high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Jan Masek, Radim Burget, Lukas Povoda, Malay Kishore Dutta

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Tags: Algorithms, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 690, OpenCL

June 14, 2016 by hgpu

Processing Big Data in Main Memory and on GPU

Meisam Fathi Salmi

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Tags: big data, Computer science, CUDA, Hadoop, Java, OpenCL, Scala, Spark, Thesis

June 14, 2016 by hgpu

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Alberto Magni

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Package, performance portability, Tesla K20, Thesis

June 9, 2016 by hgpu

Decoupled Vector-Fetch Architecture with a Scalarizing Compiler

Yunsup Lee

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Tags: Computer science, CUDA, Electronic design automation, Heterogeneous systems, LLVM, OpenCL, Performance, PTX, SoC, Thesis

June 9, 2016 by hgpu

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Yusuke Nagasaka, Akira Nukada, Satoshi Matsuoka

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

June 9, 2016 by hgpu

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Naila Farooqui

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Tags: ATI, ATI Radeon HD 7770, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, PTX, Tesla C2050, Tesla M2075, Thesis

June 9, 2016 by hgpu

Massively-Parallel Lossless Data Decompression

Evangelia Sitaridi, Rene Mueller, Tim Kaldewey, Guy Lohman, Kenneth Ross

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Tesla K40

June 7, 2016 by hgpu

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Matthew Moskewicz, Forrest Iandola, Kurt Keutzer

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Tags: ARM, CNN, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, Neural networks, OpenCL

June 7, 2016 by hgpu

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Jeffrey Kelling, Geza Odor, Sibylle Gemming

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Tags: Computational Physics, Computer science, CUDA, Materials Science, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce Titan Black, Statistical Mechanics, Tesla K80

June 7, 2016 by hgpu

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bo Yang, Hui Liu, Zhangxin Chen

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Tags: Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

June 7, 2016 by hgpu

MODESTO: Data-centric Analytic Optimization of Complex Stencil Programs on Heterogeneous Architectures

Tobias Gysi, Tobias Grosser, Torsten Hoefler

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Tags: Analytical model, CUDA, nVidia, Optimization, Package, Stencil computation, Tesla K20

June 3, 2016 by gysit

Challenges for a GPU-Accelerated Dynamic Programming Approach for Join-Order Optimization

Andreas Meister, Gunter Saake

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Tags: Algorithms, Computer science, Databases

June 2, 2016 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Programming

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Processing Big Data in Main Memory and on GPU

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Decoupled Vector-Fetch Architecture with a Scalarizing Compiler

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Massively-Parallel Lossless Data Decompression

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

MODESTO: Data-centric Analytic Optimization of Complex Stencil Programs on Heterogeneous Architectures

Challenges for a GPU-Accelerated Dynamic Programming Approach for Join-Order Optimization

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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

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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