Contrast results for manually tuning financial data and using data layout templates in the Intel® C++ Compiler.
STAC-A2 is a set of specifications defined by leading financial institutions, academia, and hardware vendors to represent realistic market risk analysis workloads. This article describes testing that measured the performance scaling of a system consisting of two Intel® Xeon® processors E5-2697 v3 and two Intel® Xeon Phi™ coprocessors 7120P or Intel® Xeon Phi™ processor 7250 and running the...
This article covers the Monte Carlo Methods using a simple quasi random number generator.
Financial derivative pricing is a cornerstone of quantitative finance. The most common form of financial derivatives is common stock options, which are contracts between two parties regarding buying or selling an asset (specifically shares of stock) at a certain time at an agreed price.
Threading and Memory Analysis of Financial Applications with Intel® Inspector 2017
Michael D’Mello, Ravi Vemuri
Q: What are the Clock speed and IPC improvements for IVB-EP?
This is an exercise in performance optimization on heterogeneous Intel architecture systems based on multi-core processors and manycore (MIC) coprocessors.
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.
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