Instructions for how-to use the "Enabling collector for Linux*" to collect Intel® Thread Profiler data on a Linux* system and view the results with the Intel® Thread Profiler for Windows*, part of the Intel® VTune Performance Analyzer for Windows*.
When confronted with nested loops, the granularity of the computations that are assigned to threads will directly affect performance. Loop transformations such as splitting and merging nested loops can make parallelization easier and more productive.
One key to attaining good parallel performance is choosing the right granularity for the application. Granularity is the amount of real work in the parallel task. If granularity is too fine, then performance can suffer from communication overhead.
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.
避免线程之间发生堆冲突 (PDF 256KB)
检测线程应用中的内存带宽饱和度 (PDF 231KB)
使用深度学习部署工具套件 (DLDT) 部署深度学习算法，以解决角色的反向运动学 (IK) 问题。
MSC.Software SimXpert* is a fully integrated simulation environment for performing multidiscipline based analysis with a graphical interface designed to facilitate the end-to-end simulations. This article describes the threading of SimXpert.
OpenVINO™ 2018 R3 Release - Gold release of the Intel® FPGA Deep Learning Acceleration Suite accelerates AI inferencing workloads using Intel® FPGAs that are optimized for performance, power, and cost, Windows* support for the Intel® Movidius™ Neural Compute Stick, Python* API preview that supports the inference engine, Open Neural Network Exchange (ONNX) Model Zoo provides initial support for...