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Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Devi Abhiseshu Adurti, Mohit Battu

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 1050 Ti, Thesis

July 4, 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

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Beau Johnston, Jeffrey S. Vetter, Josh Milthorpe

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Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, CUDA, Heterogeneous systems, HIP, Matrix multiplication, nVidia, OpenCL, Package, Performance, Tesla P100

November 29, 2020 by hgpu

OpenCL Performance on the Intel Heterogeneous Architecture Research Platform

Steven Harris, Roger D. Chamberlain, Christopher Gill

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Tags: Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Optimization, performance portability

October 25, 2020 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

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Orestis Zachariadis, Nitin Satpute, Juan Gómez-Luna, Joaquín Olivares

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2070, nVidia Titan RTX, Package, Performance, Sparse matrix

October 4, 2020 by hgpu

Heterogeneous parallel computing for image registration and linear algebra applications

Orestis Zachariadis

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Tags: CUDA, Heterogeneous systems, Image processing, Image registration, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1050, nVidia GeForce RTX 2070, Package, Sparse matrix, Thesis

August 9, 2020 by hgpu

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Zijing Gu

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, Package, Sparse matrix, Tesla T4

August 2, 2020 by hgpu

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080, Package, Sparse matrix

July 12, 2020 by hgpu

Sparse GPU Kernels for Deep Learning

Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, Sparse matrix, Tesla V100

June 28, 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

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Steven Harris

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Tags: ASIC, Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Performance, performance portability, Thesis

June 7, 2020 by hgpu

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

Face.evoLVe: A High-Performance Face Recognition Library

OpenCLDecompiler

A method for decompilation of AMD GCN kernels to OpenCL

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

StreamBlocks: A compiler for heterogeneous dataflow computing

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

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high performance computing on graphics processing units: hgpu.org

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Accelerating Sparse Approximate Matrix Multiplication on GPUs

Evaluating the Performance and Portability of Contemporary SYCL Implementations

OpenCL Performance on the Intel Heterogeneous Architecture Research Platform

Flexible Performant GEMM Kernels on GPUs

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Heterogeneous parallel computing for image registration and linear algebra applications

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Sparse GPU Kernels for Deep Learning

Automatic Kernel Generation for Volta Tensor Cores

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Recent source codes

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

OpenCLDecompiler

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

StreamBlocks: A compiler for heterogeneous dataflow computing

Bempp-cl: a boundary element method library

futhark-stencil-benchmarks

POCL: Portable Computing Language

K-FAC: PyTorch Distributed K-FAC Preconditioner

pystencils

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

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Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020