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Bridging OpenCL and CUDA: A Comparative Analysis and Translation

Junghyun Kim, Thanh Tuan Dao, Jaehoon Jung, Jinyoung Joo, Jaejin Lee

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Tags: ATI, ATI Radeon HD 7970, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL

December 1, 2015 by hgpu

Neural GPUs Learn Algorithms

Lukasz Kaiser, Ilya Sutskever

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40

December 1, 2015 by hgpu

Reordering GPU Kernel Launches to Enable Efficient Concurrent Execution

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580

November 29, 2015 by hgpu

Design, Implementation and Performance Evaluation of a Stochastic Gradient Descent Algorithm on CUDA

Emanuele De Falco

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GT 540 M

November 29, 2015 by hgpu

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Philipp Kainz, Michael Pfeiffer, Martin Urschler

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Tags: Algorithms, CUDA, Deep learning, Image processing, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black

November 29, 2015 by hgpu

Acceleration of Agent-Based Pandemic Modeling on Multiple GPUs

Barzan Shekh

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

November 25, 2015 by hgpu

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Tesla C2070, Virtualization

November 25, 2015 by hgpu

Pulsar Acceleration Searches on the GPU for the Square Kilometre Array

Sofia Dimoudi, Wesley Armour

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX Titan X, Tesla K80

November 25, 2015 by hgpu

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Liang-Chieh Chen

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 650 M, OpenCV, Tesla K40, Thesis

November 24, 2015 by hgpu

Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, Dongjun Shin

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Tags: ARM, Bayesian, Caffe, CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

November 24, 2015 by hgpu

Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning

Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah

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Tags: Caffe, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X

November 24, 2015 by hgpu

Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures

E. Phipps, M. D'Elia, H. C. Edwards, M. Hoemmen, J. Hu, S. Rajamanickam

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Tags: Computer science, CUDA, Differential equations, nVidia, OpenMP, Partial differential equations, PDEs, Tesla K20

November 24, 2015 by hgpu

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Bridging OpenCL and CUDA: A Comparative Analysis and Translation

Neural GPUs Learn Algorithms

Reordering GPU Kernel Launches to Enable Efficient Concurrent Execution

Design, Implementation and Performance Evaluation of a Stochastic Gradient Descent Algorithm on CUDA

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Acceleration of Agent-Based Pandemic Modeling on Multiple GPUs

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

Pulsar Acceleration Searches on the GPU for the Square Kilometre Array

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning

Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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