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Tensor Processing Units for Financial Monte Carlo

Francois Belletti, Davis King, Kun Yang, Roland Nelet, Yusef Shafi, Yi-Fan Chen, John Anderson

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Tags: Cloud, Deep learning, Finance, Mixed precision, Neural networks, nVidia, Option pricing, Tesla V100, TPU

June 12, 2019 by hgpu

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro

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Tags: Computer science, CUDA, Deep learning, LSTM, Mixed precision, Neural networks, nVidia, Package, Tesla V100, Torch

August 11, 2018 by hgpu

Aspect-Driven Mixed-Precision Tuning Targeting GPUs

Ricardo Nobre, Luis Reis, Joao Bispo, Tiago Carvalho, Joao M.P. Cardoso, Stefano Cherubin, Giovanni Agosta

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Tags: AMD Vega 10 XT, ATI, Code generation, Computer science, Mixed precision, nVidia, OpenCL, Package

June 13, 2018 by hgpu

NVIDIA Tensor Core Programmability, Performance & Precision

Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Mixed precision, nVidia, Performance, Tesla V100

March 17, 2018 by hgpu

Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers

Azzam Haidar, Panruo Wu, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Artificial intelligence, BLAS, Computer science, Linar Algebra, Mixed precision, Neural networks, nVidia, Tesla P100

December 10, 2017 by hgpu

Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System

Taisuke Boku, Ishikawa Ken-Ichi, Yoshinobu Kuramashi, Lawrence Meadows

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Tags: Computational Physics, High Energy Physics - Lattice, Intel Xeon Phi, Mixed precision, MPI, OpenMP, Package, Physics, QCD

September 28, 2017 by hgpu

Automatic code generation methods applied to numerical linear algebra in high performance computing

M. Ian Masliah

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Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia Tegra TX1, Programming techniques, Tesla C2075, Tesla K40, Thesis

November 16, 2016 by hgpu

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Janus Juul Eriksen

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Tags: Chemical Physics, Chemistry, Mixed precision, nVidia, OpenACC, Physics, Tesla K40

September 27, 2016 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Lukas Einkemmer

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Tags: Computational Physics, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, Mixed precision, nVidia, OpenMP, Tesla K80

March 25, 2016 by hgpu

Optimising Reconfigurable Systems for Real-time Applications

Thomas Chun Pong Chau

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, Mixed precision, nVidia, Package, Tesla C2070, Thesis

August 7, 2015 by hgpu

Towards an automatic generation of dense linear algebra solvers on parallel architectures

Marc Baboulin, Joel Falcou, Ian Masliah

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Tags: Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, Tesla C2075

December 20, 2014 by hgpu

Watershed analysis algorithm implementation in CUDA and OpenACC

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

OpenCL Performance Prediction using Architecture-Independent Features

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

PySPH: a Python-based SPH framework

PySPH: a Python-based framework for smoothed particle hydrodynamics

Efficient Interleaved Batch Matrix Solvers for CUDA

Efficient Interleaved Batch Matrix Solvers for CUDA

ArborX: Performance-portable geometric search library

ArborX: A Performance Portable Search Library

FlowSeq: Generative Flow based Sequence-to-Sequence Toolkit

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Compositional Deep Learning in Futhark

Compositional Deep Learning in Futhark

MLPerf Benchmark Suite

Demystifying the MLPerf Benchmark Suite

stdgpu: Efficient STL-like Data Structures on the GPU

stdgpu: Efficient STL-like Data Structures on the GPU

ikra-cpp: C++ Library for Object-oriented Programming with Structure of Arrays Layout

Memory-Efficient Object-Oriented Programming on GPUs

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

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

2019

May

14

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

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