high performance computing on graphics processing units: hgpu.org

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Contract-Based General-Purpose GPU Programming

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package, Software Engineering

October 27, 2014 by hgpu

Parallel Primitive Optimization for GPU and Multicore

Andreas Hormandinger

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Tags: ATI, ATI Radeon HD 6970, Code generation, Computer science, OpenCL, Thesis

September 20, 2014 by hgpu

Directive-Based Compilers for GPUs

Swapnil Ghike, Ruben Gran, Maria J. Garzaran, David Padua

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenACC, Tesla X2090

September 2, 2014 by hgpu

A Framework for Lattice QCD Calculations on GPUs

F. T. Winter, M. A. Clark, R. G. Edwards, B. Joo

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Tags: Algorithms, Code generation, Computational Physics, CUDA, High Energy Physics - Lattice, Mathematical Software, nVidia, Physics, PTX, QCD, Tesla K20

August 27, 2014 by hgpu

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Moritz Schmid, Oliver Reiche, Christian Schmitt, Frank Hannig, Jurgen Teich

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Tags: ARM, Code generation, Compilers, Computational Complexity, Computer science, Differential equations, FPGA, Image processing, Partial differential equations, PDEs

August 23, 2014 by hgpu

Optimising Purely Functional GPU Programs (Thesis)

Trevor L. McDonell

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Performance, Tesla T10, Thesis

August 10, 2014 by hgpu

OMP2HMPP: HMPP Source Code Generation from Programs with Pragma Extensions

Albert Saa-Garriga, David Castells-Rufas, Jordi Carrabina

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C2075, Tesla K20, Tesla M2050

July 28, 2014 by hgpu

Simulating spiking neural networks on massively parallel graphical processing units using a code generation approach with GeNN

Esin Yavuz, James Turner, Thomas Nowotny

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Tags: Bioinformatics, Biology, Code generation, CUDA, Neural networks, Neuroscience, nVidia, Package, Tesla C2070

July 28, 2014 by hgpu

Abstraction and Implementation of Unstructured Grid Algorithms on Massively Parallel Heterogeneous Architectures

Istvan Zoltan Reguly

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Tags: Algorithms, Code generation, Computer science, CUDA, Differential equations, FEM, Finite element method, Finite volume method, Fluid dynamics, Heterogeneous systems, Linear Algebra, nVidia, Partial differential equations, PDEs, Programming techniques, Tesla C2070, Tesla K20, Tesla K40, Thesis

July 24, 2014 by hgpu

Loo.py: transformation-based code generation for GPUs and CPUs

Andreas Klockner

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Tags: ATI, ATI Radeon HD 7990, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, Programming Languages, PyOpenCL, Python

June 2, 2014 by hgpu

Geometric Algebra Enhanced Precompiler for C++, OpenCL and Mathematica’s OpenCLLink

Patrick Charrier, Mariusz Klimek, Christian Steinmetz, Dietmar Hildenbrand

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Package

May 12, 2014 by hgpu

Piko: A Design Framework for Programmable Graphics Pipelines

Anjul Patney, Stanley Tzeng, Kerry A. Seitz Jr., John D. Owens

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Tags: 3D Graphics and Realism, Code generation, Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, PTX, Rasterization, Raytracing, Rendering

April 29, 2014 by hgpu

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Contract-Based General-Purpose GPU Programming

Parallel Primitive Optimization for GPU and Multicore

Directive-Based Compilers for GPUs

A Framework for Lattice QCD Calculations on GPUs

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Optimising Purely Functional GPU Programs (Thesis)

OMP2HMPP: HMPP Source Code Generation from Programs with Pragma Extensions

Simulating spiking neural networks on massively parallel graphical processing units using a code generation approach with GeNN

Abstraction and Implementation of Unstructured Grid Algorithms on Massively Parallel Heterogeneous Architectures

Loo.py: transformation-based code generation for GPUs and CPUs

Geometric Algebra Enhanced Precompiler for C++, OpenCL and Mathematica’s OpenCLLink

Piko: A Design Framework for Programmable Graphics Pipelines

Recent source codes

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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