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SAGE: Self-Tuning Approximation for Graphics Engines

Mehrzad Samadi, Janghaeng Lee, D. Anoushe Jamshidi, Amir Hormati, Scott Mahlke

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Tags: Algorithms, Code generation, Compilers, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 560, Performance

November 14, 2013 by hgpu

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Michal Januszewski, Marcin Kostur

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Tags: Code generation, Computational Physics, CUDA, Fluid dynamics, Fluid simulation, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 285, OpenCL, Package, Physics, Python, Tesla C1060, Tesla C2050, Tesla K10, Tesla K20

November 12, 2013 by hgpu

Automatic Synthesis of Heterogeneous CPU-GPU Embedded Applications from a UML Profile

Federico Ciccozzi

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia

November 8, 2013 by hgpu

Towards Code Generation from Design Models for Embedded Systems on Heterogeneous CPU-GPU Platforms

Federico Ciccozzi

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia

October 18, 2013 by hgpu

Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Tiark Rompf, Martin Odersky, Kunle Olukotun

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla C2050

October 15, 2013 by hgpu

Speculative Execution of Parallel Programs with Precise Exception Semantics on GPUs

Akihiro Hayashi, Max Grossman, Jisheng Zhao, Jun Shirako, Vivek Sarkar

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Tags: Code generation, Computer science, Java, nVidia, OpenCL, Tesla M2050

October 5, 2013 by hgpu

Parametric GPU Code Generation for Affine Loop Programs

Athanasios Konstantinidis, Paul H. J. Kelly, J. Ramanujam, P. Sadayappan

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GT 540 M, nVidia GeForce GTX 580, OpenCL, Package, Tesla K20, Tesla M2070

October 5, 2013 by hgpu

Compiler Optimizations for SIMD/GPU/Multicore Architectures

Jun Liu

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Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050, Thesis

October 3, 2013 by hgpu

Kokkos: Enabling performance portability across manycore architectures

H. Carter Edwards, Christian R. Trott

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Tags: Code generation, Computer science, CUDA, Intel Phi, Molecular dynamics, nVidia, Package, Tesla K20

September 17, 2013 by hgpu

Automatic Skeleton-Based Compilation through Integration with an Algorithm Classification

Cedric Nugteren, Pieter Custers, Henk Corporaal

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, OpenCL, OpenMP

August 7, 2013 by hgpu

The Promises of Hybrid Hexagonal/Classical Tiling for GPU

Tobias Grosser, Sven Verdoolaege, Albert Cohen, P. Sadayappan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia Quadro NVS 5200 M, PTX

July 31, 2013 by hgpu

Counting and Occurrence Sort for GPUs using an Embedded Language

Josef Svenningsson, Bo Joel Svensson, Mary Sheeran

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Tags: Algorithms, Code generation, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 670, Package, Sorting

July 30, 2013 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

SAGE: Self-Tuning Approximation for Graphics Engines

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Automatic Synthesis of Heterogeneous CPU-GPU Embedded Applications from a UML Profile

Towards Code Generation from Design Models for Embedded Systems on Heterogeneous CPU-GPU Platforms

Domain-Specific Languages for Heterogeneous Parallel Computing

Speculative Execution of Parallel Programs with Precise Exception Semantics on GPUs

Parametric GPU Code Generation for Affine Loop Programs

Compiler Optimizations for SIMD/GPU/Multicore Architectures

Kokkos: Enabling performance portability across manycore architectures

Automatic Skeleton-Based Compilation through Integration with an Algorithm Classification

The Promises of Hybrid Hexagonal/Classical Tiling for GPU

Counting and Occurrence Sort for GPUs using an Embedded Language

Recent source codes

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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