high performance computing on graphics processing units: hgpu.org

hgpu.org » Code generation

An Automated Tool for Converting Directive Based C Code Into Parallel CUDA Code

Batliwala Saifuddin, Kadtan Lalit, Khan Mujjammil, Khandagale Pratik, S. M. Walunj

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia

April 19, 2014 by hgpu

Code Generation for Embedded Heterogeneous Architectures on Android

R. Membarth, O. Reiche, F. Hannig, J. Teich

View

Download (PDF)

Tags: Code generation, Heterogeneous systems, Image processing

April 1, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

Increasing programmability of an embedded domain specific language for GPGPU kernels using static analysis

Niklas Ulvinge

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Thesis

March 4, 2014 by hgpu

Automatic Resource-Constrained Static Task Parallelization

Dounia Khaldi

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, MPI, OpenCL, Task scheduling, Thesis

January 28, 2014 by hgpu

High Performance Code Generation for Stencil Computation on Heterogeneous Multi-device Architectures

Pei Li, Elisabeth Brunet, Raymond Namyst

View

Download (PDF)

Tags: Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, OpenCL

January 14, 2014 by hgpu

A Framework for Productive, Efficient and Portable Parallel Computing

Ekaterina I. Gonina

View

Download (PDF)

Source codes

Tags: Algorithms, Code generation, Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 480, Package, Python, Thesis

January 12, 2014 by hgpu

DEF-G: Declarative Framework for GPU Environment

Robert Senser, Tom Altman

View

Download (PDF)

Tags: Code generation, Computer science, Filtering, nVidia, nVidia GeForce GT 430, OpenCL, Tesla T20

January 5, 2014 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

A Methodology for Translating C-Programs to OpenCL

Krishnahari Thouti, S. R. Sathe

View

Download (PDF)

Tags: Code generation, Compilers, Computer science, nVidia, nVidia Quadro FX 3800, OpenCL, Programming Languages

November 16, 2013 by hgpu

SAGE: Self-Tuning Approximation for Graphics Engines

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

View

Download (PDF)

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

View

Download (PDF)

Source codes

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

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

* * *

high performance computing on graphics processing units: hgpu.org

An Automated Tool for Converting Directive Based C Code Into Parallel CUDA Code

Code Generation for Embedded Heterogeneous Architectures on Android

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Increasing programmability of an embedded domain specific language for GPGPU kernels using static analysis

Automatic Resource-Constrained Static Task Parallelization

High Performance Code Generation for Stencil Computation on Heterogeneous Multi-device Architectures

A Framework for Productive, Efficient and Portable Parallel Computing

DEF-G: Declarative Framework for GPU Environment

Multilayered Abstractions for Partial Differential Equations

A Methodology for Translating C-Programs to OpenCL

SAGE: Self-Tuning Approximation for Graphics Engines

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

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

Most viewed papers (last 30 days)