linear algebra operations. Then, existing techniques for parallelizing linear algebra operations can be applied to accelerate CFR. We also describe how our technique can be applied to other tabular members of the CFR family of algorithms, including the state-of-the-art, such as CFR+, discounted CFR, and predictive variants of CFR. Experimentally, we show that our CFR implementation on a GPU is up to four orders of magnitude faster than Google DeepMind OpenSpiel's CFR implementations on a CPU.