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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

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

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

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

Live Migration for OpenCL FPGA Accelerators

Anuj Vaishnav, Khoa Dang Pham, Dirk Koch

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Tags: Computer science, FPGA, OpenCL

October 6, 2018 by hgpu

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

David W. Brown

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 750 Ti, Package

October 6, 2018 by hgpu

HSTREAM: A directive-based language extension for heterogeneous stream computing

Suejb Memeti, Sabri Pllana

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro P 4000, OpenMP

October 6, 2018 by hgpu

On Reinforcement Learning for Full-length Game of StarCraft

Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu

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Tags: AI, Algorithms, Artificial intelligence, Computer science, Deep learning, Games, Machine learning, nVidia, Tesla K40

October 6, 2018 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Applications

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

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

Resource Elastic Virtualization for FPGAs using OpenCL

Sparse Winograd Convolutional neural networks on small-scale systolic arrays

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Live Migration for OpenCL FPGA Accelerators

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

HSTREAM: A directive-based language extension for heterogeneous stream computing

On Reinforcement Learning for Full-length Game of StarCraft

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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