high performance computing on graphics processing units: hgpu.org

hgpu.org » Auto-Tuning

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

Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

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

View

Download (PDF)

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

View

Download (PDF)

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

February 9, 2016 by mergian

Hierarchical Product Quantization for Approximate Nearest Neighbors Search

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

hlslib: A collection of extensions for Vivado HLS and Intel FPGA OpenCL to improve developer quality of life

hlslib: Software Engineering for Hardware Design

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

MCL kernel verification library

Verification of GPU Program Optimizations in Lean

waLBerla: A block-structured high-performance framework for multiphysics simulations

MIOpen: AMD's Machine Intelligence Library

MIOpen: An Open Source Library For Deep Learning Primitives

horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet

Exascale Deep Learning for Scientific Inverse Problems

Futhark Vulkan Backend

Megatron-LM: Ongoing research training transformer language models at scale, including: BERT & GPT-2

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

See all packages

* * *

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

Boston, MA, USA

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

2019

May

Larnaca, Cyprus

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

2019

July

26-28

Rome, Italy

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

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

Add event

See all events

high performance computing on graphics processing units: hgpu.org

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

Hierarchical Product Quantization for Approximate Nearest Neighbors Search

hlslib: A collection of extensions for Vivado HLS and Intel FPGA OpenCL to improve developer quality of life

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

MCL kernel verification library

waLBerla: A block-structured high-performance framework for multiphysics simulations

MIOpen: AMD's Machine Intelligence Library

horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet

Futhark Vulkan Backend

Megatron-LM: Ongoing research training transformer language models at scale, including: BERT & GPT-2

Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

Most viewed papers (last 30 days)

Featured events

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

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019