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Framework for Parallel Kernels Auto-tuning

Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenCL, Package, Performance, Thesis

November 10, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

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Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun

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Tags: Algorithms, Auto-Tuning, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

June 23, 2019 by hgpu

Dynamic Application Autotuning for Self-Aware Approximate Computing

Davide Gadioli

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Tags: Auto-Tuning, Computer science, OpenCL, Package, Thesis

March 31, 2019 by hgpu

The ANTAREX Domain Specific Language for High Performance Computing

Cristina Silvano, Giovanni Agosta, Andrea Bartolini, Andrea R. Beccari, Luca Benini, Loïc Besnard, João Bispo, Radim Cmar, João M. P. Cardoso, Carlo Cavazzoni, Daniele Cesarini, Stefano Cherubin, Federico Ficarelli, Davide Gadioli, Martin Golasowski, Antonio Libri, Jan Martinovič, Gianluca Palermo, Pedro Pinto, Erven Rohou, Kateřina Slaninová, Emanuele Vitali

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Tags: Auto-Tuning, Compilers, Computer science, DSL, OpenCL, Package

March 24, 2019 by hgpu

Parallel source code transformation techniques using design patterns

David del Rio Astorga

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Tags: Algorithms, Auto-Tuning, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Tesla K40, Thesis

December 2, 2018 by hgpu

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 by hgpu

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on Next-Generation Architectures

Simon Garcia De Gonzalo, Simon D. Hammond, Christian R. Trott, Wen-Mei Hwu

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Tags: Auto-Tuning, Computer science, CUDA, Intel Xeon Phi, nVidia, Sparse matrix, Tesla K20, Tesla K40, Tesla K80, Tesla P100

January 28, 2018 by hgpu

A Survey on Compiler Autotuning using Machine Learning

Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, Cristina Silvano

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Tags: Auto-Tuning, Compilers, Computer science, Machine learning, Performance

January 28, 2018 by hgpu

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Nicolas Weber, Sandra C. Amend and Michael Goesele

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Tags: Array Layouts, Auto-Tuning, CUDA, Profiling

February 9, 2016 by mergian

EASYPAP: a Framework for Learning Parallel Programming

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Interoperable GPU Kernels as Latency Improver for MEC

MNN: a lightweight deep neural network inference engine

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

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high performance computing on graphics processing units: hgpu.org

Framework for Parallel Kernels Auto-tuning

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Dynamic Application Autotuning for Self-Aware Approximate Computing

The ANTAREX Domain Specific Language for High Performance Computing

Parallel source code transformation techniques using design patterns

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on Next-Generation Architectures

A Survey on Compiler Autotuning using Machine Learning

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Recent source codes

EASYPAP: a Framework for Learning Parallel Programming

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

StarNEig: A Task-based Library for Solving Nonsymmetric Eigenvalue Problems

FLAMEGPU: a high performance GPU extension to the FLAME framework

CommAnalyzer

elevate: implementation of the Elevate language

reproducibility of TensorFlow with ResNet

SCHNAPS

Interoperable GPU Kernels as Latency Improver for MEC

MNN: a lightweight deep neural network inference engine

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Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019