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Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Zhongyi Lin, Louis Feng, Ehsan K. Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan XP, Package, Performance, Tesla P100, Tesla V100

January 23, 2022 by hgpu

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Dengpan Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Operating systems, PTX, SYCL, Thesis

January 16, 2022 by hgpu

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu, Wen-mei Hwu, Izzat El Hajj

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Tags: Compilers, Computer science, CUDA, nVidia, Package, Performance, Tesla V100

January 16, 2022 by hgpu

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Hulin Dai, Xuan Peng, Xuanhua Shi, Ligang He, Qian Xiong, Hai Jin

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, PyTorch, TensorFlow, Tesla P100

January 9, 2022 by hgpu

Analysis of High Level implementations for Recursive Methods on GPUs

Cheng Cao, Justin Kalloor

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Tags: Computer science, CUDA, DSL, nVidia, nVidia GeForce RTX 3080, OpenGL, Vulkan

January 9, 2022 by hgpu

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

Farui Wang, Weizhe Zhang, Shichao Lai, Meng Hao, Zheng Wang

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Package

January 9, 2022 by hgpu

Domain-Specific On-Device Object Detection Method

Seongju Kang, Jaegi Hwang, Kwangsue Chung

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce RTX 2060

January 9, 2022 by hgpu

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Bo Qiao

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Tags: Code generation, Computer science, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce RTX 2080, OpenCL, Package, performance portability, Tesla K20, Thesis

January 2, 2022 by hgpu

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

Shulin Zeng, Guohao Dai, Hanbo Sun, Jun Liu, Shiyao Li, Guangjun Ge, Kai Zhong, Kaiyuan Guo, Yu Wang, Huazhong Yang

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Tags: Cloud, Computer science, CUDA, FPGA, Neural networks, nVidia, Tesla V100, Virtualization

January 2, 2022 by hgpu

A Variant of Concurrent Constraint Programming on GPU

Pierre Talbot, Frederic Pinel, Pascal Bouvry

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Tags: Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Package, Performance

January 2, 2022 by hgpu

COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs

Ruobing Han, Jaewon Lee, Jaewoong Sim, Hyesoon Kim

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia

December 26, 2021 by hgpu

GPU-Smash: Compression abstraction library

A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

Noarr Structures

Abstractions for C++ code optimizations in parallel high-performance applications

$3D-fractal-generators$

HiCCL: A hierarchical collective communications library with portable optimizations

HiCCL: A Hierarchical Collective Communication Library

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

In-browser Inference Measurement

Anatomizing Deep Learning Inference in Web Browsers

See all packages

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high performance computing on graphics processing units: hgpu.org

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Analysis of High Level implementations for Recursive Methods on GPUs

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

Domain-Specific On-Device Object Detection Method

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

GPU-accelerated Faster Mean Shift with euclidean distance metrics

A Variant of Concurrent Constraint Programming on GPU

COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs

Recent source codes

GPU-Smash: Compression abstraction library

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Noarr Structures

3D-fractal-generators

SyncPerformance

VerCors: a verification toolset for verifying parallel and concurrent software

HiCCL: A hierarchical collective communications library with portable optimizations

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

In-browser Inference Measurement

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