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A Model Extraction Attack on Deep Neural Networks Running on GPUs

Jonah G. O'Brien Weiss

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Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 8000, Security, Tesla T4

August 13, 2023 by hgpu

Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

Shixun Wu, Yujia Zhai, Jinyang Liu, Jiajun Huang, Zizhe Jian, Bryan M. Wong, Zizhong Chen

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Tags: Code generation, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, nVidia A100, Package, Performance, Reliability, Tesla T4

May 7, 2023 by hgpu

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang

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Tags: Computer science, CUDA, Deep learning, nVidia, Optimization, Package, Tesla K80, Tesla T4

February 12, 2023 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

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, Tesla T4

October 31, 2021 by hgpu

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Benchmarking, Computer science, CUDA, Databases, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 980, Tesla T4

April 5, 2021 by hgpu

DISC: A Dynamic Shape Compiler for Machine Learning Workloads

Kai Zhu, Wenyi Zhao, Zhen Zheng, Tianyou Guo, Pengzhan Zhao, Junjie Bai, Jun Yang, Xiaoyong Liu, Lansong Diao, Wei Lin

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Tags: Compilers, Comuter science, CUDA, Machine learning, nVidia, Tesla T4

March 14, 2021 by hgpu

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Mike Turner, Jamil Appa, Neil Ashton

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, SYCL, Tesla A100, Tesla T4, Tesla V100

January 31, 2021 by hgpu

TopicBERT for Energy Efficient Document Classification

Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze

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Tags: classification, Computer science, CUDA, Deep learning, NLP, nVidia, Tesla T4

November 8, 2020 by hgpu

A Comparison of Optimal Scanline Voxelization Algorithms

Tim Håkansson

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Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, OpenGL, Package, Tesla T4, Thesis, Voxelization

September 20, 2020 by hgpu

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Zijing Gu

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, Package, Sparse matrix, Tesla T4

August 2, 2020 by hgpu

Beehive SPIR-V Toolkit

Beehive SPIR-V Toolkit: A Composable and Functional API for Runtime SPIR-V Code Generation

CATO: automatic source transformation to apply HPC frameworks with minimal user interaction

Compiler-assisted distribution of OpenMP code for improved scalability

ADIOS2.jl: a Julia interface to ADIOS2

Julia as a unifying end-to-end workflow language on the Frontier exascale system

Code generated HPC Kernels Using Llama-2-70b

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

Scope is all you need: Transforming LLMs for HPC Code

pocl - Portable Computing Language

PoCL-R: An Open Standard Based Offloading Layer for Heterogeneous Multi-Access Edge Computing with Server Side Scalability

HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU

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high performance computing on graphics processing units: hgpu.org

A Model Extraction Attack on Deep Neural Networks Running on GPUs

Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Real-Time High-Performance Computing for Embedded Control Systems

BAT: A Benchmark suite for AutoTuners

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

DISC: A Dynamic Shape Compiler for Machine Learning Workloads

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

TopicBERT for Energy Efficient Document Classification

A Comparison of Optimal Scanline Voxelization Algorithms

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Recent source codes

Beehive SPIR-V Toolkit

CATO: automatic source transformation to apply HPC frameworks with minimal user interaction

ADIOS2.jl: a Julia interface to ADIOS2

Code generated HPC Kernels Using Llama-2-70b

Scope is all you need: Transforming LLMs for HPC Code

pocl - Portable Computing Language

HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU

openCLDispatcher

CEED Benchmarks

Sieve: Stratified GPU-Compute Workload Sampling

Most viewed papers (last 30 days)