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Intel® Advisor Tutorials

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Intel® Advisor XE 2016 Vectorization Tutorials

The following tutorials show how to use the Vectorization Advisor, the Intel Advisor vectorization analysis tool, to:

  • Identify loops that will benefit most from vectorization.
  • Identify what is blocking effective vectorization.
  • Increase the confidence that vectorization is safe.
  • Explore the benefit of alternative data reorganizations.
Learn To Do ThisTake This Tutorial

Improve application performance using the Vectorization Advisor
Duration: 20 minutes

C++ Tutorial
Windows* OS: HTML
Linux* OS: HTML
Sample code: vec_samples

Intel® Advisor XE 2015 Threading Tutorials

The following tutorials provide a quick path to help you get started using Threading Advisor, the Intel Advisor threading design and prototyping tool. They demonstrate an end-to-end workflow you can ultimately apply to your own applications:

  1. Survey the target executable to locate the loops and functions where your application spends the most time.
  2. In the target sources, add Intel Advisor annotations to mark possible parallel tasks and their enclosing parallel sites.
  3. Check Suitability to predict the maximum parallel performance speedup of the target based on these annotations.
  4. Check Correctness to predict parallel data sharing problems in the target based on these annotations.
  5. If the predicted maximum speedup benefit is worth the effort to fix the predicted parallel data sharing problems, fix the problems.
  6. Recheck Suitability to see how your fixes impact the predicted maximum speedup.
  7. If the predicted maximum speedup benefit is still worth the effort to add parallelism to the target, replace the annotations with parallel framework code that enables parallel execution.
Learn To Do ThisTake This Tutorial

Find where to add parallelism using the Threading Advisor
Duration: 15-20 minutes

C++ Tutorial
Windows* OS: HTML | PDF
Linux* OS: HTML | PDF
Sample code: nqueens_Advisor

Fortran Tutorial
Windows* OS: HTML | PDF
Linux* OS: HTML | PDF
Sample code: nqueens


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