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EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Manuel de Castro, Inmaculada Santamaria-Valenzuela, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: AMD FirePro W9100, ATI, Computer science, Heterogeneous systems, Numerical simulation, nVidia, OpenCL, Package, Stencil computation, Tesla V100

February 12, 2023 by hgpu

Persistent Kernels for Iterative Memory-bound GPU Applications

Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng, Xiao Wang, Satoshi Matsuoka

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Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla A100, Tesla V100

April 10, 2022 by hgpu

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Dominik Ernst, Georg Hager, Markus Holzer, Matthias Knorr, Gerhard Wellein

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Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, Performance, Stencil computation, Tesla V100

July 11, 2021 by hgpu

Efficient Nearest-Neighbor Data Sharing in GPUs

Negin Nematollahi, Mohammad Sadrosadati, Hajar Falahati, Marzieh Barkhordar, Mario Paulo Drumond, Hamid Sarbazi-Azad, Babak Falsafi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Stencil computation

January 10, 2021 by hgpu

Accelerating High-Order Stencils on GPUs

Ryuichi Sai, John Mellor-Crummey, Xiaozhu Meng, Mauricio Araya-Polo, Jie Meng

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Tags: Computer science, HPC, nVidia, nVidia Quadro NVS 510, Package, Performance, Stencil computation, Tesla P100, Tesla V100

September 13, 2020 by hgpu

High-Performance High-Order Stencil Computation on FPGAs Using OpenCL

Hamid Reza Zohouri, Artur Podobas, Satoshi Matsuoka

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Tags: Computer science, FPGA, OpenCL, Stencil computation

February 23, 2020 by hgpu

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka

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Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla P100, Tesla V100

July 21, 2019 by hgpu

On Optimizing Complex Stencils on GPUs

Prashant Singh Rawat, Miheer Vaidya, Aravind Sukumaran-Rajam, Atanas Rountev, Louis-Noël Pouchet, P. Sadayappan

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Tags: Code generation, Computer science, CUDA, nVidia, Stencil computation, Tesla P100

April 20, 2019 by hgpu

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

Tuowen Zhao, Samuel Williams, Mary Hall, Hans Johansen

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, performance portability, Stencil computation, Tesla P100

December 16, 2018 by hgpu

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman

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Tags: AMD Radeon R9 390, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation

April 28, 2018 by hgpu

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

Tsukasa Endo, Hasitha Muthumala Waidyasooriya, Masanori Hariyama

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Tags: Computer science, FPGA, OpenCL, Performance, Stencil computation

April 15, 2018 by hgpu

Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

Hamid Reza Zohouri, Artur Podobas, Satoshi Matsuoka

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Tags: Computer science, FPGA, OpenCL, Stencil computation

February 9, 2018 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

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Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Persistent Kernels for Iterative Memory-bound GPU Applications

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Efficient Nearest-Neighbor Data Sharing in GPUs

Accelerating High-Order Stencils on GPUs

High-Performance High-Order Stencil Computation on FPGAs Using OpenCL

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

On Optimizing Complex Stencils on GPUs

Delivering Performance-Portable Stencil Computations on CPUs and GPUs Using Bricks

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

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Prediction of Performance and Power Consumption of GPGPU Applications

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