high performance computing on graphics processing units: hgpu.org

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GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Syed Tahir Hussain Rizvi, Gianpiero Cabodi, Denis Patti, Gianluca Francini

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Tags: CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, Matrix multiplication, Neural networks, nVidia, nVidia Jetson TK1

December 10, 2016 by hgpu

dMath: Distributed Linear Algebra for DL

Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale

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Tags: Algorithms, Computer science, CUDA, Deep learning, Linear Algebra, Matrix multiplication, MPI, Neural networks, nVidia, Tesla K80

November 25, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Tarun Beri

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Tags: Computer science, CUDA, FFT, GPU cluster, Linear Algebra, Matrix multiplication, nVidia, Package, Prefetch, Tesla M2070, Thesis

November 5, 2016 by hgpu

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

Ryotaro Sakai, Fumihiko Ino, Kenichi Hagihara

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Performance, Tesla K40

November 1, 2016 by hgpu

Accelerating BLAS on Custom Architecture through Algorithm-Architecture Co-design

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

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Tags: Algorithms, BLAS, Computer science, CUDA, FPGA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2050

October 29, 2016 by hgpu

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

David Richie, James Ross

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Tags: Algorithms, ARM, Computer science, Matrix multiplication, Memory model, OpenCL, Package

August 16, 2016 by hgpu

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Michel Steuwer, Toomas Remmelg, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, BLAS, Code generation, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan Black, OpenCL, performance portability

July 8, 2016 by hgpu

Persistent RNNs: Stashing Recurrent Weights On-Chip

Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh

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Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Speech recognition

June 30, 2016 by hgpu

First Application of Lattice QCD to Pezy-SC Processor

Tatsumi Aoyama, Ken-Ichi Ishikawa, Yasuyuki Kimura, Hideo Matsufuru, Atsushi Sato, Tomohiro Suzuki, Sunao Torii

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Tags: High Energy Physics - Lattice, Matrix multiplication, OpenCL, Physics, QCD

June 14, 2016 by hgpu

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Vi Ngoc-Nha Tran, Phuong Hoai Ha

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Sparse matrix

May 28, 2016 by hgpu

Improving GPU Performance: Reducing Memory Conflicts and Latency

Gert-Jan van den Braak

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Matrix multiplication, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Thesis

May 11, 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

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

dMath: Distributed Linear Algebra for DL

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

Accelerating BLAS on Custom Architecture through Algorithm-Architecture Co-design

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Persistent RNNs: Stashing Recurrent Weights On-Chip

First Application of Lattice QCD to Pezy-SC Processor

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Improving GPU Performance: Reducing Memory Conflicts and Latency

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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