high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Real-time colouring and filtering with graphics shaders

Dany Vohl, Christopher J. Fluke, David G. Barnes, Amr H. Hassan

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Tags: Algorithms, Astrophysics, Filtering, GLSL, Image processing, nVidia, nVidia GeForce GT 750 M, nVidia GeForce GTX Titan X, nVidia Grid K1, Package, Rendering

July 5, 2017 by hgpu

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke

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Tags: Algorithms, Computer science, CUDA, Genomics, Linear Algebra, Matrix multiplication, nVidia, Performance, Tesla K80

July 5, 2017 by hgpu

Parle: parallelizing stochastic gradient descent

Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar

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Tags: Algorithms, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Performance, Torch

July 5, 2017 by hgpu

GPU-acceleration for Large-scale Tree Boosting

Huan Zhang, Si Si, Cho-Jui Hsieh

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Tags: Algorithms, AMD Radeon RX 480, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

July 2, 2017 by hgpu

Snowflake: A Lightweight Portable Stencil DSL

Nathan Zhang, Michael Driscoll, Armando Fox, Charles Markley, Samuel Williams, Protonu Basu

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Tags: Code generation, Computer science, DSL, nVidia, OpenCL, Package, Python, Stencil computation, Tesla K20

July 2, 2017 by hgpu

Speeding up lattice sieve with Xeon Phi coprocessor

Anja Becker, Dusan Kostic

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Tags: Algorithms, Computer science, Intel Xeon Phi, Security

July 2, 2017 by hgpu

Synthesis of Embedded Software using Dataflow Schedule Graphs

Abhay Raina

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Tags: Computer science, DSP, nVidia, nVidia GeForce GTX 680, OpenCL, Signal processing, Thesis

July 2, 2017 by hgpu

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

K. Mills, I. Tamblyn

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Tags: Condensed matter, CUDA, Deep learning, Ising model, Materials Science, Neural networks, nVidia, Phase transition, Physics, Tesla K40

July 2, 2017 by hgpu

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

Loc N. Huynh, Youngki Lee, Rajesh Krishna Balan

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Tags: Algorithms, Computer science, Deep learning, OpenCL, Package, Vulkan

June 25, 2017 by hgpu

ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android

Andreas Palsson

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Tags: Algorithms, Android, Image processing, Java, OpenCL, RenderScript, Thesis

June 25, 2017 by hgpu

Scalar collapse in AdS with an OpenCL open source code

Steven L. Liebling, Gaurav Khanna

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Tags: AMD Radeon R9 295X2, ATI, General Relativity and Quantum Cosmology, Heterogeneous systems, High Energy Physics - Theory, nVidia, OpenCL, Package, Physics, Tesla K40

June 25, 2017 by hgpu

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Yuechao Lu, Fumihiko Ino, Yasuyuki Matsushita

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Tags: Algorithms, Computer science, Computer vision, CUDA, Linear Algebra, Machine learning, nVidia, Tesla P100

June 25, 2017 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

Can Large Language Models Predict Parallel Code Performance?

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Real-time colouring and filtering with graphics shaders

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Parle: parallelizing stochastic gradient descent

GPU-acceleration for Large-scale Tree Boosting

Snowflake: A Lightweight Portable Stencil DSL

Speeding up lattice sieve with Xeon Phi coprocessor

Synthesis of Embedded Software using Dataflow Schedule Graphs

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android

Scalar collapse in AdS with an OpenCL open source code

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Recent source codes

CFAL-bench

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

Can Large Language Models Predict Parallel Code Performance?

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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