high performance computing on graphics processing units: hgpu.org

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Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores

Gabin Schieffer, Ivy Peng

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Tags: Chemistry, Computer science, CUDA, Macromolecule, molecular docking, nVidia, nVidia A100, nVidia V100, Physics, Tesla T4

October 20, 2024 by hgpu

Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

Anwar Hossain Zahid, Ignacio Laguna, Wei Le

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Tags: AMD Radeon Instinct MI250, ATI, Computer science, CUDA, HIP, Numerical Analysis, nVidia, nVidia V100, Programming Languages

October 20, 2024 by hgpu

Data-driven Forecasting of Deep Learning Performance on GPUs

Seonho Lee, Amar Phanishayee, Divya Mahajan

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia P100, nVidia V100, Performance, PyTorch, Tesla T4

August 4, 2024 by hgpu

Data-driven Performance Optimization for Data-intensive Applications

Jie Liu

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Tags: Computer science, Databases, Deep learning, Neural networks, nVidia, nVidia V100, TensorFlow, Thesis

July 28, 2024 by hgpu

Optimal Kernel Orchestration for Tensor Programs with Korch

Muyan Hu, Ashwin Venkatram, Shreyashri Biswas, Balamurugan Marimuthu, Bohan Hou, Gabriele Oliaro, Haojie Wang, Liyan Zheng, Xupeng Miao, Jidong Zhai

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Tags: Compilers, Computer science, CUDA, Neural networks, nVidia, nVidia A100, nVidia V100, Package, PyTorch

June 23, 2024 by hgpu

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Johannes Pekkilä, Oskar Lappi, Fredrik Robertsén, Maarit J. Korpi-Lagg

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Energy-efficient computing, HIP, nVidia, nVidia A100, nVidia V100, Package, Performance, PyTorch, Stencil computation

June 16, 2024 by hgpu

Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs

Yixuan Mei, Yonghao Zhuang, Xupeng Miao, Juncheng Yang, Zhihao Jia, Rashmi Vinayak

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Tags: Computer science, GPU cluster, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, Tesla T4

June 9, 2024 by hgpu

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Zhaodong Chen

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia A100, nVidia V100, Thesis

May 26, 2024 by hgpu

Direct Numerical Simulation of Turbulence on Heterogenous Computer Systems: Architectures, Algorithms, and Applications

Martin Karp

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Tags: AMD Radeon Instinct MI250X, ATI, cfd, Fluid dynamics, FPGA, Heterogeneous systems, Numerical simulation, nVidia, nVidia A100, nVidia P100, nVidia V100, OpenCL, Spectral elements, Thesis

May 12, 2024 by hgpu

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Peter Thoman, Philip Salzmann

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Tags: Benchmarking, Computer science, GPU cluster, HPC, nVidia, nVidia V100, Package, SYCL

April 14, 2024 by hgpu

Speed, power and cost implications for GPU acceleration of Computational Fluid Dynamics on HPC systems

Zachary Cooper-Baldock, Brenda Vara Almirall, Kiao Inthavong

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Tags: cfd, Fluid dynamics, HPC, MPI, nVidia, nVidia A100, nVidia V100, Performance

April 7, 2024 by hgpu

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Xinyi Li, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, Ganesh Gopalakrishnan

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, Hardware Architecture, HPC, Matrix multiplication, nVidia, nVidia A100, nVidia H100, nVidia V100, PTX

March 10, 2024 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores

Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs

Data-driven Forecasting of Deep Learning Performance on GPUs

Data-driven Performance Optimization for Data-intensive Applications

Optimal Kernel Orchestration for Tensor Programs with Korch

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Direct Numerical Simulation of Turbulence on Heterogenous Computer Systems: Architectures, Algorithms, and Applications

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Speed, power and cost implications for GPU acceleration of Computational Fluid Dynamics on HPC systems

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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