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.

Data-Driven Dynamic Algorithm Dispatch with Large Language Models
Aug 21, 2026Rushil Shah, Emmanuel Lujan, Rabab Alomairy +1Algorithm Selection High-Performance Computing
Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA.
Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing
Aug 14, 2026Yixi Cai, Rundong Li, Yuhan Xie +3High-Performance Computing Simultaneous Localization and Mapping
Division of Robotics and Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Mechanical Engineering, the University of Hong Kong · School of Computing and Data Science, the University of Hong Kong
Effect of Abstractions and Prompting Strategies on LLM-Guided High-Performance Optimizations
Aug 8, 2026Jiří Klepl, Maty'aš Brabec, Martin KrulišHigh-Performance Computing Large Language Model-Guided Optimization
Charles University, Prague, Czech Republic
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
Aug 7, 2026Xiangyu Meng, Peng Chen, Mingzhen Li +7GPU Kernel Optimization GPU Acceleration
College of Computer Science and Technology, Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, China University of Petroleum (East China), Qingdao, China · A*STAR Institute of Advanced Intelligence and Computing, Singapore · RIKEN Center for Computational Science, Kobe, Japan +2
SparseDitto: An Agentic Sparse Compilation Framework through Architecture-Aware Synthesis on GPUs
Aug 5, 2026Shiyang Li, Guangyan Sun, Jinwei Tang +3GPU Kernel Optimization High-Performance Computing
University of Minnesota Minneapolis, MN, USA · Northeastern University Boston, MA, USA
CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives
Aug 3, 2026Zifan Zhang, Zhichao Hou, Tingxiang Ji +1High-Performance Computing Time Series Forecasting
Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System
Aug 3, 2026Kaiyuan Liao, Xiwei Xuan, Tanwi Mallick +3High-Performance Computing Time Series Forecasting
Department of Computer Science, University of California, Davis, USA · Argonne National Laboratory, Lemont, IL, USA · Rensselaer Polytechnic Institute, Troy, NY, USA
Multi-tenant Kubernetes Use Cases for AI, Secure Computing and Data Services, and More
Aug 1, 2026Jake Watson, Sadaf R Alam, Christopher Woods +6High-Performance Computing Cloud Computing
Bristol Centre for Supercomputing University of Bristol Bristol, United Kingdom · HPE HPC/AI EMEA Research Lab Hewlett Packard Enterprise Bristol, United Kingdom · HPE HPC/AI EMEA Research Lab Hewlett Packard Enterprise Zürich, Switzerland
A Fortran General-Purpose Transpiler: Proof of Concept
Jul 31, 2026Shivamshan Sivanesan, Kazem ArdanehCode Translation Automatic Differentiation
1Climate Modeling Center, Sorbonne University, CNRS, IPSL, Paris, France
HARGO: Heterogeneity-Aware Reward-Guided Optimization for RL Post-Training of LLMs on HPC Tasks
Jul 30, 2026Tiangang Li, Xiangbo TianRL for Language Models LLM Alignment
School of Computer Science, Wuhan University, Wuhan, China
Improving Improved Kernel PLS
Jul 17, 2026Ole-Christian Galbo EngstrømHigh-Performance Computing Kernel Methods
FOSS Analytical A/S, Nils Foss All´e 1, Hillerød, 3400, Denmark
Rendering 3D Gaussians on a Graph Processor
Jul 17, 2026Nicholas Fry, Ignacio Alzugaray, Mark Pupilli +23D Gaussian Splatting 3DGS Acceleration
Imperial College London, Department of Computing · Independent Researcher
CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
Jul 8, 2026Tingkai Liu, Muralidhar Andoorveedu, Sanjoy Das +2GPU Kernel Optimization High-Performance Computing
University of Illinois Urbana-Champaign · NVIDIA Corporation
UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods
Jul 7, 2026Yipeng Liu, Chang Liu, Si Shen +16Expert Parallelism High-Performance Computing
State Key Laboratory for Novel Software Technology, Nanjing University · Huawei Technologies Co., Ltd.
Design-CP: Context Parallelism for Design of Protein Nanoparticles
Jul 3, 2026Lorenzo Tarricone, Helen E. Eisenach, Aiko Muraishi +1GPU Acceleration High-Performance Computing
Department of Statistics, University of Oxford, Oxford, UK · Ellison Institute of Technology Oxford, Oxford, UK · Institute for Protein Design, University of Washington, Seattle, USA +2
Scalable Maximal Frequent Episode Mining with Desbordante
