high performance computing on graphics processing units: hgpu.org

hgpu.org » Fortran

ForOpenCL: Transformations Exploiting Array Syntax in Fortran for Accelerator Programming

Matthew J. Sottile, Craig E Rasmussen, Wayne N. Weseloh, Robert W. Robey, Daniel Quinlan, Jeffrey Overbey

View

Download (PDF)

Source codes

Tags: Computer science, Fortran, nVidia, OpenCL, Package, Programming Languages, Tesla C2050

July 14, 2011 by hgpu

Acceleration of the GAMESS-UK electronic structure package on graphical processing units

Karl A. Wilkinson, Paul Sherwood, Martyn F. Guest, Kevin J. Naidoo

Tags: Chemistry, Fortran, Molecular dynamics, Molecular modeling, nVidia

May 21, 2011 by hgpu

An algorithmic incremental and iterative development method to parallelize dusty-deck FORTRAN HPC codes in GPGPUs using CUDA

N. Ghaemian, M. Sharifi, B. Minaei,R. Orujov

Tags: Computer science, CUDA, Fortran, High-level Languages, nVidia

April 6, 2011 by hgpu

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Pei-Hung Lin, Jagan Jayaraj, and Paul R. Woodward

View

Download (PDF)

Tags: Cell processor, Code generation, Compilers, Computer science, CUDA, Fortran, High-level Languages, Memory model, nVidia, Tesla C1060

February 17, 2011 by hgpu

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

See all packages

* * *