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Collaborative design and optimization using Collective Knowledge

Anton Lokhmotov, Grigori Fursin

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Tags: Computer science, Machine learning, OpenCL

February 8, 2016 by hgpu

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Jinhwan Park, Wonyong Sung

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Tags: Computer science, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black

February 6, 2016 by hgpu

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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Tags: Algorithms, Computer science, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla K40

February 6, 2016 by hgpu

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

Gang Mei, Hong Tian

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, Package

February 6, 2016 by hgpu

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

Milan Ceska, Petr Pilar, Nicola Paoletti, Lubos Brim, Marta Kwiatkowska

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Probabilistic model checking

February 6, 2016 by hgpu

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TK1, Sparse matrix

February 6, 2016 by hgpu

A Performance Analysis Framework for Optimizing OpenCL Applications on FPGAs

Zeke Wang, Bingsheng He, Wei Zhang, Shunning Jiang

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Tags: Computer science, FPGA, OpenCL, Performance

February 4, 2016 by hgpu

Deep Learning For Smile Recognition

Patrick O. Glauner

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

February 3, 2016 by hgpu

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Ashwin Trikuta Srinath

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Tags: Algorithms, Computer science, CUDA, Finite difference, Fluid dynamics, GPU cluster, Numerical simulation, nVidia, OpenMPI, Tesla K20, Tesla K40, Thesis

February 3, 2016 by hgpu

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Flavio Vella, Giancarlo Carbone, Massimo Bernaschi

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Graph theory, nVidia, OpenMPI, Tesla K20, Tesla K80

February 3, 2016 by hgpu

Edge coloring in unstructured CFD codes

Andrew Giuliani, Lilia Krivodonova

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Tags: Algorithms, Computer science, Fluid dynamics, nVidia, nVidia GeForce GTX 580

February 3, 2016 by hgpu

Fast, Realistic Terrain Synthesis

Justin Crause

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 660 Ti, Thesis

February 2, 2016 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

Collaborative design and optimization using Collective Knowledge

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Asynchronous Methods for Deep Reinforcement Learning

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

EIE: Efficient Inference Engine on Compressed Deep Neural Network

A Performance Analysis Framework for Optimizing OpenCL Applications on FPGAs

Deep Learning For Smile Recognition

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Edge coloring in unstructured CFD codes

Fast, Realistic Terrain Synthesis

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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