Risk Management | hgpu.org

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Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

Wei Min, Weiming Liang, Hang Yin, Zhurong Wang, Mei Li, Alok Lal

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Tags: Algorithms, Clustering, Deep learning, Finance, nVidia, Risk Management, Tesla M40

January 17, 2021 by hgpu

Running Financial Risk Management Applications on FPGA in the Amazon Cloud

Javier Alejandro Varela, Norbert Wehn

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Tags: Finance, FPGA, OpenCL, Risk Management

February 10, 2018 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

Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures

Blesson Varghese, Andrew Rau-Chaplin

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Tags: Computational Engineering, Computer science, CUDA, Finance, nVidia, Risk Management, Tesla C2075

October 10, 2013 by hgpu

Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs

A. K. Bahl, O. Baltzer, A. Rau-Chaplin, B. Varghese, A. Whiteway

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Tags: Algorithms, Computer science, CUDA, Finance, nVidia, Risk Management, Stochastic simulation, Tesla C2075, Tesla M2090

August 13, 2013 by hgpu

A Novel GPU Implementation of Eigen Analysis for Risk Management

Mustafa U. Torun, Ali N. Akansu

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Tags: CUDA, Filtering, Finance, nVidia, Risk Management, Tesla C2070

July 29, 2012 by hgpu

Fast Greeks: Case of Credit Valuation Adjustments

Vytautas Savickas

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Tags: CUDA, Finance, nVidia, Risk Management, Tesla C2070, Thesis

January 13, 2012 by hgpu

Massively Parallelized Monte Carlo Simulation and its Applications in Finance

Ashkan Ziabakhshdeylami, Lauren Looi

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Tags: Algorithms, Finance, Monte Carlo simulation, nVidia, Risk Management, Tesla C2050

December 2, 2011 by hgpu

High performance finite difference PDE solvers on GPUs

Daniel Egloff

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Tags: CUDA, Finance, Finite difference, nVidia, Partial differential equations, PDEs, Risk Management, Tesla C1060

October 16, 2011 by hgpu

Quantile Mechanics II: Changes of Variables in Monte Carlo methods and a GPU-Optimized Normal Quantile

William T. Shaw, Nick Brickman

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Tags: Computational finance, CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia Quadro FX 4800, Risk Management, Statistical Finance

November 8, 2010 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

Running Financial Risk Management Applications on FPGA in the Amazon Cloud

GPU Accelerated framework for financial nested simulations

Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures

Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs

A Novel GPU Implementation of Eigen Analysis for Risk Management

Fast Greeks: Case of Credit Valuation Adjustments

Massively Parallelized Monte Carlo Simulation and its Applications in Finance

High performance finite difference PDE solvers on GPUs

Quantile Mechanics II: Changes of Variables in Monte Carlo methods and a GPU-Optimized Normal Quantile

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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