Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
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
This tutorial shows how to install Offload over Fabric (OoF) software on 2nd generation Intel® Xeon Phi™ processor, configure the hardware, test the basic configuration, and enable OoF
学习如何在英特尔® 至强融核™ 处理器中使用 MPI-3 共享内存