This paper is a more formal response to an Intel® Developer Zone forum posting. See: (https://software.intel.com/en-us/forums/intel-moderncode-for-parallel-architectures/topic/590710).
Parallelize loops with Intel® Threading Building Blocks using Intel® C++ Compiler for lambda expressions.
Contrast results for manually tuning financial data and using data layout templates in the Intel® C++ Compiler.
Improve your vectorization project using techniques and methodologies from Intel.
Эта статья представляет собой формализованный ответ на публикацию на форуме Intel® Developer Zone. См.: (https://software.intel.com/en-us/forums/intel-moderncode-for-parallel-architectures/topic/590710).
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.
In this paper, we walk through a 3D Animation algorithm example and describe some techniques and methodologies that may benefit your next vectorization endeavors. We also integrate the algorithm with SIMD Data Layout Templates (SDLT), which is a feature of Intel® C++ Compiler, to improve data layout and SIMD efficiency. Includes code sample.
In this tutorial, we demonstrate some possible ways to optimize an application to run on the Intel® Xeon Phi™ processor
Find out how to use the command-line interface in Intel® Advisor 2017 for a quick, initial analysis of loop performance that gives an overview of the hotspots in your code.