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Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms

Konrad Moren, Diana Gohringer

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Tags: AMD Radeon R9 290, ATI, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 780 Ti, OpenCL

October 28, 2018 by hgpu

The Ocean Tensor Package

Ewout van den Berg

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Package

October 28, 2018 by hgpu

Towards Efficient Large-Scale Graph Neural Network Computing

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai

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Tags: Computer science, CUDA, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, Tesla P100

October 28, 2018 by hgpu

Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering

Ricardo Nobre, Luis Reis, Joao M. P. Cardoso

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Tags: Computer science, nVidia, nVidia GeForce GTX 1070, OpenCL, Performance, PTX

October 28, 2018 by hgpu

Using Compiler Snippets to Exploit Parallelism on Heterogeneous Hardware: A Java Reduction Case Study

Juan Fumero, Christos Kotselidis

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Tags: Code generation, Computer science, Heterogeneous systems, Java, nVidia, nVidia Quadro GP100, OpenCL, Package

October 21, 2018 by hgpu

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

Sparsh Mittal

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL, survey

October 21, 2018 by hgpu

Exploiting Task Parallelism with OpenCL: A Case Study

Pekka Jaaskelainen, Ville Korhonen, Matias Koskela, Jarmo Takala, Karen Egiazarian, Aram Danielyan, Cristovao Cruz, James Price, Simon McIntosh-Smith

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Tags: Computer science, OpenCL, Package, Task scheduling

October 21, 2018 by hgpu

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc Van Gool

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Tags: Android, Artificial intelligence, Benchmarking, Computer science, Deep learning, Neural networks

October 21, 2018 by hgpu

Resource Elastic Virtualization for FPGAs using OpenCL

Anuj Vaishnav, Khoa Dang Pham, Dirk Koch, James Garside

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Tags: Computer science, FPGA, OpenCL, Virtualization

October 13, 2018 by hgpu

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 2018 by hgpu

Sparse Winograd Convolutional neural networks on small-scale systolic arrays

Feng Shi, Haochen Li, Yuhe Gao, Benjamin Kuschner, Song-Chun Zhu

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Tags: Analytical model, Computer science, Deep learning, FPGA, Neural networks

October 13, 2018 by hgpu

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Pierre Huchant, Denis Barthou, Marie-Christine Counilh

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Tags: Computer science, Heterogeneous systems, load balancing, nVidia, OpenCL, Tesla M2075

October 13, 2018 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

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

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

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

Applications

Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms

The Ocean Tensor Package

Towards Efficient Large-Scale Graph Neural Network Computing

Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering

Using Compiler Snippets to Exploit Parallelism on Heterogeneous Hardware: A Java Reduction Case Study

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

Exploiting Task Parallelism with OpenCL: A Case Study

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Resource Elastic Virtualization for FPGAs using OpenCL

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Sparse Winograd Convolutional neural networks on small-scale systolic arrays

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

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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