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Scaling Deep Learning on Multiple In-Memory Processors

Lifan Xu, Dong Ping Zhang, Nuwan Jayasena

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Tags: ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, OpenCL

December 8, 2015 by hgpu

Nonlinear Dynamic Analysis Efficiency by Using a GPU Parallelization

Hong-yu Li, Jun Teng, Zuo-hua Li, Lu Zhang

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GT 430, nVidia GeForce GT 720 M, nVidia GeForce GTX 460

December 8, 2015 by hgpu

Using Data Compression for Increasing Efficiency of Data Transfer Between Main Memory and Intel Xeon Phi Coprocessor or NVidia GPU in Parallel DBMS

Konstantin Y. Besedin, Pavel S. Kostenetskiy, Stepan O. Prikazchikov

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Tags: Algorithms, Compression, Computer science, CUDA, Databases, Intel Xeon Phi, nVidia, OpenMP, Tesla K40

December 6, 2015 by hgpu

A Study of Parallel Sorting Algorithms Using CUDA and OpenMP

Hakan Gokahmetoglu

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Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M, OpenMP, Sorting, Thesis

December 6, 2015 by hgpu

CuMF: scale matrix factorization using just ONE machine with GPUs

Wei Tan, Liangliang Cao

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Tags: Computer science, CUDA, Data parallelism, Factorization, Linear Algebra, nVidia, nVidia GeForce GTX Titan X

December 6, 2015 by hgpu

Parallelization Methods of the Template Matching Method on Graphics Accelerators

Gabor Kertesz, Sandor Szenasi, Zoltan Vamossy

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, Tesla K40

December 6, 2015 by hgpu

The Genetic Convolutional Neural Network Model Based on Random Sample

You Zhining, Pu Yunming

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Tags: Algorithms, CNN, Computer science, CUDA, Deep learning, Genetic algorithm, Neural networks, nVidia, nVidia GeForce GTX 770

December 4, 2015 by hgpu

An Accelerator based on the rho-VEX Processor: an Exploration using OpenCL

Hugo van der Wijst

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Tags: Computer science, FPGA, hardware acceleration, OpenCL, Thesis

December 4, 2015 by hgpu

Optimizing CUDA Shared Memory Usage

Shuang Gao, Gregory D. Peterson

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Tags: Computer science, CUDA, nVidia, Optimization, Poster

December 4, 2015 by hgpu

Nested Parallelism on GPU: Exploring Parallelization Templates for Irregular Loops and Recursive Computations

Da Li, Hancheng Wu, Michela Becchi

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Tags: Computer science, nVidia, OpenCL, Tesla K20

December 4, 2015 by hgpu

An Efficient Parallel Algorithm for Graph Isomorphism on GPU using CUDA

Min-Young Son, Young-Hak Kim, Byoung-Woo Oh

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 670

December 4, 2015 by hgpu

Efficient Static and Dynamic Memory Management Techniques for Multi-GPU Systems

Max Grossman, Mauricio Araya-Polo

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K10

December 1, 2015 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

Scaling Deep Learning on Multiple In-Memory Processors

Nonlinear Dynamic Analysis Efficiency by Using a GPU Parallelization

Using Data Compression for Increasing Efficiency of Data Transfer Between Main Memory and Intel Xeon Phi Coprocessor or NVidia GPU in Parallel DBMS

A Study of Parallel Sorting Algorithms Using CUDA and OpenMP

CuMF: scale matrix factorization using just ONE machine with GPUs

Parallelization Methods of the Template Matching Method on Graphics Accelerators

The Genetic Convolutional Neural Network Model Based on Random Sample

An Accelerator based on the rho-VEX Processor: an Exploration using OpenCL

Optimizing CUDA Shared Memory Usage

Nested Parallelism on GPU: Exploring Parallelization Templates for Irregular Loops and Recursive Computations

An Efficient Parallel Algorithm for Graph Isomorphism on GPU using CUDA

Efficient Static and Dynamic Memory Management Techniques for Multi-GPU Systems

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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