high performance computing on graphics processing units: hgpu.org

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cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

Yu-hsuan Shih, Garrett Wright, Joakim Andén, Johannes Blaschke, Alex H. Barnett

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Tags: CUDA, FFT, Image reconstruction, Mathematical Software, Numerical Analysis, nVidia, Package, Signal processing, Tesla V100

February 21, 2021 by hgpu

Serverless Computing Strategies on Cloud Platforms

Diana María Naranjo Delgado

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Tags: Cloud, Computer science, CUDA, Machine learning, nVidia, Tesla V100, Thesis, Virtualization

February 14, 2021 by hgpu

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Tomasz Boiński, Paweł Czarnul

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Tesla K20

February 14, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 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

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

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

Walther Carballo-Hernández, Maxime Pelcat, François Berry

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

February 7, 2021 by hgpu

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Guei-Yuan Lueh, Kaiyu Chen, Gang Chen, Joel Fuentes, Wei-Yu Chen, Fangwen Fu, Hong Jiang, Hongzheng Li, Daniel Rhee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages

January 31, 2021 by hgpu

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Mike Turner, Jamil Appa, Neil Ashton

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, SYCL, Tesla A100, Tesla T4, Tesla V100

January 31, 2021 by hgpu

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

Aamir Shafi, Jahanzeb Maqbool Hashmi, Hari Subramoni, Dhabaleswar K. (DK) Panda

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Tags: Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Performance, Python, Tesla V100

January 31, 2021 by hgpu

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Weicheng Xue

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Tags: cfd, CUDA, Finite difference, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenACC, Tesla C2075, Tesla P100, Tesla V100, Thesis

January 31, 2021 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

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

Optimization of Data Assignment for Parallel Processing in a Hybrid Heterogeneous Environment Using Integer Linear Programming

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

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

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?

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Recent source codes

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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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