high performance computing on graphics processing units: hgpu.org

hgpu.org » Deep learning

A Deep Learning Based Cost Model for Automatic Code Optimization

Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, Saman Amarasinghe

View

Download (PDF)

Tags: Compilers, Computer science, Deep learning, Optimization, Performance

March 21, 2021 by hgpu

LazyTensor: combining eager execution with domain-specific compilers

Alex Suhan, Davide Libenzi, Ailing Zhang, Parker Schuh, Brennan Saeta, Jie Young Sohn, Denys Shabalin

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Package, Programming Languages, TPU

March 7, 2021 by hgpu

Benchmarking Modern Edge Devices for AI Applications

Pilsung Kang, Jongmin Jo

View

Download (PDF)

Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, TPU

March 7, 2021 by hgpu

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Akio Hayakawa, Masato Ishii, Yoshiyuki Kobayashi, Akira Nakamura, Takuya Narihira, Yukio Obuchi, Andrew Shin, Takuya Yashima, Kazuki Yoshiyama

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, Package, Tesla V100

March 7, 2021 by hgpu

INSTA-YOLO: Real-Time Instance Segmentation

Eslam Mohamed, Abdelrahman Shaker, Hazem Rashed, Ahmad El-Sallab, Mayada Hadhoud

View

Download (PDF)

Tags: Deep learning, Image processing, nVidia, nVidia GeForce GTX 1080

February 21, 2021 by hgpu

Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models

Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Nathaniel Cresswell-Clay

View

Download (PDF)

Tags: Deep learning, Earth and Space Sciences, Neural networks, nVidia, Tesla V100, Weather prediction, Weather Prediction Model

February 14, 2021 by hgpu

Transparent FPGA Acceleration with TensorFlow

Simon Pfenning, Philipp Holzinger, Marc Reichenbach

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, FPGA, Hardware Architecture, HSA, OpenCL, Package, TensorFlow

February 14, 2021 by hgpu

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2070, Performance, Python, Tesla P100, Tesla V100

February 7, 2021 by hgpu

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, NLP, nVidia, nVidia A100, Package, Python, Tesla P100

February 7, 2021 by hgpu

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

Walther Carballo-Hernández, Maxime Pelcat, François Berry

View

Download (PDF)

Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia Jetson TX2, Performance

February 7, 2021 by hgpu

Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents

Uttaran Bhattacharya, Nicholas Rewkowski, Abhishek Banerjee, Pooja Guhan, Aniket Bera, Dinesh Manocha

View

Download (PDF)

Source codes

Tags: Biomechanics, Computer science, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Virtual reality

January 31, 2021 by hgpu

Non-Parametric Adaptive Network Pruning

Lin Mingbao, Ji Rongrong, Li Shaojie, Wang Yan, Wu Yongjian, Huang Feiyue, Ye Qixiang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, nVidia, Package, PyTorch, Tesla V100

January 24, 2021 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

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

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A Deep Learning Based Cost Model for Automatic Code Optimization

LazyTensor: combining eager execution with domain-specific compilers

Benchmarking Modern Edge Devices for AI Applications

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

INSTA-YOLO: Real-Time Instance Segmentation

Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models

Transparent FPGA Acceleration with TensorFlow

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents

Non-Parametric Adaptive Network Pruning

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Most viewed papers (last 30 days)