Tutorials and Samples
Discover Where Vectorization Pays Off The Most
- 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.
Prototype Threading Designs
- Survey the target executable to locate the loops and functions where your application spends the most time.
- In the target sources, addIntel Advisorannotations to mark possible parallel tasks and their enclosing parallel sites.
- Check Suitability to predict the maximum parallel performance speedup of the target based on these annotations.
- Check Dependencies to predict parallel data sharing problems in the target based on these annotations.
- If the predicted maximum speedup benefit is worth the effort to fix the predicted parallel data sharing problems, fix the problems.
- Recheck Suitability to see how your fixes impact the predicted maximum speedup.
- 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.
Use the Automated Roofline Chart to Make Optimization Decisions - C++ Sample
- What is the maximum achievable performance with your current hardware resources?
- Does your application work optimally on current hardware resources?
- If not, what are the best candidates for optimization?
- Is memory bandwidth or compute capacity limiting performance for each optimization candidate?
- Sample applications are non-deterministic.
- Sample applications are designed only to illustrate theIntel Advisorfeatures and do not represent best practices for creating and optimizing code.