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Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

Yannik Könneker

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, OpenACC, OpenCL, Package, Performance, Tesla V100, Thesis

April 17, 2022 by hgpu

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

Navdeep Katel, Vivek Khandelwal, Uday Bondhugula

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Tags: Code generation, Computer science, CUBLAS, CUDA, HPC, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Package

September 5, 2021 by hgpu

CoCoNet: Co-Optimizing Computation and Communication for Distributed Machine Learning

Abhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet, Saeed Maleki, Youshan Miao, Madanlal Musuvathi, Todd Mytkowicz, Olli Sarikivi

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Tags: Code generation, Computer science, CUBLAS, CUDA, Deep learning, Machine learning, nVidia, nVidia DGX-2

May 23, 2021 by hgpu

SLATE port to AMD and Intel platforms

Ahmad Abdelfattah, Mohammed Al Farhan, Cade Brown, Mark Gates, Dalal Sukkari, Asim YarKhan, Jack Dongarra

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, OpenCL, Package, SYCL

April 18, 2021 by hgpu

Accelerating Sparse Approximate Matrix Multiplication on GPUs

Xiaoyan Liu, Yi Liu, Ming Dun, Bohong Yin, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla V100

March 28, 2021 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

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Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

Automatic Kernel Generation for Volta Tensor Cores

Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover

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Tags: Compilers, Computer science, CUBLAS, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia Quadro GV100, Performance, Programming Languages, PTX

June 28, 2020 by hgpu

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Shaohuai Shi, Qiang Wang, Xiaowen Chu

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Sparse matrix, Tesla P100

June 7, 2020 by hgpu

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels, Rastislav Bodik, Vinod Grover

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce RTX 2080 Ti, nVidia Quadro GV100, Programming Languages

March 22, 2020 by hgpu

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Dominik Ernst, Georg Hager, Jonas Thies, Gerhard Wellein

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Performance, Tesla V100

May 12, 2019 by hgpu

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Tesla V100

September 2, 2018 by hgpu

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

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Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

CoCoNet: Co-Optimizing Computation and Communication for Distributed Machine Learning

SLATE port to AMD and Intel platforms

Accelerating Sparse Approximate Matrix Multiplication on GPUs

Flexible Performant GEMM Kernels on GPUs

Automatic Kernel Generation for Volta Tensor Cores

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

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Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

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