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Profiling Concurrent Vision Inference Workloads on NVIDIA Jetson – Extended

Abhinaba Chakraborty, Wouter Tavernier, Akis Kourtis, Mario Pickavet, Andreas Oikonomakis, Didier Colle

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Tags: Computer science, CUDA, nVidia, nVidia Jetson Nano, nVidia Jetson Orin Nano, Performance

August 24, 2025 by hgpu

General-Purpose Computing on Tensor Processors

Kuan-Chieh Hsu

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Tags: Artificial intelligence, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, nVidia Jetson Nano, Package, Sparse matrix, Thesis, TPU

October 27, 2024 by hgpu

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Youssef Faqir-Rhazoui, Carlos García

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Jetson Nano, Optical flow, Package, SYCL

October 22, 2023 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

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

Yanwen Xu, Ang Li, Tyler Sorensen

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, HLS, Intel UHD 630, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, Package, Performance, SYCL

May 7, 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

OpenMP Offloading in the Jetson Nano Platform

Ilias K. Kasmeridis, Vassilios V. Dimakopoulos

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia Jetson Nano, OpenMP, Package

October 16, 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

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang

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Tags: ARM, CNN, Compilers, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson Nano, OpenCL, Package

July 7, 2019 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Profiling Concurrent Vision Inference Workloads on NVIDIA Jetson – Extended

General-Purpose Computing on Tensor Processors

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Performance/power assessment of CNN packages on embedded automotive platforms

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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

OpenMP Offloading in the Jetson Nano Platform

Real-Time High-Performance Computing for Embedded Control Systems

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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