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hgpu.org » Programming » CUDA

SAGA: SystemC Acceleration on GPU Architectures

Sara Vinco, Debapriya Chatterjee, Valeria Bertacco, Franco Fummi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance

June 14, 2012 by hgpu

Performance Gains in Conjugate Gradient Computation with Linearly Connected GPU Multiprocessors

Stephen J. Tarsa, Tsung-han Lin, H.T. Kung

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 570

June 14, 2012 by hgpu

Exploiting Unexploited Computing Resources for Computational Logics

Alessandro Dal Palu, Agostino Dovier, Andrea Formisano, Enrico Pontelli

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 560, SAT, Tesla C2075

June 14, 2012 by hgpu

Parakeet: A Just-In-Time Parallel Accelerator for Python

Alex Rubinsteyn, Eric Hielscher, Nathaniel Weinman, Dennis Shasha

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Tags: Computer science, CUDA, nVidia, Python, Tesla C1060

June 14, 2012 by hgpu

Using Fermi architecture knowledge to speed up CUDA and OpenCL programs

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Performance

June 13, 2012 by hgpu

Experiences with High-Level Programming Directives for Porting Applications to GPUs

Oscar Hernandez, Wei Ding, Barbara Chapman, Ramanan Sankaran, and Richard Graham

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Tags: Computer science, CUDA, nVidia, OpenCL, Tesla C2070

June 13, 2012 by hgpu

A comparison of CPU and GPU performance for Fourier pseudospectral simulations of the Navier-Stokes, Cubic Nonlinear Schrodinger and Sine Gordon Equations

B. Cloutier, B.K. Muite, P. Rigge

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Tags: Algorithms, CUDA, FFT, Fortran, Navier-Stokes equations, nVidia, OpenACC, OpenMP, Physics, Spectral methods, Tesla M2070

June 13, 2012 by hgpu

Fluid Dynamics Simulations on Multi-GPU Systems

Eugenio Rustico

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Tags: CUDA, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 480, Thesis

June 13, 2012 by hgpu

Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation

Holger Schwenk, Anthony Rousseau, Mohammed Attik

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Tags: Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Package, Speech recognition, Tesla M2090

June 11, 2012 by hgpu

GPUSync: Architecture-Aware Management of GPUs for Predictable Multi-GPU Real-Time Systems

Glenn A. Elliott, Bryan C. Ward, James H. Anderson

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470, Package, Performance

June 11, 2012 by hgpu

Range query processing in a multi-GPU environment

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Databases, GPU cluster, nVidia, OpenMP, Tesla C1060

June 11, 2012 by hgpu

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Federico Raimondo, Juan Kamienkowski, Mariano Sigman, Diego Fernandez Slezak

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Tags: Algorithms, Biology, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neuroscience, nVidia, nVidia GeForce GTX 560, Package, Tesla C2070

June 11, 2012 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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high performance computing on graphics processing units: hgpu.org

Programming

SAGA: SystemC Acceleration on GPU Architectures

Performance Gains in Conjugate Gradient Computation with Linearly Connected GPU Multiprocessors

Exploiting Unexploited Computing Resources for Computational Logics

Parakeet: A Just-In-Time Parallel Accelerator for Python

Using Fermi architecture knowledge to speed up CUDA and OpenCL programs

Experiences with High-Level Programming Directives for Porting Applications to GPUs

A comparison of CPU and GPU performance for Fourier pseudospectral simulations of the Navier-Stokes, Cubic Nonlinear Schrodinger and Sine Gordon Equations

Fluid Dynamics Simulations on Multi-GPU Systems

Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation

GPUSync: Architecture-Aware Management of GPUs for Predictable Multi-GPU Real-Time Systems

Range query processing in a multi-GPU environment

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

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