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Accelerating Winograd Convolutions using Symbolic Computation and Meta-programming

Arya Mazaheri, Tim Beringer, Matthew Moskewicz, Felix Wolf, Ali Jannesari

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Tags: AMD Radeon RX 580, ATI, Code generation, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance, performance portability, Symbolic Computation, Vulkan

April 26, 2020 by hgpu

OpenCL-Darknet: implementation and optimization of OpenCL-based deep learning object detection framework

Yongbon Koo, Sunghoon Kim, Young-guk Ha

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Tags: AMD Radeon R7, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

April 19, 2020 by hgpu

Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge

Anirban Bhattacharjee, Ajay Dev Chhokra, Hongyang Sun, Shashank Shekhar, Aniruddha Gokhale, Gabor Karsai, Abhishek Dubey

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia Jetson TX2, Performance

April 19, 2020 by hgpu

MNN: A Universal and Efficient Inference Engine

Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lv, Zhihua Wu

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package, Vulkan

April 12, 2020 by hgpu

Neural Architecture Search for Lightweight Non-Local Networks

Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille

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Tags: Algorithms, Computer science, Deep learning, Neural networks, nVidia, Package, TensorFlow, Tesla V100

April 12, 2020 by hgpu

Deep Learning for Compilers

Chris Cummins

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Tags: AMD Radeon HD 7970, ATI, Benchmarking, Compilers, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Thesis

April 5, 2020 by hgpu

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

Nicolas Weber, Felipe Huici

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V100, nVidia Quadro P 4000, PyTorch

March 29, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

Learnergy: Energy-based Machine Learners

Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, Machine learning, Neural networks, nVidia, Package, Python, PyTorch

March 22, 2020 by hgpu

Performance evaluation of deep learning on smartphones

Abhishek Srivastava

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Tags: Benchmarking, Computer science, Deep learning, TensorFlow, Thesis

March 22, 2020 by hgpu

ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search

Xu Zhang, Chenjun Zhou, Bo Gu

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Tags: Computer science, Deep learning, Neural networks

March 8, 2020 by hgpu

Accelerating CNN on FPGA: An Implementation of MobileNet on FPGA

Yulan Shen

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Tags: classification, Computer science, Computer vision, Deep learning, FPGA, Image processing, nVidia, nVidia GeForce GTX 940 M, Thesis

March 1, 2020 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Accelerating Winograd Convolutions using Symbolic Computation and Meta-programming

OpenCL-Darknet: implementation and optimization of OpenCL-based deep learning object detection framework

MNN: A Universal and Efficient Inference Engine

Neural Architecture Search for Lightweight Non-Local Networks

Deep Learning for Compilers

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Learnergy: Energy-based Machine Learners

Performance evaluation of deep learning on smartphones

ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search

Accelerating CNN on FPGA: An Implementation of MobileNet on FPGA

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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