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Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Optimization, Overview, Security

August 7, 2022 by hgpu

DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware

Hanieh Hashemi, Yongqin Wang, Murali Annavaram

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Tags: Algorithms, Computer science, Deep learning, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Security

July 10, 2022 by hgpu

Securing GPU via Region-based Bounds Checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080, OpenCL, Security

June 19, 2022 by hgpu

User’s needs influencing HPC technologies

E. Athanasaki, N. Meyer, M. Cestari, A.Tuncer Durak, A. Tekin, P. Gschwandtner

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Tags: AI, Artificial intelligence, Cloud, Computer science, CUDA, HIP, HPC, nVidia, OpenCL, Security, SYCL

May 29, 2022 by hgpu

Electrical-Level Attacks on CPUs, FPGAs, and GPUs: Survey and Implications in the Heterogeneous Era

Dina G. Mahmoud, Vincent Lenders, Mirjana Stojilović

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Tags: Computer science, FPGA, Heterogeneous systems, Security, survey

February 13, 2022 by hgpu

A Variant RSA Acceleration with Parallelization

Jun-jie Liu, Kang-Too Tsang, Yu-Hui Deng

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Tags: Computer science, CUDA, nVidia, RSA, Security, Tesla P100

November 28, 2021 by hgpu

Homomorphic-Encrypted Volume Rendering

Sebastian Mazza

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Source codes

Tags: Algorithms, AMD Radeon RX 580, ATI, Computer science, GLSL, nVidia, nVidia GeForce RTX 3090, Package, Rendering, Security, Thesis, Visualization, Vulkan

October 17, 2021 by hgpu

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Yue Niu, Ramy E. Ali, Salman Avestimehr

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Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 5000, Security

October 10, 2021 by hgpu

Accelerating Encrypted Computing on Intel GPUs

Yujia Zhai, Mohannad Ibrahim, Yiqin Qiu, Fabian Boemer, Zizhong Chen, Alexey Titov, Alexander Lyashevsky

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Tags: Algorithms, Computer science, Deep learning, Intel, Package, Security, SYCL

October 3, 2021 by hgpu

Accelerating Regular-Expression Matching on FPGAs with High-Level Synthesis

Devon Callanan

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Tags: Computer science, FPGA, OpenCL, Security, Thesis

July 18, 2021 by hgpu

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

Sijun Tan, Brian Knott, Yuan Tian, David J. Wu

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, Neural networks, nVidia, Package, PyTorch, Security, Tesla V100

April 25, 2021 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware

Securing GPU via Region-based Bounds Checking

Electrical-Level Attacks on CPUs, FPGAs, and GPUs: Survey and Implications in the Heterogeneous Era

A Variant RSA Acceleration with Parallelization

Homomorphic-Encrypted Volume Rendering

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Accelerating Encrypted Computing on Intel GPUs

Accelerating Regular-Expression Matching on FPGAs with High-Level Synthesis

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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