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From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation

Linus Bantel, Moritz Strack, Alexander Strack, Dirk Pflüger

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Tags: Code generation, Computer science, LLM, OpenMP

March 1, 2026 by hgpu

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: Benchmarking, Code generation, Computer science, CUDA, HPC, LLM, Matrix multiplication, nVidia, nVidia H100, OpenMP, Performance

February 16, 2026 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

Erel Kaplan, Tomer Bitan, Lian Ghrayeb, Le Chen, Tom Yotam, Niranjan Hasabnis, Gal Oren

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4060, OpenMP, Package

January 12, 2026 by hgpu

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

Gabriel Rodriguez-Canal, David Katz, Nick Brown

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Tags: Computer science, Fortran, FPGA, Heterogeneous systems, HLS, HPC, LLVM, OpenMP, Package

November 16, 2025 by hgpu

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Gabriel Kosmacher, Eric T. Phipps, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Kokkos, Mathematical Software, nVidia, nVidia H100, OpenMP, Package

October 19, 2025 by hgpu

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

M. D. Lepinzan, G. Lacopo, D. Goz, G. Taffoni, P. Monaco, P. J. Elahi, U. Varetto, M. Cytowski

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Benchmarking, Heterogeneous systems, HIP, HPC, Instrumentation and Methods for Astrophysics, nVidia, nVidia A100, OpenMP, Package

October 12, 2025 by hgpu

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Shun-ichiro Hayashi, Koki Morita, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: AI, Code generation, Computer science, CUDA, HPC, LLM, nVidia, OpenACC, OpenMP, Package, Tesla V100

October 5, 2025 by hgpu

TRUST: the HPC open-source CFD platform – from CPU to GPU

Elie Saikali∗ , Adrien Bruneton, Pierre Ledac, Remi Bourgeois

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Tags: AMD Radeon Instinct MI250A, AMD Radeon Instinct MI300A, ATI, cfd, CUDA, Fluid dynamics, HIP, MPI, Numerical simulation, nVidia, nVidia A100, OpenMP, Package

September 28, 2025 by hgpu

An HPC Benchmark Survey and Taxonomy for Characterization

Andreas Herten, Olga Pearce, Filipe S. M. Guimarães

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Tags: Benchmarking, Computer science, CUDA, Fortran, HIP, HPC, MPI, OpenACC, OpenCL, OpenMP, Package, Performance, ROCm, SYCL

September 14, 2025 by hgpu

High Performance Matrix Multiplication

Ethan Davis

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Package, Performance, Python, Tesla V100

September 14, 2025 by hgpu

AnnotationGym: A Generic Framework for Automatic Source Code Annotation

Hafsah Shahzad, Ahmed Sanaullah, Sanjay Arora, Ulrich Drepper, Martin Herbordt

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Tags: Benchmarking, Compilers, Computer science, FPGA, HLS, OpenACC, OpenMP

September 7, 2025 by hgpu

ConTraPh: Contrastive Learning for Parallelization and Performance Optimization

Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Nesreen K. Ahmed, Theodore L. Willke, Ali Jannesari

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Tags: Code generation, Computer science, Heterogeneous systems, HPC, LLM, nVidia, OpenACC, OpenCL, OpenMP, Tesla V100

August 10, 2025 by hgpu

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

From Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

TRUST: the HPC open-source CFD platform – from CPU to GPU

An HPC Benchmark Survey and Taxonomy for Characterization

High Performance Matrix Multiplication

AnnotationGym: A Generic Framework for Automatic Source Code Annotation

ConTraPh: Contrastive Learning for Parallelization and Performance Optimization

Recent source codes

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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