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.

A Practical Two-Stage Framework for GPU Resource and Power Prediction in Heterogeneous HPC Systems
Apr 2, 2026Beste Oztop, Dhruva Kulkarni, Zhengji Zhao +2High-Performance Computing
Electrical and Computer Engineering, Boston University, Boston, MA · Application Performance Group (NERSC), Lawrence Berkeley National Laboratory, Berkeley, CA · Advanced Technologies Group (NERSC), Lawrence Berkeley National Laboratory, Berkeley, CA +1
Improving MPI Error Detection and Repair with Large Language Models and Bug References
Apr 2, 2026Scott Piersall, Yang Gao, Shenyang Liu +1Language Model Error Detection Automated Program Repair
Dept. of Computer Science, University of Central Florida, Orlando, 32816, FL, US
AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases
Mar 28, 2026Mahesh Natarajan, Xiaoye Li, Weiqun ZhangRetrieval-Augmented Code Generation Code Generation
Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers
Mar 20, 2026Yijiang Li, Zilinghan Li, Kyle Chard +4Federated Learning Heterogeneous Federated Learning
Mathematics and Computer Science Division, Argonne National Laboratory · Data Science and Learning Division, Argonne National Laboratory · Department of Computer Science, University of Chicago
The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
Mar 18, 2026Narjes Ansari, César Feniou, Nicolaï Gouraud +13Drug Discovery Quantum Chemistry
Qubit Pharmaceuticals, Advanced Research Department, 75014 Paris, France · Qubit Pharmaceuticals Inc, Advanced Research Department, Chicago, IL, USA · Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 75005 Paris, France
When GPUs Fail Quietly: Observability-Aware Early Warning Beyond Numeric Telemetry
Mar 17, 2026Michael Bidollahkhani, Freja Nordsiek, Julian M. KunkelFault Detection Predictive Maintenance
Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG) Georg-August-Universität Göttingen Göttingen, Germany
An Agentic Evaluation Framework for AI-Generated Scientific Code in PETSc
Mar 16, 2026Hong Zhang, Barry Smith, Satish Balay +4High-Performance Computing Code Generation
Argonne National Laboratory, Lemont, IL, USA
Joint Optimization of Storage and Loading for High-Performance 3D Point Cloud Data Processing
Mar 16, 2026Ke Wang, Yanfei Cao, Xiangzhi Tao +7High-Performance Computing
Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
Mar 16, 2026Dejun Lin, Simon Chu, Vishanth Iyer +35Context Parallelism Protein Structure Prediction
NVIDIA · Rezo Therapeutics · Proxima +1
Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation
Mar 9, 2026Yuxin Tang, Zhiyuan Xin, Zhimin Ding +4High-Performance Computing
Rice University Houston, Texas, USA
Duration-Informed Workload Scheduler
Mar 7, 2026Daniela Loreti, Davide Leone, Andrea BorghesiHigh-Performance Computing
DISI, University of Bologna, Bologna, Viale Risorgimento 2, Italy
Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts
Mar 7, 2026Yuxuan Han, Meng-Hao Guo, Zhengning Liu +2Sparse Matrix Multiplication GPU Kernel Optimization
Tsinghua University · Proxseer Inc.
ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
Mar 3, 2026Liu Yang, Zeyu Nie, Andrew Liu +4High-Performance Computing Code Generation
Department of Computer Science, Yale University, New Haven, CT, USA · Google DeepMind, Mountain View, CA, USA
From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation
Feb 24, 2026Linus Bantel, Moritz Strack, Alexander Strack +1Code Generation LLM Code Generation
Institute for Parallel and Distributed Systems, University of Stuttgart, Stuttgart, Germany
Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler
Feb 22, 2026Javed Absar, Samarth Narang, Muthu BaskaranHigh-Performance Computing Edge ML
Qualcomm Technologies International, Ltd. · Qualcomm Technologies, Inc.
When Scaling Fails: Network and Fabric Effects on Distributed GPU Training Performance
Feb 16, 2026Dinesh Gopalan, Ratul AliDistributed Training High-Performance Computing
Principal Member of Technical Staff, AI Enablement AMD Dallas, Texas, USA · Institute of Information Technology (Postgraduate Researcher) Jahangirnagar University Dhaka, Bangladesh
DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce
Feb 9, 2026Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +1Communication-Efficient Distributed Training High-Performance Computing
University College London · VMware Research by Broadcom · Harvard University +1
Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple
Jan 22, 2026Evangelos Georganas, Alexander Heinecke, Pradeep DubeyHigh-Performance Computing
Intel Corporation
Tensor computing application programming interface: An application-oriented, lightweight API for portable high-performance tensor network computations
Dec 30, 2025Rong-Yang Sun, Tomonori Shirakawa, Hidehiko Kohshiro +2Tensor Networks High-Performance Computing
Department of Physics and Astronomy, California State University Northridge, Northridge, California 91330, USA · RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), Wako, Saitama 351-0198, Japan · Computational Materials Science Research Team, RIKEN Center for Computational Science (R-CCS), Kobe, Hyogo, 650-0047, Japan +5
Block Sparse Flash Attention
Dec 7, 2025Daniel Ohayon, Itay Lamprecht, Itay Hubara +3GPU Acceleration Self-Attention
Technion – Israel Institute of Technology, Haifa, Israel · Intel - Habana Labs · Intel – Habana Labs, Tel Aviv, Israel
CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning
Dec 2, 2025Songqiao Su, Xiaoya Li, Albert Wang +3Reinforcement Learning GPU Kernel Optimization
DeepReinforce Team
TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents
Sep 28, 2025Lukas Breitwieser, Ahmad Hesam, Abdullah Giray Yağlıkçı +3Agent-Based Modeling High-Performance Computing
ETH Zurich, Switzerland · Delft University of Technology, The Netherlands · CERN, Switzerland
BeforeIT.jl: High-Performance Agent-Based Macroeconomics Made Easy
Feb 18, 2025Adriano Meligrana, Mitja Devetak, Steven Hoekstra +5Agent-Based Modeling High-Performance Computing
University of Turin, Turin, Italy · Paris 1 Panthéon-Sorbonne University, Paris, France · Complexity Science Hub, Vienna, Austria +3

