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Heterogeneous Computing for Solving System of the Linear Equations by the Conjugate Gradient Method

Eduard Bondarenko

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Tags: Computer science, Conjugate gradient solver, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 650 Ti, OpenACC

May 29, 2014 by hgpu

Optimizing Krylov Subspace Solvers on Graphics Processing Units

Hartwig Anzt, Stanimire Tomov, Piotr Luszczek, Ichitaro Yamazaki, Jack Dongarra, William Sawyer

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 6, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Large-Scale Paralleled Sparse Principal Component Analysis

W. Liu, H. Zhang, D. Tao, Y. Wang, K. Lu

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Numerical Analysis, nVidia, Tesla C2050

December 24, 2013 by hgpu

Resource Centered Computing delivering high parallel performance

Jens Gustedt, Stephane Vialle, Patrick Mercier

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Tags: Computer science, CUBLAS, CUDA, GPU cluster, Hierarchical clustering, MPI, nVidia, nVidia GeForce GTX 580

December 22, 2013 by hgpu

Efficient Heterogeneous Execution on Large Multicore and Accelerator Platforms: Case Study Using a Block Tridiagonal Solver

Alfred J. Park, Kalyan S. Perumalla

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Tags: Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Tesla X2090

August 20, 2013 by hgpu

GPU Implementation of the DP code

F. Sottile, C. Roedl, V. Slavnic, P. Jovanovic, D. Stankovic, P. Kestener, F. Houssen

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Tags: Computer science, CUBLAS, CUDA, nVidia, Tesla M2090

June 23, 2013 by hgpu

Making the case of GPUs in courses on computational physics

Knut Skogstrand Gjerden

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Tags: Computational Physics, CUBLAS, CUDA, Education, FFT, Mathematical Software, nVidia, nVidia GeForce GTX 320 M, Physics, Tesla C2050

May 17, 2013 by hgpu

Optimizing CUDA Code By Kernel Fusion – Application on BLAS

Jiri Filipovic, Matus Madzin, Jan Fousek, Ludek Matyska

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

May 7, 2013 by hgpu

Batched Kronecker product for 2-D matrices and 3-D arrays on NVIDIA GPUs

Chetan Jhurani

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Tags: BLAS, CUBLAS, CUDA, Kronecker product, nVidia, Tesla K20

April 10, 2013 by chetan.jhurani

A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices

Chetan Jhurani, Paul Mullowney

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Tags: BLAS, CUBLAS, CUDA, Dense linear algebra, GEMM, Linear Algebra, nVidia, Parallel programming, Tesla K20

April 9, 2013 by chetan.jhurani

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

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high performance computing on graphics processing units: hgpu.org

Heterogeneous Computing for Solving System of the Linear Equations by the Conjugate Gradient Method

Optimizing Krylov Subspace Solvers on Graphics Processing Units

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Large-Scale Paralleled Sparse Principal Component Analysis

Resource Centered Computing delivering high parallel performance

Efficient Heterogeneous Execution on Large Multicore and Accelerator Platforms: Case Study Using a Block Tridiagonal Solver

GPU Implementation of the DP code

Making the case of GPUs in courses on computational physics

Optimizing CUDA Code By Kernel Fusion – Application on BLAS

Batched Kronecker product for 2-D matrices and 3-D arrays on NVIDIA GPUs

A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices

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

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