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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

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

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

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

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjorn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen

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Tags: CUDA, Deep learning, Keras, Medicine, Neural networks, nVidia, Package, TensorFlow

September 16, 2018 by hgpu

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Khoa Dang Pham, Anuj Vaishnav, Malte Vesper, Dirk Koch

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Tags: Computer science, FPGA, HLS, OpenCL, Package, Task scheduling

September 16, 2018 by hgpu

Efficient and Scalable k-Means on GPUs

Clemens Lutz, Sebastian Bress, Tilmann Rabl, Steffen Zeuch, Volker Markl

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Tags: Algorithms, Clustering, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

September 9, 2018 by hgpu

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Edward Choi

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Tags: CUDA, Deep learning, Medicine, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Tesla K80, Theano, Thesis

September 9, 2018 by hgpu

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Chemical Physics, Code generation, Computational Physics, Factorization, nVidia, OpenCL, Package, Physics, Tesla C2075, Tesla K40

September 9, 2018 by hgpu

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Tesla V100

September 2, 2018 by hgpu

A study of integer sorting on multicores

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, MPI, Package, Performance, Sorting

September 2, 2018 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

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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

Exploiting Task Parallelism with OpenCL: A Case Study

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

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

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Efficient and Scalable k-Means on GPUs

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

A study of integer sorting on multicores

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)