Apr 2, 2026J/K move · Enter open · S save

A Practical Two-Stage Framework for GPU Resource and Power Prediction in Heterogeneous HPC Systems

Beste Oztop, Dhruva Kulkarni, Zhengji Zhao, Ayse Kivilcim Coskun+1

Electrical and Computer Engineering, Boston University, Boston, MA · Application Performance Group (NERSC), Lawrence Berkeley National Laboratory, Berkeley, CA · Advanced Technologies Group (NERSC), Lawrence Berkeley National Laboratory, Berkeley, CA+1

High-Performance Computing

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Efficient utilization of GPU resources and power has become critical with the growing demand for GPUs in high-performance computing (HPC). In this paper, we analyze GPU utilization and GPU memory utilization, as well as the power consumption of the Vienna ab initio Simulation Package (VASP), using the Slurm workload manager historical logs and GPU performance metrics collected by NVIDIA's Data Center GPU Manager (DCGM). VASP is a widely used materials science application on Perlmutter at NERSC, an HPE Cray EX system based on NVIDIA A100 GPUs. Using our insights from the resource utilization analysis of VASP applications, we propose a resource prediction framework to predict the average GPU power, maximum GPU utilization, and maximum GPU memory utilization values of heterogeneous HPC system applications to enable more efficient scheduling decisions and power-aware system operation. Our prediction framework consists of two stages: 1) using only the Slurm accounting logs as training data and 2) augmenting the training data with historical GPU profiling metrics collected with DCGM. The maximum GPU utilization predictions using only the Slurm submission features achieve up to 97% accuracy. Furthermore, features engineered from GPU-compute and memory activity metrics exhibit good correlations with average power utilization, and our runtime power usage prediction experiments result in up to 92% prediction accuracy. These findings demonstrate the effectiveness of DCGM metrics in capturing application characteristics and highlight their potential for developing predictive models to support dynamic power management in HPC systems.