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Warped Register File: A Power Efficient Register File for GPGPUs

Mohammad Abdel-Majeed, Murali Annavaram

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, Performance

January 28, 2013 by hgpu

Reaction-diffusion model Monte Carlo simulations on the GPU

Raoul D. Schram

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Tags: Computational Physics, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, OpenCL, Physics, Statistical Mechanics

January 28, 2013 by hgpu

GPUfs: Integrating a File System with GPUs

Mark Silberstein, Bryan Ford, Idit Keidar, Emmett Witchel

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Tags: Computer science, CUDA, nVidia, Operating systems, Tesla C2075

January 26, 2013 by hgpu

Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Ishan Rajani, G Nanda Gopal

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Tags: AMD FireStream 9270, ATI, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Warp level parallelism

January 26, 2013 by hgpu

Selection algorithm of graphic accelerators in heterogeneous cluster for optimization computing

A. M. Lavreniuk, M. S. Lavreniuk

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, OpenCL, Tesla M2050

January 26, 2013 by hgpu

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Malik Muhammad Zaki Murtaza Khan

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Tags: Code generation, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050, Thesis

January 26, 2013 by hgpu

Automatic Parallelization for GPUs

Thomas B. Jablin

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Programming techniques, Thesis

January 26, 2013 by hgpu

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Jan Verschelde, Genady Yoffe

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Tags: Algorithms, CUDA, Extended precision, Mathematics, nVidia, Tesla C2050

January 25, 2013 by hgpu

Regularization and nonlinearities for neural language models: when are they needed?

Marius Pachitariu, Maneesh Sahani

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Tags: CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 680, Statistics

January 25, 2013 by hgpu

Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures

Thierry Gautier, Joao V. F. Lima, Nicolas Maillard, Bruno Raffin

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Source codes

Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Package, Tesla C2050

January 25, 2013 by hgpu

Vlasov on GPU (VOG Project)

M. Mehrenberger, C. Steiner, L. Marradi, N. Crouseilles, E. Sonnendrucker, B. Afeyan

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Tags: FFT, Numerical simulation, nVidia, nVidia GeForce 9400 M, nVidia GeForce GTX 470, OpenCL, Physics, Plasma physics, Tesla C2070

January 25, 2013 by hgpu

A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

Weihua Geng, Ferosh Jacob

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Tags: Biomolecules, CUDA, Electrostatics, Linear Algebra, Mathematics, Molecular surface, Numerical Analysis, nVidia, Poisson Boltzmann, Tesla M2070

January 25, 2013 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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Warped Register File: A Power Efficient Register File for GPGPUs

Reaction-diffusion model Monte Carlo simulations on the GPU

GPUfs: Integrating a File System with GPUs

Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Selection algorithm of graphic accelerators in heterogeneous cluster for optimization computing

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Automatic Parallelization for GPUs

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Regularization and nonlinearities for neural language models: when are they needed?

Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures

Vlasov on GPU (VOG Project)

A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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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