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Case Study: Computing Black-Scholes with Intel® Advanced Vector Extensions

Case study discusses Intel® Advanced Vector Extensions (Intel® AVX), gives an overview of Black-Scholes valuation.
Authored by Last updated on 07/06/2019 - 16:27
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基于英特尔® 架构加速金融应用

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Authored by George Raskulinec (Intel) Last updated on 07/06/2019 - 16:40
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案例研究: 使用分布式优化框架在 Monte Carlo 欧式期权方面实现高级性能

1. 简介

Monte Carlo 使用统计计算方法解决复杂的科学计算问题。 它创新地使用随机数字模拟一个问题输入结果的不确定性,并通过处理重复的参数抽样获得一个确定的结果和解决一些以其他方式无法解决的问题。 该方法最早起源于上世纪 40 年代末,由参与“曼哈顿”计划的核物理学家们率先提出。 并采用摩纳哥最大的赌城 Monte Carlo 来命名。

Authored by Last updated on 07/06/2019 - 16:40
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面向同步异构计算的结构化性能优化框架

随着 HP Apollo 6000 系统的 ProLiant XL250a 服务器宣布支持英特尔® 至强融核™ 协处理器,基于多核主机系统和众核加速器设备的异构计算平台在进军主流 HPC 计算市场方面实现了重大飞跃。 尽管许多应用开发商尝试使用该平台的方式与 GPGPU 加速平台相同,但这种做法会降低多核主机处理器的处理能力,以及企业 IT 运营的效率。

Authored by Last updated on 03/21/2019 - 12:00
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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).
Authored by Artem G. (Intel) Last updated on 07/04/2019 - 21:42
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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...
Authored by Artem G. (Intel) Last updated on 07/04/2019 - 21:42
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Optimize Data Layout with SIMD Templates

Contrast results for manually tuning financial data and using data layout templates in the Intel® C++ Compiler.
Authored by Nimisha R. (Intel) Last updated on 12/12/2018 - 18:00
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Monte-Carlo simulation on Asian Options Pricing

This is an exercise in performance optimization on heterogeneous Intel architecture systems based on multi-core processors and manycore (MIC) coprocessors.
Authored by Mike P. (Intel) Last updated on 03/21/2019 - 12:00
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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.
Authored by Nimisha R. (Intel) Last updated on 12/12/2018 - 18:00
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用于亚洲期权定价的 Monte Carlo 模拟

This is an exercise in performance optimization on heterogeneous Intel architecture systems based on multi-core processors and manycore (MIC) coprocessors.
Authored by Mike P. (Intel) Last updated on 03/21/2019 - 12:00