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Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

Nadjib Mammeri, Markus Neu, Sohan Lal, Ben Juurlink

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Tags: Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, Neural networks, OpenCL, OpenGL, SoC

February 23, 2020 by hgpu

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

Donatella Granata, Angelo Palombo, Federico Santini, Umberto Amato

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Tags: Algorithms, Heterogeneous systems, nVidia, nVidia GeForce GTX 1650, OpenCL, Sensing, Signal processing

February 2, 2020 by hgpu

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Lukas Sommer, Florian Stock, Leonardo Solis-Vasquez, Andreas Koch

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Tags: Automotive, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, OpenCL, OpenMP

January 26, 2020 by hgpu

Automatically Harnessing Sparse Acceleration

Philip Ginsbach, Bruce Collie, Michael F.P. O'Boyle

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Tags: AMD Radeon R7, ATI, Computer science, CUDA, Fortran, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance

January 26, 2020 by hgpu

Towards High Performance Java-based Deep Learning Frameworks

Athanasios Stratikopoulos, Juan Fumero, Zoran Sevarac, Christos Kotselidis

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Tags: Computer science, Computer vision, CUDA, Data mining, Deep learning, Heterogeneous systems, Java, Machine learning, nVidia, OpenCL, Package

January 19, 2020 by hgpu

Static Analysis and Dynamic Adaptation of Parallelism

Pierre Huchant

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla K20, Tesla M2075, Thesis

January 12, 2020 by hgpu

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Akrem Benatia, Weixing Ji, Yizhuo Wang, Feng Shi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Sparse matrix

January 5, 2020 by hgpu

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Tim Besard

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Tags: Computer science, CUDA, Heterogeneous systems, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Thesis

December 29, 2019 by hgpu

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

Leonardo Solis Vasquez

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, FPGA, Heterogeneous systems, Macromolecule, nVidia, OpenCL, Package, Physics, Tesla V100, Thesis

December 29, 2019 by hgpu

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Robert Dietrich

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Tags: Computer science, CUDA, Data acquisition, Heterogeneous systems, MPI, nVidia, OpenACC, OpenCL, Tesla K80, Tesla V100, Thesis

December 1, 2019 by hgpu

Compiler-Driven Performance on Heterogeneous Computing Platforms

Artem Chikin

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, Performance, Tesla P100, Tesla V100, Thesis

November 17, 2019 by hgpu

Accelerating Stochastic Simulations on GPUs Using OpenCL

Pilsung Kang

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Tags: Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Physics, Stochastic simulation

November 10, 2019 by hgpu

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

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Automatically Harnessing Sparse Acceleration

Towards High Performance Java-based Deep Learning Frameworks

Static Analysis and Dynamic Adaptation of Parallelism

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Compiler-Driven Performance on Heterogeneous Computing Platforms

Accelerating Stochastic Simulations on GPUs Using OpenCL

Recent source codes

XaaS containers

microSYCL: SYCL micro-benchmarks repository

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

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

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

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