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Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Franz Xaver Bayerl

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Tags: Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 560 Ti, Performance, Thesis

April 19, 2012 by hgpu

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

Hans Henrik Brandenborg Sorensen

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

April 18, 2012 by hgpu

High-Performance Matrix-Vector Multiplication on the GPU

Hans Henrik Brandenborg Sorensen

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

April 18, 2012 by hgpu

Bound the Peak Performance of SGEMM on GPU with software-controlled fast memory

Junjie Lai, Andre Seznec

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Tags: Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 580, Performance

April 7, 2012 by hgpu

Parallel Semi-Implicit Time Integrators

B. Ong, A. Melfi, A. Christlieb

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Partial differential equations, PDEs, Tesla C1060

February 9, 2012 by hgpu

CUDA Expression Templates

Paul Wiemann, Stephan Wenger, Marcus Magnor

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Source codes

Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTS 250, Package

January 30, 2012 by hgpu

Cost Efficient PageRank Computation using GPU

Praveen K., Vamshi Krishna K., Anil Sri Harsha B., S. Balasubramanian, P.K. Baruah

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Tags: Algorithms, Computer science, CUBLAS, CUDA, List ranking, nVidia, Sparse matrix, Tesla T20

January 24, 2012 by hgpu

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Maxim Naumov

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050

January 12, 2012 by hgpu

Applying OOC Techniques in the Reduction to Condensed Form for Very Large Symmetric Eigenproblems on GPUs

Davor Davidovic, Enrique S. Quintana-Orti

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Tags: Algorithms, CUBLAS, CUDA, Linear Algebra, Mathematics, nVidia, Tesla C2050

January 6, 2012 by hgpu

Optimising the DBCSR GPU Implementation

Jay Chetty

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla C2050, Thesis

December 31, 2011 by hgpu

Automatic library generation for BLAS3 on GPUs

Huimin Cui, Lei Wang, Jingling Xue, Yang Yang, Xiaobing Feng

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Tags: Algorithms, Computer science, CUBLAS, Linear Algebra, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 285, Optimization, Tesla C2050

December 13, 2011 by hgpu

Computation of Large Covariance Matrices by SAMMY on Graphical Processing Units and Multicore CPUs

G. Arbanas, M.E. Dunn, D. Wiarda

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Tags: CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, Nuclear Experiment, nVidia, Physics, Tesla C2050

December 3, 2011 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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

High-Performance Matrix-Vector Multiplication on the GPU

Bound the Peak Performance of SGEMM on GPU with software-controlled fast memory

Parallel Semi-Implicit Time Integrators

CUDA Expression Templates

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Applying OOC Techniques in the Reduction to Condensed Form for Very Large Symmetric Eigenproblems on GPUs

Optimising the DBCSR GPU Implementation

Automatic library generation for BLAS3 on GPUs

Computation of Large Covariance Matrices by SAMMY on Graphical Processing Units and Multicore CPUs

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

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

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