Jul 3, 2026Maxim Ivanov, Matvei Smirnov, Alisa Strazdina +1High-Performance Computing
Desbordante Team Saint Petersburg, Russia · Saint Petersburg State University Saint Petersburg, Russia
Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models
Jul 2, 2026Xuan-Phi Nguyen, Shrey Pandit, Yiran Zhao +3Communication-Efficient Distributed Training High-Performance Computing
Salesforce AI Research
Scaling Weisfeiler-Leman Expressiveness Analysis to Massive Graphs with GPUs
Jul 1, 2026Filippo Biondi, Mirco Tribastone, Max TschaikowskiGPU Acceleration High-Performance Computing
IMT Lucca, Italy · Sapienza University Rome, Italy
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
Jun 27, 2026Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2Quantum Chemistry High-Performance Computing
D-ITET, ETH Zurich, Zurich, Switzerland
Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems
Jun 26, 2026Adrian P. Dieguez, Victor Conchello Vendrell, Alex Batlle +3High-Performance Computing Distributed Training
Qualcomm AI Research
Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization
Jun 24, 2026Jiading Gai, Shuai Zhang, Kaj Bostrom +6GPU Kernel Optimization High-Performance Computing
Amazon · Siemens · University of Minnesota
SOLAR: AI-Powered Speed-of-Light Performance Analysis
Jun 24, 2026Qijing Huang, Sana Damani, Zhifan Ye +9GPU Kernel Optimization High-Performance Computing
NVIDIA
Axon: A Synthesizing Superoptimizer for Tensor Programs
Jun 24, 2026Akash Kothari, Shaowei Zhu, Daniel Kroening +1AI Accelerator Inference High-Performance Computing
University of Illinois at Urbana-Champaign, USA · Amazon Web Services, USA
Scaling Nonlinear Optimization: Many Problems One GPU
Jun 24, 2026John Viljoen, Johanna Haffner, Masayoshi Tomizuka +1Trajectory Optimization GPU Acceleration
Department of Mechanical Engineering, University of California, Berkeley, CA, USA · Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
\chisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation
Jun 24, 2026Ira WolfsonGPU Acceleration Black-Box Optimization
Department of Electronics and Electrical Engineering Braude College of Engineering, Karmiel, Israel
BluTrain: A C++/CUDA Framework for AI Systems
Jun 23, 2026Adhitya Charan, Adwaid Suresh, Anuj Kumar +19GPU Acceleration High-Performance Computing
Blubridge AI
Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs
Jun 16, 2026Aditya Devarakonda, Irene Simó Muñoz, Giulia GuidiGPU Acceleration Communication-Efficient Distributed Training
Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA · Department of Computer Science, Cornell University, Ithaca, NY, USA
Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices
Jun 13, 2026Tao Lu, Haoyu Wang, Zonghui Wang +3LLM Pruning GPU Kernel Optimization
National University of Singapore · Zhejiang University
STREAM: Multi-Tier LLM Inference Middleware with Dual-Channel HPC Token Streaming
Jun 11, 2026Anas Nassar, Steve Mohr, Leonard Apanasevich +1LLM Serving LLM Inference
Advanced Cyberinfrastructure for Education and Research (ACER) University of Illinois Chicago Chicago, Illinois, USA
Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering
Jun 9, 2026Gal Bloch, Ariel Gera, Matan Orbach +2GPU Kernel Optimization GPU Acceleration
IBM Research

Aug 21, 2026J/K move · Enter open · S save

Data-Driven Dynamic Algorithm Dispatch with Large Language Models

Rushil Shah, Emmanuel Lujan, Rabab Alomairy, Alan Edelman

Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA.

Algorithm Selection High-Performance Computing Automated Algorithm Discovery Large Language Model-Guided Optimization

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We introduce a large language model (LLM)-driven approach for generating dynamic algorithmic dispatch heuristics in high-performance linear algebra. By combining prompt engineering with LLaMA 3 and a curated performance database, the model learns to synthesize selection heuristics that exploit structural patterns to identify fast algorithmic choices. A case study on LU factorization demonstrates the model's ability to replicate expert-designed strategies. This work, developed as part of the DARPA-MIT SmartSolve project, highlights the promise of LLMs for algorithmic discovery and the development of more adaptive, fast linear algebra software.