high performance computing on graphics processing units: hgpu.org

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Automated and parallel code generation for finite-differencing stencils with arbitrary data types

K.A. Hawick and D.P. Playne

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Tags: Code generation, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 295, Partial differential equations, PDEs

February 13, 2011 by hgpu

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

D. P. Playne, K. A. Hawick

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Tags: Code generation, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX 480

February 13, 2011 by hgpu

Copperhead: Compiling an embedded data parallel language

Bryan Catanzaro, Michael Garland and Kurt Keutzer

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Tags: Code generation, Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 480, Package

February 13, 2011 by hgpu

Loop Transformation Recipes for Code Generation and Auto-Tuning

Mary Hall, Jacqueline Chame, Chun Chen, Jaewook Shin, Gabe Rudy, Malik Khan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenMP

February 12, 2011 by hgpu

Cg: a system for programming graphics hardware in a C-like language

William R. Mark, R. Steven Glanville, Kurt Akeley, Mark J. Kilgard

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Tags: 3D Graphics and Realism, Cg, Code generation, Compilers, Computer science, High-level Languages, nVidia, Package, Review

February 10, 2011 by hgpu

Automatic C-to-CUDA Code Generation for Affine Programs

Muthu Baskaran, J. Ramanujam, P. Sadayappan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX

February 8, 2011 by hgpu

Data transformations enabling loop vectorization on multithreaded data parallel architectures

Byunghyun Jang, Perhaad Mistry, Dana Schaa, Rodrigo Dominguez, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, ATI Stream, Brook, Code generation, Compilers, Computer science, Optimization

January 7, 2011 by hgpu

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Wenjing Ma, Gagan Agrawal

Tags: Code generation, Computer science, CUDA, GPU cluster, nVidia

October 28, 2010 by hgpu

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

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

Copperhead: Compiling an embedded data parallel language

Loop Transformation Recipes for Code Generation and Auto-Tuning

Cg: a system for programming graphics hardware in a C-like language

Automatic C-to-CUDA Code Generation for Affine Programs

Data transformations enabling loop vectorization on multithreaded data parallel architectures

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Recent source codes

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

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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