high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications » Finance

Financial Derivatives Modeling Using GPU’s

Myungho Lee, Chin Hong Chun, Sugwon Hong

Tags: Computational finance, Finance, Optimization

April 25, 2011 by hgpu

Challenges of mapping financial analytics to many-core architecture

M. Smelyanskiy

Tags: Computational finance, Finance

April 22, 2011 by hgpu

Linear optimization on modern GPUs

Daniele G. Spampinato, Anne C. Elster

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Tags: CUDA, Finance, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Optimization

April 18, 2011 by hgpu

A High-Performance Multi-user Service System for Financial Analytics Based on Web Service and GPU Computation

Gow-Hsing King, Zong-You Cai, Yan-Ying Lu, Jan-Jan Wu, Hung-Pin Shih, Chao-Rui Chang

Tags: Finance, nVidia, nVidia GeForce GTX 275, Tesla C1060

April 16, 2011 by hgpu

Parallel implementation of a Quantization algorithm for pricing American style options on GPGPU

G. Pages, B. Wilbertz

Tags: CUDA, Finance, nVidia

April 15, 2011 by hgpu

GPU-computing in econophysics and statistical physics

T. Preis

Tags: Computer science, CUDA, Finance, Ising model, nVidia, Physics

April 11, 2011 by hgpu

Scalable and Parallel Implementation of a Financial Application on a GPU: With Focus on Out-of-Core Case

Myungho Lee, Jin-hong Jeon, Joonsuk Kim, Joonhyun Song

Tags: Finance, Monte Carlo simulation

March 27, 2011 by hgpu

On Using GPU to Compute Options and Derivatives

Option Models

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Tags: Computational finance, CUDA, Finance, nVidia, nVidia GeForce 8800 GT

March 23, 2011 by hgpu

Option Pricing on the GPU

Steven Solomon, Ruppa K Thulasiram, Parimala Thulasiraman

Tags: Computational finance, Finance

March 14, 2011 by hgpu

Application-guided tool development for architecturally diverse computation

Roger D. Chamberlain, Jeremy Buhler, Mark A. Franklin, James H. Buckley

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Tags: Algorithms, Astrophysics, Biology, Computer science, CUDA, Finance, FPGA, Heterogeneous systems, Monte Carlo simulation, nVidia, nVidia GeForce GTX 260, Search, Security

March 7, 2011 by hgpu

Accelerated Root Finding for Computational Finance

Mark J. Bennett

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Tags: Algorithms, Computational finance, Differential equations, Finance, Stochastic simulation

February 21, 2011 by hgpu

Acceleration of Binomial Options Pricing via Parallelizing along time-axis on a GPU

Narayan Ganesan, Roger D. Chamberlain, Jeremy Buhler

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Tags: CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce 8600 GT

February 21, 2011 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Financial Derivatives Modeling Using GPU’s

Challenges of mapping financial analytics to many-core architecture

Linear optimization on modern GPUs

A High-Performance Multi-user Service System for Financial Analytics Based on Web Service and GPU Computation

Parallel implementation of a Quantization algorithm for pricing American style options on GPGPU

GPU-computing in econophysics and statistical physics

Scalable and Parallel Implementation of a Financial Application on a GPU: With Focus on Out-of-Core Case

On Using GPU to Compute Options and Derivatives

Option Pricing on the GPU

Application-guided tool development for architecturally diverse computation

Accelerated Root Finding for Computational Finance

Acceleration of Binomial Options Pricing via Parallelizing along time-axis on a GPU

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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