Article

Making the Monte Carlo Approach Even Easier and Faster

Introduction
作者: Andrey Naraikin (Intel) 最后更新时间: 2017/06/07 - 12:15
Article

Overview of Summary Statistics (SS) in Intel® MKL

 

作者: 最后更新时间: 2019/03/27 - 12:20
Article

Optimize Financial Applications using Intel® Math Kernel Library

Intel® Math Kernel Library (Intel® MKL) contains a wealth of highly optimized math functions that are fundamental to a wide variety of Financial Applications.

作者: Zhang, Zhang (Intel) 最后更新时间: 2018/05/25 - 15:30
Article

Non-Obvious Implementation Choices for the Monte Carlo Simulation

This article was written for the Monte Carlo Simulation of European Swaptions sample
作者: 最后更新时间: 2018/12/12 - 08:20
Article

Multithreaded Code Optimization in PARSEC* 3.0: BlackScholes

Learn about the Blach-Scholes benchmark, part of the benchmark suite of multithreaded programs that comprise the Princeton Application Repository for Shared-Memory Computers (PARSEC).
作者: Artem G. (Intel) 最后更新时间: 2019/07/04 - 21:42
Article

PARSEC* 3.0 中的多线程代码优化: BlackScholes

The Black-Scholes benchmark is a one of the 13 benchmarks in the PARSEC. This benchmark does option pricing with Black-Scholes Partial Differential Equation (PDE). The Black-Scholes equation is a differential equation that describes how, under a certain set of assumptions, the value of an option changes as the price of the underlying asset changes. Based on this formula, one can compute the...
作者: Artem G. (Intel) 最后更新时间: 2019/07/04 - 21:42
Article

Optimize Data Layout with SIMD Templates

Contrast results for manually tuning financial data and using data layout templates in the Intel® C++ Compiler.
作者: Nimisha R. (Intel) 最后更新时间: 2018/12/12 - 18:00
Article

Threading and Memory Analysis of Financial Applications with Intel® Inspector

 

Threading and Memory Analysis of Financial Applications with Intel® Inspector

by

Michael D’Mello, Ravi Vemuri

作者: Michael D. (Intel) 最后更新时间: 2019/02/27 - 14:08
Article

借助 SIMD 数据布局模板优化数据布局

Financial service customers need to improve financial algorithmic performance for models such as Monte Carlo, Black-Scholes, and others. SIMD programming can speed up these workloads. In this paper, we perform data layout optimizations using two approaches on a Black-Scholes workload for European options valuation from the open source Quantlib library.
作者: Nimisha R. (Intel) 最后更新时间: 2018/12/12 - 18:00
File Wrapper

Parallel Universe Magazine - Issue 12, November 2012

作者: 管理 最后更新时间: 2018/12/12 - 18:08