high performance computing on graphics processing units: hgpu.org

Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

February 28, 2017 by hgpu

liquidSVM: A Fast and Versatile SVM package

Ingo Steinwart, Philipp Thomann

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Tags: Computer science, CUDA, Java, nVidia, Package, Python, Spark

February 26, 2017 by hgpu

Large-Scale Stochastic Learning using GPUs

Thomas Parnell, Celestine Dunner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro M 4000

February 26, 2017 by hgpu

Data-Parallel Flattening by Expansion

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

MGPUSim: Enabling Multi-GPU Performance Modeling and Optimization

TensorNetwork: A library for easy and efficient manipulation of tensor networks

TensorNetwork for Machine Learning

triSYCL: Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group

SYCL Code Generation for Multigrid Methods

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

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Boston, MA, USA

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

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Larnaca, Cyprus

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

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Rome, Italy

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

2019

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26-28

Rome, Italy

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

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high performance computing on graphics processing units: hgpu.org

GPU accelerated population annealing algorithm

Large-scale image analysis using docker sandboxing

Model-independent partial wave analysis using a massively-parallel fitting framework

Decoupled Block-Wise ILU(k) Preconditioner on GPU

Performance and Portability of Accelerated Lattice Boltzmann Applications with OpenACC

Billion-scale similarity search with GPUs

An Efficient Multiway Mergesort for GPU Architectures

Key Reconciliation with Low-Density Parity-Check Codes for Long-Distance Quantum Cryptography

Deep Voice: Real-time Neural Text-to-Speech

liquidSVM: A Fast and Versatile SVM package

Large-Scale Stochastic Learning using GPUs

Recent source codes

Data-Parallel Flattening by Expansion

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

MGPUSim: Enabling Multi-GPU Performance Modeling and Optimization

TensorNetwork: A library for easy and efficient manipulation of tensor networks

triSYCL: Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group

PSST: A Tutorial for Parallelised Scalable Simulations in TensorFlow

MUSE: Miami University Simulation Environment

Pressure Point Analysis framework

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

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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