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Orca: FSS-based Secure Training with GPUs

Neha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, Rahul Sharma

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Tags: Computer science, CUDA, nVidia, nVidia RTX A6000, Security

February 26, 2023 by hgpu

GPU-based Private Information Retrieval for On-Device Machine Learning Inference

Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, Edward Suh

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Tags: Computer science, Information Retrieval, Machine learning, nVidia, nVidia V100, Security

January 29, 2023 by hgpu

Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Adhokshaj Mishra, Manjesh Kumar Hanawal

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 960 M, Security

October 23, 2022 by hgpu

Behavioral graph fraud detection in E-commerce

Hang Yin, Zitao Zhang, Zhurong Wang, Yilmazcan Ozyurt, Weiming Liang, Wenyu Dong, Yang Zhao, Yinan Shan

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Tags: Clustering, Finance, Machine learning, Neural networks, Security

October 23, 2022 by hgpu

Decompiling x86 Deep Neural Network Executables

Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package, Security

October 9, 2022 by hgpu

cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs

Tao Lu, Chengkun Wei, Ruijing Yu, Yi Chen, Li Wang, Chaochao Chen, Zeke Wang, and Wenzhi Chen

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Elliptic curves, Machine learning, nVidia, nVidia GeForce GTX 3060, Security, Tesla V100

October 9, 2022 by hgpu

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

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Clava: C/C++ Source-to-Source Tool based on Clang

Challenges and Opportunities in C/C++ Source-To-Source Compilation

gpuLik

Statistical Computing With Graphics Processing Units

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

See all packages

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high performance computing on graphics processing units: hgpu.org

Orca: FSS-based Secure Training with GPUs

GPU-based Private Information Retrieval for On-Device Machine Learning Inference

Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Behavioral graph fraud detection in E-commerce

Decompiling x86 Deep Neural Network Executables

cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs

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

Recent source codes

Clava: C/C++ Source-to-Source Tool based on Clang

gpuLik

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

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