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An Optimized GPU Memory Hierarchy Design for an OpenCL Kernel

Numair Khan

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Tags: Computer science, GPGPU-sim, Memory, OpenCL

December 16, 2014 by hgpu

MatConvNet – Convolutional Neural Networks for MATLAB

Andrea Vedaldi, Karel Lenc

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, Neural networks, nVidia

December 16, 2014 by hgpu

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

Minjie Wang, Tianjun Xiao, Jianpeng Li, Jiaxing Zhang, Chuntao Hong, Zheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

December 15, 2014 by hgpu

Analysis and Optimization Techniques for Massively Parallel Processors

Wenhao Jia

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, PTX, Tesla C2070, Thesis, Virtualization

December 15, 2014 by hgpu

Easy-to-Use On-the-Fly Binary Program Acceleration on Many-Cores

Marvin Damschen, Christian Plessl

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Source codes

Tags: Computer science, Intel Xeon Phi, Package, Performance

December 15, 2014 by hgpu

Performance Comparison of GPUs with a Genetic Algorithm based on CUDA

Jae-Hyun Seo, Eun-Sol Ko, Yong-Hyuk Kim

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Tags: Algorithms, Computer science, CUDA, Genetic algorithm, nVidia, nVidia GeForce GT 430, nVidia GeForce GTX 260, nVidia GeForce GTX 760, nVidia GeForce GTX 770

December 15, 2014 by hgpu

Bamboo: Automatic Translation of MPI Source into a Latency-Tolerant Form

Nhat Tan Nguyen Thanh

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Source codes

Tags: Computer science, CUDA, Linear Algebra, MPI, nVidia, Package, Tesla K20, Thesis

December 15, 2014 by hgpu

Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Jose Ritomar Carneiro Torquato, Esteban Walter Gonzalez Clua

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance, Tesla K10, Tesla K20

December 14, 2014 by hgpu

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Jose M. Andion, Manuel Arenaz, Francois Bodin, Gabriel Rodriguez, Juan Tourino

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, Tesla S1070, Tesla S2050

December 14, 2014 by hgpu

Acceleration of Hessenberg Reduction for Nonsymmetric Matrix

Hesamaldin Nekouei

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Tags: Algorithms, Computer science, CUDA, FFT, Linear Algebra, nVidia, nVidia GeForce GT 635 M, Thesis

December 14, 2014 by hgpu

Graph Processing on GPU

Zhang Jingbo

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Tags: Algorithms, Computer science, CUDA, Data mining, Graph theory, Machine learning, nVidia, nVidia GeForce 9400 GT, nVidia GeForce GT 330, nVidia GeForce GT 520, Thesis

December 14, 2014 by hgpu

Real-Time Grasp Detection Using Convolutional Neural Networks

Joseph Redmon, Anelia Angelova

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Tags: CNN, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla K20

December 12, 2014 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

An Optimized GPU Memory Hierarchy Design for an OpenCL Kernel

MatConvNet – Convolutional Neural Networks for MATLAB

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

Analysis and Optimization Techniques for Massively Parallel Processors

Easy-to-Use On-the-Fly Binary Program Acceleration on Many-Cores

Performance Comparison of GPUs with a Genetic Algorithm based on CUDA

Bamboo: Automatic Translation of MPI Source into a Latency-Tolerant Form

Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Acceleration of Hessenberg Reduction for Nonsymmetric Matrix

Graph Processing on GPU

Real-Time Grasp Detection Using Convolutional Neural Networks

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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