high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

Charalampos Stylianopoulos, Linus Johansson, Oskar Olsson, Magnus Almgren

View

Download (PDF)

Source codes

Tags: ARM, Computer science, IoT, OpenCL, Package, Security

November 25, 2018 by hgpu

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Linnan Wang, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca

View

Download (PDF)

Source codes

Tags: Compression, Computer science, CUDA, Deep learning, FFT, GPU cluster, Neural networks, nVidia, Package, Tesla P100

November 25, 2018 by hgpu

Modeling Deep Learning Accelerator Enabled GPUs

Md Aamir Raihan, Negar Goli, Tor Aamodt

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Hardware Architecture, Machine learning, nVidia, Tesla V100

November 25, 2018 by hgpu

Dense and sparse parallel linear algebra algorithms on graphics processing units

Alejandro Lamas Davina

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Linear Algebra, MPI, nVidia, Sparse, Tesla K20, Tesla M2090, Thesis

November 25, 2018 by hgpu

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Chao Wu, Yaoxue Zhang, Yuezhi Zhou, Qiushi Li

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance

November 18, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

Guoping Long, Jun Yang, Kai Zhu, Wei Lin

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, nVidia, TensorFlow

November 18, 2018 by hgpu

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

Denis Demidov

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Differential equations, Mathematical Software, multigrid, nVidia, OpenCL, OpenMP, Package, Partial differential equations, PDEs, Tesla K80

November 18, 2018 by hgpu

Hashing, Caching, and Synchronization: Memory Techniques for Latency Masking Multithreaded Applications

Skyler Arron Windh

View

Download (PDF)

Tags: Computer science, FPGA, Hashing, HLS, OpenCL, Thesis

November 11, 2018 by hgpu

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

Jens Domke, Kazuaki Matsumura, Mohamed Wahib, Haoyu Zhang, Keita Yashima, Toshiki Tsuchikawa, Yohei Tsuji, Artur Podobas, Satoshi Matsuoka

View

Download (PDF)

Source codes

Tags: Computer science, HPC, Intel Xeon Phi, Package

November 11, 2018 by hgpu

The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs

Ahmad Al Badawi, Jin Chao, Jie Lin, Chan Fook Mun, Sim Jun Jie, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, Vijay Ramaseshan Chandrasekhar

View

Download (PDF)

Tags: CNN, Computer science, CUDA, Deep learning, nVidia, Security, Tesla V100

November 11, 2018 by hgpu

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

View

Download (PDF)

Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Modeling Deep Learning Accelerator Enabled GPUs

Dense and sparse parallel linear algebra algorithms on graphics processing units

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

Hashing, Caching, and Synchronization: Memory Techniques for Latency Masking Multithreaded Applications

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)