high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Query Processing on Tensor Computation Runtimes

Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi

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Tags: Benchmarking, Cloud, Computer science, CUDA, Databases, Machine learning, nVidia, Package, Tesla P100

March 6, 2022 by hgpu

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Shenggan Cheng, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng, Yang You

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Tags: Biology, Computer science, CUDA, Machine learning, nVidia, nVidia A100, Package

March 6, 2022 by hgpu

Enabling On-Device Smartphone GPU based Training: Lessons Learned

Anish Das, Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo

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Tags: Computer science, Deep learning, Neural networks, OpenCL

March 6, 2022 by hgpu

gLBM: A GPU enabled Lattice Boltzmann Method Library

Aaron Bray, Rachel B. Clipp, M. Umar Qureshi, Sorin Mitran, Andinet Enquobahrie

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Tags: cfd, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, Package

February 20, 2022 by hgpu

A ML-based resource utilization OpenCL GPU-kernel fusion model

Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava

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Tags: Computer science, Data parallelism, HPC, Machine learning, nVidia, nVidia GeForce GT 740 M, nVidia GeForce GTX 760, OpenCL

February 20, 2022 by hgpu

Lightning: Scaling the GPU Programming Model Beyond a Single GPU

Stijn Heldens, Pieter Hijma, Ben van Werkhoven, Jason Maassen, Rob V. van Nieuwpoort

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Tags: Computer science, CUDA, Distributed computing, nVidia, OpenCL, Package, Performance, Tesla P100

February 20, 2022 by hgpu

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

Guohao Dai, Guyue Huang, Shang Yang, Zhongming Yu, Hengrui Zhang, Yufei Ding, Yuan Xie, Huazhong Yang, Yu Wang

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Tags: Algorithms, CUDA, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

February 20, 2022 by hgpu

A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices

Qiyang Zhang, Xiang Li, Xiangying Che, Xiao Ma, Ao Zhou, Mengwei Xu, Shangguang Wang, Yun Ma, Xuanzhe Liu

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Tags: ARM, Benchmarking, Computer science, Deep learning, OpenCL, OpenGL, Vulkan

February 20, 2022 by hgpu

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, OpenCL, Tesla K20, Thesis

February 13, 2022 by hgpu

The Ecological Footprint of Neural Machine Translation Systems

Dimitar Sherionov, Eva Vanmassenhove

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

February 13, 2022 by hgpu

Flashlight: Enabling Innovation in Tools for Machine Learning

Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Tesla V100

February 6, 2022 by hgpu

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

Wenzel Jakob, Sébastien Speierer, Nicolas Roussel, Delio Vicini

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Tags: 3D Graphics and Realism, Algorithms, Code generation, CUDA, nVidia, nVidia RTX A6000, Python, Rendering

February 6, 2022 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

high performance computing on graphics processing units: hgpu.org

Programming

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Enabling On-Device Smartphone GPU based Training: Lessons Learned

gLBM: A GPU enabled Lattice Boltzmann Method Library

A ML-based resource utilization OpenCL GPU-kernel fusion model

Lightning: Scaling the GPU Programming Model Beyond a Single GPU

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

The Ecological Footprint of Neural Machine Translation Systems

Flashlight: Enabling Innovation in Tools for Machine Learning

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

Recent source codes

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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