Intel Software Development Tools

Aplication Performance Snapshot

Aplication Performance Snapshot is a tool for application profiling. This application is included to the Intel Parallel Studio installed on the HybriLIT cluster.
Aplication Performance Snapshot allows analyze such parameters as memory usage, work time of each process, operating delay, etc.

In order tp profile an application, it’s necessary to:

  1. Load a module that provides support of envarinmental variables for work with Intel Parallel Studio:

  1. Compile the application by means of loading a library:

For MPI programs

for OpenMP programs


for a sequential program where name_app.cpp – is the name of the compiled file.

  1. Run the executable file (by default a.out) with the key – aps, or using a script-file:

For MPI programs

for OpenMP programs

for a sequential program

  1. All received data will be placed in the folder aps_result_[launch date]. In order to display these data, generate a report in HTML (pic 1, pic 2) using the following command:

Pic.1 . Screenshot of an output-file of Aplication Perfomance Snapshot for MPI application (click the picture to see it at the report page)


Pic. 2. Screenshot of an output-file of Aplication Perfomance Snapshot for OpenMP application (click the picture to see it at the report page)


Intel Trace Analyzer and Collector

Intel Trace Analyzer and Collector is aimed at tracing MPI processes..

Main tasks:

  • Behavior vizualization of a parallel application;
  • Estimation of statistic profile and load balancing;
  • Performance analysis of programs and code blocks;
  • Detailing of model of exchange and performance data;
  • Detection of a hotspot;
  • Decrease of execution time and increase of application efficiency.

To launch an application use parameter –trace. Please see an example below:

To view results, please use ITAC GUI by means of the command:

As a result, an application winsow will be opened and you will need to upload a file (a.out.stf by default). An examples is shown on Pic. 3.

Distribution of computational load on MPI processes and interprocess communication (Event Timeline scale) are shown at the top of Pic.3.; numeric values are shown at the bottom of Pic.3.


Pic. 3. Examples of communication in an MPI application.

By means of Message Profile, it is now possible to estimate which processes comunicate and which communications are the most time consuming (Pic. 4.).


Pic. 4. Interprocess communication in an MPI application.

In Collective operations page, it is possible to prepare a table that reflects total span time for collective operations in MPI application (Pic.5.).


Рис. 5. Таблица временных затрат на коллективные операции в MPI-приложении.

Application Performance Snapshot —  is aimed at fast estimation of efficiency;  it doesn’t require additional charges; provides profiling up to 32000 MPI processes; allows get fasr estimation of MPI and OpenMP disbalance; and provides total estimation of performance (GFLOPS).
Intel Trace Analyzer and Collector — allows carrying out detailed analysis of MPI applications, detect communication patterns, and locate specific bottle neck of programs.

Open Visual Inference & Neural Network Optimization

OpenVINO (Open Visual Inference и Neural Network Optimization) is a free set of tools that simplifies the optimization of the deep learning model from the infrastructure and deployment using the output mechanism on Intel hardware.

The Movidius Neural Compute Stick (NCS Movidius)is a miniature accelerator for solutions related to artificial intelligence, neural networks and deep learning. The accelerator allows implementing elements of artificial intelligence on various platforms and devices. The given device is not designed for training neural network models.

The major advantage of the product is its performance, minimum size and almost zero number of dependencies. The product is ideal for implementing applications that use Deep Learning and Computer Vision to solve problems. Its disadvantage is that support for training neural networks is not included in the product.

This manual describes the steps to install and configure the tool OpenVINO Tools for Windows and Linux, as well as the work with the model optimizer.

For output to Inference Engine, read the documentation specifically for your case: Inference Engine Developer Guide