high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

Yao Chen, Xin Long, Jiong He, Yuhang Chen, Hongshi Tan, Zhenxiang Zhang, Marianne Winslett, Deming Chen

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Machine learning, nVidia, OpenCL, Tesla P4

May 24, 2020 by hgpu

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Tongsheng Geng, Marcos Amaris, Stephane Zuckerman, Alfredo Goldman, Guang R. Gao, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, Performance, Sparse matrix, Task scheduling, Tesla K20, Tesla K40

May 24, 2020 by hgpu

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

Nathan George

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Tags: Computer science, CUDA, Deep learning, Education, GPU cluster, HPC, nVidia, nVidia GeForce GTX 1080 Ti, nVidia Quadro RTX 5000, Overview

May 24, 2020 by hgpu

Deep Learning with GO

Derek L. Stinson

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Tags: Computer science, CUDA, Deep learning, Go, nVidia, nVidia GeForce GTX 1080 Ti, Package, Thesis

May 17, 2020 by hgpu

Employing OpenCL as a Standard Hardware Abstraction in a Distributed Embedded System: A Case Study

Omair Rafique, Klaus Schneider

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Tags: ARM, Computer science, Heterogeneous systems, OpenCL, Performance

May 17, 2020 by hgpu

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

R. Borrell, D. Dosimont, M. Garcia-Gasulla, G. Houzeaux, O. Lehmkuhl, V. Mehta, H. Owen, M. Vazquez, G. Oyarzun

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Tags: cfd, CUDA, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla V100

May 17, 2020 by hgpu

Task-Based Parallel Strategies for CFD Application in Heterogeneous CPU/GPU Resources

Lucas Leandro Nesi, Matheus S. Serpa, Lucas Mello Schnorr, Philippe Olivier Alexandre Navaux

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Tags: cfd, CUDA, Fluid dynamics, Heterogeneous systems, nVidia, nVidia GeForce GXT 1080 Ti, Package

May 17, 2020 by hgpu

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

Jianbin Fang, Chun Huang, Tao Tang, Zheng Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Programming Languages, Review

May 17, 2020 by hgpu

From Constraint Programming to Heterogeneous Parallelism

Philip Ginsbach

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Tags: AMD Radeon R7, ATI, Code generation, Computer science, DSL, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenMP, Thesis

May 10, 2020 by hgpu

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Mohammadreza Soltaniyeh, Richard P. Martin, Santosh Nagarakatte

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Tags: Computer science, Factorization, FPGA, HLS, Linear Algebra, Matrix multiplication, OpenCL, Sparse

May 10, 2020 by hgpu

Importance of Data Loading Pipeline in Training Deep Neural Networks

Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Tesla V100

May 10, 2020 by hgpu

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Kaijie Fan, Biagio Cosenza, Ben Juurlink

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Tags: Computer science, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla P100

May 10, 2020 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Programming

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

Deep Learning with GO

Employing OpenCL as a Standard Hardware Abstraction in a Distributed Embedded System: A Case Study

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

Task-Based Parallel Strategies for CFD Application in Heterogeneous CPU/GPU Resources

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

From Constraint Programming to Heterogeneous Parallelism

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Importance of Data Loading Pipeline in Training Deep Neural Networks

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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