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EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Vi Ngoc-Nha Tran, Phuong Hoai Ha

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Sparse matrix

May 28, 2016 by hgpu

Improving GPU Performance: Reducing Memory Conflicts and Latency

Gert-Jan van den Braak

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Matrix multiplication, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Thesis

May 11, 2016 by hgpu

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Steven Eliuk, Cameron Upright, Anthony Skjellum

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, OpenMPI, Tesla K80

April 9, 2016 by hgpu

Wanted: Floating-Point Add Round-off Error instruction

Marat Dukhan, Richard Vuduc, Jason Riedy

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Tags: Computer science, CUDA, Matrix multiplication, OpenCL, Package

March 25, 2016 by hgpu

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Weifeng Liu

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Tags: Algorithms, AMD Radeon R9 290X, ATI, Benchmarking, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan Black, OpenCL, Sparse matrix, Thesis

February 16, 2016 by hgpu

Writing a performance-portable matrix multiplication

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela

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Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20

February 11, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

Performance Portable GPU Code Generation for Matrix Multiplication

Toomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan Black, OpenCL

February 10, 2016 by hgpu

High-Performance Tensor Contractions for GPUs

A. Abdelfattah, M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, Tz. Kolev, I. Masliah, S. Tomov

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Tags: Algorithms, Code generation, Computer science, CUDA, FEM, Finite element method, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Tesla K40

January 29, 2016 by hgpu

Large Scale Artificial Neural Network Training Using Multi-GPUs

Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan X, Tesla K40

November 20, 2015 by hgpu

GEMMbench: a framework for reproducible and collaborative benchmarking of matrix multiplication

Anton Lokhmotov

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Tags: ARM, Benchmarking, Computer science, Linear Algebra, Mathematical Software, Matrix multiplication, OpenCL, Package, Performance

November 13, 2015 by hgpu

High Performance Adaptive Image Processing on Multi-scale Hybrid Architectures

Fangbin Liu

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Tags: Algorithms, CUDA, Distributed computing, Heterogeneous systems, Image processing, Matrix multiplication, nVidia, nVidia GeForce GTX 295, Thesis

November 10, 2015 by hgpu

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

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Improving GPU Performance: Reducing Memory Conflicts and Latency

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Wanted: Floating-Point Add Round-off Error instruction

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Writing a performance-portable matrix multiplication

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Performance Portable GPU Code Generation for Matrix Multiplication

High-Performance Tensor Contractions for GPUs

Large Scale Artificial Neural Network Training Using Multi-GPUs

GEMMbench: a framework for reproducible and collaborative benchmarking of matrix multiplication

High Performance Adaptive Image Processing on Multi-scale Hybrid Architectures

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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