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Free-form interest rate term structure decomposition: a 2nd order optimization problem

Brian Barnard

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Tags: Finance, Optimization, term structure decomposition

December 14, 2015 by little_jimmy

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Blesson Varghese, Javier Prades, Carlos Reano, Federico Silla

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Tags: Computer science, CUDA, Finance, nVidia, Tesla K20, Virtualization

August 12, 2015 by hgpu

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

Gordon Inggs, David B. Thomas, Wayne Luk

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Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Grid K2, nVidia Quadro K4000, OpenCL, Option pricing, Performance

May 20, 2015 by hgpu

GPU Accelerated framework for financial nested simulations

Joris Cramwinckel

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Tags: CUDA, Finance, nVidia, Risk Management, Tesla C2050, Tesla K20, Thesis

April 27, 2015 by hgpu

A Financial Benchmark for GPGPU Compilation

Christian Andreetta, Vivien Begot, Jost Berthold, Martin Elsman, Troels Henriksen, Maj-Britt Nordfang, Cosmin E. Oancea

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Tags: Benchmarking, Computer science, CUDA, Finance, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 780 Ti

March 23, 2015 by hgpu

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

Blesson Varghese

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Tags: Computational Engineering, Computer science, CUDA, Finance, Intel Xeon Phi, nVidia, Performance, Tesla C2075

January 28, 2015 by hgpu

Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads

Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed, Ivor Spence, Richard Faloon, Dimitrios S. Nikolopoulos

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Tags: ARM, Computer science, Finance, Intel Xeon Phi, Option pricing

January 5, 2015 by hgpu

High-accuracy Optimization by Parallel Iterative Discrete Approximation and Multi-GPU Computing

Di Zhao

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Tags: Biology, Computational biology, Computational finance, CUDA, Finance, nVidia, nVidia GeForce GTX 660, Thesis

November 13, 2014 by hgpu

Performance Engineering of the Kernel Polynomial Method on Large-Scale CPU-GPU Systems

Moritz Kreutzer, Georg Hager, Gerhard Wellein, Andreas Pieper, Andreas Alvermann, Holger Fehske

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Tags: Computational Engineering, CUDA, Finance, Mesoscale and Nanoscale Physics, nVidia, Tesla K20

October 22, 2014 by hgpu

GPU Implementation of a Deep Learning Network for Financial Prediction

Robin Kumar, Amandeep Kaur Cheema

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Tags: Artificial intelligence, Finance, Java, Machine learning, Neural networks, OpenCL

September 17, 2014 by hgpu

Accelerated Variance Reduction Methods on GPU

Chuan-Hsiang Han, Yu-Tuan Lin

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Tags: Algorithms, Computational finance, CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 690, Option pricing, Probability

September 15, 2014 by hgpu

High Performance Financial Simulation Using Randomized Quasi-Monte Carlo Methods

Linlin Xu, Giray Okten

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Tags: Algorithms, Computational finance, CUDA, Finance, nVidia, nVidia GeForce GTX 670

August 27, 2014 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Masivo: Parallel public transport routes simulation

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

FBLAS: BLAS implementation for Intel FPGA

FBLAS: Streaming Linear Algebra Kernels on FPGA

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2

Benchmarking Deep Learning Models on Jetson TX2

Hacking Neural Networks: A small course on exploiting and defending neural networks

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

Deep Learning Based FPGA-CPU Acceleration

KTT: Kernel Tuning Toolkit

Framework for Parallel Kernels Auto-tuning

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

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

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