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Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Francesco Lumpp

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance, Thesis

April 21, 2024 by hgpu

Performance/power assessment of CNN packages on embedded automotive platforms

Paolo Burgio, Gianluca Brilli

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Tags: CNN, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, nVidia Jetson TX2, Performance

October 22, 2023 by hgpu

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

Zhihe Zhao, Neiwen Ling, Nan Guan, Guoliang Xing

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 2060, nVidia Jetson TX2

July 16, 2023 by hgpu

Towards energy efficiency and productivity for decision making in mobile robot navigation

Denisa-Andreea Constantinescu

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Tags: Computer science, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, nVidia Jetson TX1, nVidia Jetson TX2, OpenCL, Robotics, SYCL

December 11, 2022 by hgpu

Real-Time High-Performance Computing for Embedded Control Systems

Alejandro Josué Calderón Torres

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Tags: Code generation, Computer science, Embedded high-performance computing, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, nVidia Jetson TX2, OpenCL, Tesla T4, Tesla V100, Thesis

August 7, 2022 by hgpu

Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge

Dumitrel Loghin, Tien Tuan Anh Dinh, Aung Maw, Chen Gang, Yong Meng Teo, Beng Chin Ooi

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Tags: Cloud, Computer science, Finance, nVidia, nVidia Jetson TX2, Package

May 22, 2022 by hgpu

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Aditya Rajagopal, Christos-Savvas Bouganis

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Source codes

Tags: AI, Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, Package, Performance, Tesla P40

August 22, 2021 by hgpu

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Hamid Tabani, Fabio Mazzocchetti, Pedro Benedicte, Jaume Abella, Francisco J. Cazorla

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance

April 25, 2021 by hgpu

AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries

Jason Kennedy, Blesson Varghese, Carlos Reaño

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Tags: Cloud, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TX2, Virtualization

March 21, 2021 by hgpu

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

Walther Carballo-Hernández, Maxime Pelcat, François Berry

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia Jetson TX2, Performance

February 7, 2021 by hgpu

Efficient Deep Neural Network Inference for Embedded Systems: A Mixture of Experts Approach

Benjamin David Taylor

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Tags: Algorithms, Cloud, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TX2, Thesis

November 29, 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

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Performance/power assessment of CNN packages on embedded automotive platforms

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

Towards energy efficiency and productivity for decision making in mobile robot navigation

Real-Time High-Performance Computing for Embedded Control Systems

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

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Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

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