high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand

View

Download (PDF)

Source codes

Tags: 3D Graphics and Realism, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 1080 Ti, Package, Rendering, Tesla V100

October 6, 2019 by hgpu

waLBerla: A block-structured high-performance framework for multiphysics simulations

Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde

View

Download (PDF)

Source codes

Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100

October 6, 2019 by hgpu

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Maria Papadaki, Yannis Sfakianakis, Christos Kozanitis, Angelos Bilas

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, Grid, nVidia, nVidia Quadro K 2200

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

View

Download (PDF)

Source codes

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

September 29, 2019 by hgpu

Futhark Vulkan Backend

Steffen Holst Larsen

View

Download (PDF)

Source codes

Tags: Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, Vulkan

September 29, 2019 by hgpu

Elastic deep learning in multi-tenant GPU cluster

Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, James Cheng

View

Download (PDF)

Tags: Computer science, Deep learning, Distributed computing, GPU cluster, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

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

View

Download (PDF)

Source codes

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

September 22, 2019 by hgpu

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu

View

Download (PDF)

Tags: AI, AMD Radeon R7, ATI, Computer science, Deep learning, Neural networks, nVidia, PyTorch, TensorFlow, Tesla P100, Tesla V100, TPU

September 22, 2019 by hgpu

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Abhinav Jangda, Arjun Guha

View

Download (PDF)

Tags: CUDA, Image procesing, nVidia, nVidia GeForce GTX 1080 Ti, Python, Tesla V100

September 22, 2019 by hgpu

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Dejice Jacob, Jeremy Singer

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 1060, Python

September 22, 2019 by hgpu

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Speech recognition

September 22, 2019 by hgpu

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

Natalija Stojanovic, Dragan Stojanovic

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, OpenACC, Package, Tesla K80

September 15, 2019 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

waLBerla: A block-structured high-performance framework for multiphysics simulations

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Exascale Deep Learning for Scientific Inverse Problems

Futhark Vulkan Backend

Elastic deep learning in multi-tenant GPU cluster

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

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

Recent source codes

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

Most viewed papers (last 30 days)