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Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Nico Reissmann

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Tags: Computer science, CUDA, nVidia, OpenMP, Package, Performance, performance portability, Tesla K20, Thesis

May 5, 2019 by hgpu

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Sangkyun Lee, Jeonghyun Lee

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Tags: Algorithms, ARM, Compression, Computer science, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Package

May 5, 2019 by hgpu

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

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Tags: Algorithms, Computer science, Machine learning, Neural networks, nVidia, Package, TensorFlow, Tesla V100

May 5, 2019 by hgpu

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Heinrich Riebler, Gavin Vaz, Tobias Kenter, Christian Plessl

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Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

May 1, 2019 by hgpu

The Risks of WebGL: Analysis, Evaluation and Detection

Alex Belkin, Nethanel Gelernter, Israel Cidon

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Tags: Computer science, nVidia, nVidia GeForce GTX 1080, Security, WebGL

May 1, 2019 by hgpu

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

James D. Stevens

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Tags: AMD Radeon R9 Fury, ATI, Code generation, Computer science, Finite difference, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 28, 2019 by hgpu

Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets

Jingpeng Wu, William M. Silversmith, H. Sebastian Seung

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Tags: Cloud, CNN, Computer science, CUDA, Deep learning, Electron microscopy, Image processing, Microscopy, nVidia, nVidia GeForce GTX 970, Package, Tesla K80

April 28, 2019 by hgpu

Wasserstein-Fisher-Rao Document Distance

Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao

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Tags: Computer science, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 1080 Ti

April 28, 2019 by hgpu

GPU-based Efficient Join Algorithms on Hadoop

Hongzhi Wang, Ning Li, Zheng Wang, Jianing Li

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Tags: Algorithms, Computer science, CUDA, Databases, Hadoop, nVidia

April 28, 2019 by hgpu

On Optimizing Complex Stencils on GPUs

Prashant Singh Rawat, Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, Louis-Noël Pouchet, P. Sadayappan

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Tags: Code generation, Computer science, CUDA, nVidia, Stencil computation, Tesla P100

April 20, 2019 by hgpu

Real world applications of Artificial Intelligence on constrained hardware

Wiebe Van Ranst

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Tags: Artificial intelligence, Computer science, Computer vision, Image processing, nVidia, nVidia Jetson TX2, OpenCL, Thesis

April 20, 2019 by hgpu

Concurrent query processing in a GPU-based database system

Hao Li, Yi-Cheng Tu, Bo Zeng

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX Titan X, Package

April 20, 2019 by hgpu

Can Large Language Models Predict Parallel Code Performance?

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

The Risks of WebGL: Analysis, Evaluation and Detection

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

Wasserstein-Fisher-Rao Document Distance

GPU-based Efficient Join Algorithms on Hadoop

On Optimizing Complex Stencils on GPUs

Real world applications of Artificial Intelligence on constrained hardware

Concurrent query processing in a GPU-based database system

Recent source codes

Can Large Language Models Predict Parallel Code Performance?

CFAL-bench

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

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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