OpenCL | Page 49 | hgpu.org

Programming

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

Li-Wen Chang

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, OpenCL, performance portability, Tesla C2050, Tesla K20, Thesis

October 15, 2017 by hgpu

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Ole Martin Skafsa

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Tags: CNN, Computer science, Deep learning, FPGA, Machine learning, Neural networks, NLP, nVidia, nVidia Tegra TX1, OpenCL, Thesis

October 3, 2017 by hgpu

Computing Treewidth on the GPU

Tom C. van der Zanden, Hans L. Bodlaender

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Source codes

Tags: Algorithms, Bloom filter, Computer science, Graph theory, nVidia, nVidia GeForce GTX 1060, OpenCL, Package

October 3, 2017 by hgpu

OpenCL Actors – Adding Data Parallelism to Actor-based Programming with CAF

Raphael Hiesgen, Dominik Charousset, Thomas C. Schmidt

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Tags: Computer science, Data parallelism, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 780 M, OpenCL, Package, Tesla C2075

September 28, 2017 by hgpu

Accelerating Radio Astronomy with Auto-Tuning

Alessio Sclocco

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Tags: AMD FirePro W9100, AMD Radeon R9 Fury X, Astrophysics, ATI, ATI Radeon HD 6970, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Tesla K20, Thesis

September 21, 2017 by hgpu

Out-of-core Implementation for Accelerator Kernels on Heterogeneous Clouds

Hamidreza Khaleghzadeh, Ziming Zhong, Ravi Reddy, Alexey Lastovetsky

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Tags: BLAS, Cloud, Computer science, CUBLAS, CUDA, FPGA, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Virtualization

September 16, 2017 by hgpu

End-to-end Deep Learning of Optimization Heuristics

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Compilers, Computer science, Deep learning, Keras, Machine learning, nVidia GeForce GTX 480, OpenCL, Optimization, Package, TensorFlow, Tesla K20, Theano

September 16, 2017 by hgpu

Analysis of GPU accelerated OpenCL applications on the Intel HD 4600 GPU

Arvid Johnsson

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Tags: Benchmarking, Computer science, Intel HD 4600, OpenCL, Sparse matrix, Thesis

September 9, 2017 by hgpu

Generating Custom Code for Efficient Query Execution on Heterogeneous Processors

Sebastian Bress, Bastian Kocher, Henning Funke, Tilmann Rabl, Volker Markl

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Tags: APU, Computer science, Databases, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K40

September 7, 2017 by hgpu

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

Yiqiu Wang, Anshumali Shrivastava, Junghee Ryu

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Tags: Algorithms, Computer science, Hashing, Machine learning, Nearest neighbour, nVidia, OpenCL, OpenMP, Tesla P100

September 7, 2017 by hgpu

Multi-Tasking Scheduling for Heterogeneous Systems

Yuan Wen

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Tags: ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 780, OpenCL, Task scheduling, Thesis

September 7, 2017 by hgpu

Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial

N.Alharbi, M. Chavent, RS. Laramee

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Tags: Computer science, Image generation, Intel HD 6100, Molecular dynamics, OpenCL, OpenGL, Package, Rendering, Tutorial

September 3, 2017 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Computing Treewidth on the GPU

OpenCL Actors – Adding Data Parallelism to Actor-based Programming with CAF

Accelerating Radio Astronomy with Auto-Tuning

End-to-end Deep Learning of Optimization Heuristics

Analysis of GPU accelerated OpenCL applications on the Intel HD 4600 GPU

Generating Custom Code for Efficient Query Execution on Heterogeneous Processors

FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search

Multi-Tasking Scheduling for Heterogeneous Systems

Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial

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)