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Exact diagonalization of the Hubbard model on graphics processing units

Topi Siro, Ari Harju

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Tags: Algorithms, Computational Physics, CUDA, nVidia, Physics, Tesla M2070

April 17, 2012 by hgpu

A pilgrimage to gravity on GPUs

Jeroen Bedorf, Simon Portegies Zwart

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Tags: Algorithms, Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Review

April 17, 2012 by hgpu

High Performance Stencil Code Algorithms for GPGPUs

Andreas Schäfer and Dietmar Fey

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Tags: Computer science, nVidia, nVidia GeForce GTX 480, Tesla C2050

April 17, 2012 by gentryx

Solving incompressible two-phase flows on multi-GPU clusters

Peter Zaspel, Michael Griebel

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Tags: CUDA, Finite difference, Fluid dynamics, GPU cluster, Navier-Stokes equations, nVidia, Tesla C2050, Tesla S1070

April 16, 2012 by hgpu

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Hans Henrik Brandenborg Sorensen

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050

April 16, 2012 by hgpu

Acceleration of CFD and data analysis using graphics processors

Ali Khajeh Saeed

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Tags: ATI, ATI FirePro V7800, ATI Stream, CUDA, Fluid dynamics, nVidia, Tesla C2070, Thesis

April 16, 2012 by hgpu

Fast GPU-based fluid simulations using SPH

Oystein E. Krog, Anne C. Elster

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Tags: CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 470, Package, Tesla C2050

April 16, 2012 by hgpu

Efficient Hash Tables on the GPU

Dan Anthony Feliciano Alcantara

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Tags: Algorithms, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, Thesis

April 14, 2012 by hgpu

An implementation of the tile QR factorization for a GPU and multiple CPUs

Jakub Kurzak, Rajib Nath, Peng Du, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 480

April 14, 2012 by hgpu

Parallelization of PageRank on Multicore Processors

Tarun Kumar, Parikshit Sondhi, Ankush Mittal

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Tags: Algorithms, Cell processor, Computer science, CUDA, nVidia, nVidia GeForce GTX 280

April 14, 2012 by hgpu

phiGEMM: a CPU-GPU library for porting Quantum ESPRESSO on hybrid systems

Filippo Spiga, Ivan Girotto

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Tags: Benchmarking, BLAS, Computer science, CUDA, FFT, Matrix multiplication, nVidia, Package, Tesla C2050

April 14, 2012 by hgpu

GPU parallel computing: Programming language, debugging tools and data structures

Kun Zhou

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Tags: Algorithms, Computer science, CUDA, nVidia, Programming Languages

April 14, 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

Exact diagonalization of the Hubbard model on graphics processing units

A pilgrimage to gravity on GPUs

High Performance Stencil Code Algorithms for GPGPUs

Solving incompressible two-phase flows on multi-GPU clusters

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Acceleration of CFD and data analysis using graphics processors

Fast GPU-based fluid simulations using SPH

Efficient Hash Tables on the GPU

An implementation of the tile QR factorization for a GPU and multiple CPUs

Parallelization of PageRank on Multicore Processors

phiGEMM: a CPU-GPU library for porting Quantum ESPRESSO on hybrid systems

GPU parallel computing: Programming language, debugging tools and data structures

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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