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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut

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

September 29, 2019 by hgpu

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia DGX-2, Package, Tesla V100

September 22, 2019 by hgpu

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, Eduard Hovy

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package

September 8, 2019 by hgpu

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

Hui Chen, Zijia Lin, Guiguang Ding, Jianguang Lou, Yusen Zhang, Borje Karlsson

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Tags: Computer science, Neural networks, NLP, nVidia, Tesla P100

July 21, 2019 by hgpu

Semantic Product Search

Priyanka Nigam, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian (Allen)Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, Bing Yin

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Tags: Computer science, Deep learning, Hashing, NLP, nVidia, Tesla V100

July 4, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

Yonghae Kim, Hyesoon Kim

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Tags: Code generation, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, OpenCL

May 23, 2019 by hgpu

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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Tags: Computer science, CUDA, NLP, nVidia, Package, PyTorch, Video

April 7, 2019 by hgpu

Evaluating the Efficiency of CPUs, GPUs and FPGAs on a Near-Duplicate Document Detection Via OpenCL

Ercan Canhasi

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Tags: Computer science, FPGA, Machine learning, NLP, OpenCL

July 5, 2018 by hgpu

Neural Code Comprehension: A Learnable Representation of Code Semantics

Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, CUDA, Deep learning, LLVM, LSTM, Machine learning, NLP, nVidia, nVidia GeForce GTX 970, OpenCL, Programming Languages, RNN

June 20, 2018 by hgpu

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang

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Tags: CNN, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

June 17, 2018 by hgpu

Assessment of various GPU acceleration strategies in text categorization processing flow

Lukasz Kordula, Maciej Wielgosz, Michal Karwatowski, Marcin Pietron, Dominik Zurek, Kazimierz Wiatr

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Tags: AMD Radeon R9 280, ATI, Computer science, NLP, nVidia, nVidia Quadro NVS 4200 M, OpenCL

June 13, 2018 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

Semantic Product Search

A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Evaluating the Efficiency of CPUs, GPUs and FPGAs on a Near-Duplicate Document Detection Via OpenCL

Neural Code Comprehension: A Learnable Representation of Code Semantics

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Assessment of various GPU acceleration strategies in text categorization processing flow

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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