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Accelerating Deep Neural Networks implementation: A survey

Meriam Dhouibi, Ahmed Karim Ben Salem, Afef Saidi, Slim Ben Saoud

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Tags: ASIC, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL, Optimization, Performance, survey

March 28, 2021 by hgpu

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline

Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steve Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alex Ballow, Alina Lazar

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Tags: Algorithms, CUDA, Deep learning, Exa.TrkX, HEP, Neural networks, nVidia, Package, Physics, Tesla A100, Tesla V100

March 21, 2021 by hgpu

AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries

Jason Kennedy, Blesson Varghese, Carlos Reaño

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Tags: Cloud, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TX2, Virtualization

March 21, 2021 by hgpu

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

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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

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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

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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

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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

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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

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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

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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

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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

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

February 7, 2021 by hgpu

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

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