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Pretty Good Accuracy in Matrix Multiplication with GPUs

Matthew Badin, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285

April 21, 2011 by hgpu

A performance prediction model for the CUDA GPGPU platform

Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Performance

April 21, 2011 by hgpu

Compressing Floating-Point Number Stream for Numerical Applications

Hisanobu Tomari, Mary Inaba, Kei Hiraki

Tags: Algorithms, Compression, Computer science, FFT, FPGA, Matrix multiplication

April 20, 2011 by hgpu

Linear optimization on modern GPUs

Daniele G. Spampinato, Anne C. Elster

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Tags: CUDA, Finance, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Optimization

April 18, 2011 by hgpu

A high-performance fault-tolerant software framework for memory on commodity GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Fault simulation, FFT, Matrix multiplication, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

April 14, 2011 by hgpu

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Noboru Tanabe, Yuuka Ogawa, Masami Takata, Kazuki Joe

Tags: Computer science, Heterogeneous systems, Matrix multiplication, Sparse matrix

April 14, 2011 by hgpu

Speeding up K-Means Algorithm by GPUs

You Li, Kaiyong Zhao, Xiaowen Chu, Jiming Liu

Tags: Algorithms, Computer science, Matrix multiplication, Optimization

April 13, 2011 by hgpu

Dense linear algebra solvers for multicore with GPU accelerators

Stanimire Tomov, Rajib Nath, Hatem Ltaief, Jack Dongarra

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Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Package

March 23, 2011 by hgpu

Improving accuracy for matrix multiplications on GPUs

Matthew Badin, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Performance

March 22, 2011 by hgpu

Dense Matrix Algebra on the GPU

Adam Moravanszky

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Tags: ATI, ATI Radeon 9500 Pro, ATI Radeon 9700 Pro, Computer science, DirectX, Linear Algebra, Matrix multiplication

February 24, 2011 by hgpu

Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Byunghyun Jang, Dana Schaa, Perhaad Mistry, and David Kaeli

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Tags: Computer science, Matrix multiplication, Memory model, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 285, OpenCL, Optimization

February 16, 2011 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

Pretty Good Accuracy in Matrix Multiplication with GPUs

A performance prediction model for the CUDA GPGPU platform

Compressing Floating-Point Number Stream for Numerical Applications

Linear optimization on modern GPUs

A high-performance fault-tolerant software framework for memory on commodity GPUs

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Speeding up K-Means Algorithm by GPUs

Dense linear algebra solvers for multicore with GPU accelerators

Improving accuracy for matrix multiplications on GPUs

Dense Matrix Algebra on the GPU

Evaluating one-sided programming models for GPU cluster computations

Static Memory Access Pattern Analysis on a Massively Parallel GPU

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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