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Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Vlad Slavici, Raghu Varier, Gene Cooperman, Robert J. Harrison

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Tags: Chemistry, Computational chemistry, Computer science, CUBLAS, CUDA, GPU cluster, nVidia, Tesla M2090

November 6, 2012 by hgpu

Forecasting high frequency financial time series using parallel FFN with CUDA and ZeroMQ

Paola Arce, Cristian Maureira, Roberto Bonvallet, Cesar Fernandez

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Tags: Algorithms, CUBLAS, CUDA, Finance, Linear Algebra, Machine learning, Neural networks, nVidia, Tesla M2050

September 20, 2012 by hgpu

Systematic Approach in Optimizing Numerical Memory-Bound Kernels on GPU

Ahmad Abdelfattah, David Keyes, Hatem Ltaief

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2070

September 13, 2012 by hgpu

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

Beata Bylina, Jaroslaw Bylina

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Tags: Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla C2050

September 3, 2012 by hgpu

A Unified Optimizing Compiler Framework for Different GPGPU Architectures

Yi Yang, Ping Xiang, Jingfei Kong, Mike Mantor, Huiyang Zhou

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Tags: ATI, ATI Radeon HD 5870, Compilers, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL, Optimization, Package, Programming Languages

August 27, 2012 by hgpu

GPU Accelerated Nonlinear Optimization in Radio Interferometric Calibration

Sarod Yatawatta, Sanaz Kazemi, Saleem Zaroubi

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Tags: Algorithms, Astrophysics, CUBLAS, CUDA, Linear Algebra, Nonlinear optimization, nVidia, Optimization, Tesla M1060

August 26, 2012 by hgpu

Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators

Ahmad Abdelfattah, Jack Dongarra, David Keyes, Hatem Ltaief

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Tags: Computer science, CUBLAS, CUDA, nVidia, Optimization, Package, Tesla C2070

August 10, 2012 by hgpu

Fast Linear Algebra on GPU

Lukas Polok, Pavel Smrz

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Tags: Computer science, CUBLAS, Linear Algebra, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 590, OpenCL

July 28, 2012 by hgpu

Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

A. Barberis, G. Danese, F. Leporati, A. Plaza, E. Torti

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Tags: Algorithms, CUBLAS, CUDA, Data acquisition, Earth and Space Sciences, Geoscience, Linear Algebra, nVidia, nVidia GeForce GTX 460

July 13, 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

A fight for performance and accuracy of the matrix multiplication routines: CUBLAS on Nvidia Tesla versus MKL and ATLAS on Intel Nehalem

Philippe Estival, Luc Giraud

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla T10

May 29, 2012 by hgpu

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)

He Huang, Liqiang Wang, En-Jui Lee, Po Chen

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Tags: Computer science, CUBLAS, CUDA, Earth and Space Sciences, Geoscience, MPI, nVidia, Optimization, Tesla C2050, Tesla M2070

May 19, 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

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

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Forecasting high frequency financial time series using parallel FFN with CUDA and ZeroMQ

Systematic Approach in Optimizing Numerical Memory-Bound Kernels on GPU

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

A Unified Optimizing Compiler Framework for Different GPGPU Architectures

GPU Accelerated Nonlinear Optimization in Radio Interferometric Calibration

Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators

Fast Linear Algebra on GPU

Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

A fight for performance and accuracy of the matrix multiplication routines: CUBLAS on Nvidia Tesla versus MKL and ATLAS on Intel Nehalem

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)

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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