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Tuned and GPU-accelerated parallel data mining from comparable corpora

Krzysztof Wolk, Krzysztof Marasek

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Tags: Algorithms, Computer science, Data mining, Machine learning, NLP, nVidia, nVidia GeForce GTX 660

October 3, 2015 by hgpu

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh

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Tags: ARM, ASIC, Computer science, FPGA, Machine learning, nVidia, nVidia GeForce GTX 650 Ti, nVidia Jetson TK1, Performance, Tesla K40

September 29, 2015 by hgpu

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

John-Alexander M. Assael

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Tags: CNN, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Tesla K40, Thesis

September 26, 2015 by hgpu

A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

Hasmik Osipyan, Martin Krulis, Stephane Marchand-Maillet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Multidimensional scaling, nVidia, nVidia GeForce GT 740 M, Pattern recognition

September 26, 2015 by hgpu

Autotuning Wavefront Patterns for Heterogeneous Architectures

Siddharth Mohanty

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

September 19, 2015 by hgpu

linalg: Matrix Computations in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Sparks, Alexander Ulanov, Matei Zaharia

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Scala, Tesla M2050

September 15, 2015 by hgpu

Refinements in Syntactic Parsing

David Hall

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Tags: Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 690, Package, Scala, Thesis

September 15, 2015 by hgpu

Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems

Fabian Tschopp

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Tags: AMD FirePro W9100, Artificial intelligence, ATI, Caffe, CNN, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Thesis

September 15, 2015 by hgpu

Convolutional Neural Network for Sentence Classification

Yahui Chen

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Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan X, Thesis

September 5, 2015 by hgpu

Partitioning Large Scale Deep Belief Networks Using Dropout

Yanping Huang, Sai Zhang

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Tags: Benchmarking, Databases, Deep learning, Machine learning, nVidia, nVidia GeForce 8800 GS, Python, Statistics

August 31, 2015 by hgpu

High-Speed Object Detection: Design, Study and Implementation of a Detection Framework using Channel Features and Boosting

Tom Runia

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Tags: ATI, ATI Radeon HD 6450, Computer science, Computer vision, Image processing, Machine learning, nVidia, nVidia Jetson TK1, nVidia Tegra, OpenCL, OpenCV, Thesis

August 28, 2015 by hgpu

Accelerated Deep Learning using Intel Xeon Phi

Andre Viebke

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Machine learning, Neural networks, OpenMP, Thesis

August 27, 2015 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

Tuned and GPU-accelerated parallel data mining from comparable corpora

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

Autotuning Wavefront Patterns for Heterogeneous Architectures

linalg: Matrix Computations in Apache Spark

Refinements in Syntactic Parsing

Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems

Convolutional Neural Network for Sentence Classification

Partitioning Large Scale Deep Belief Networks Using Dropout

High-Speed Object Detection: Design, Study and Implementation of a Detection Framework using Channel Features and Boosting

Accelerated Deep Learning using Intel Xeon Phi

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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