Accuracy Level

Enabled Analyses

Result Interpretation

More accurate estimations of traffic and time for all cache and memory levels.
Measured data transfer and estimated data transfer between host and device memory.
Total data for the loop/function from different callees.

In the
Code Regions
metrics table:
In the
Estimated Bounded By
column, review how much time is spent to transfer data (data transfer tax). In the
Taxes with Reuse
column, see the biggest and total time taxes paid for offloading a code regions to a target platform.
Expand the
Estimated Bounded By
group to see a full picture of all time taxes paid for offloading the region to the target platform.
In the
Estimated Data Transfer with Reuse
column, review how much data is transferred per kernel in different directions (from host to device, from device to host). Expand the column to see data per memory level.
In the
Memory Estimations
column, see how well your application uses resources of all memory levels. Expand the group to see more detailed and accurate metrics for different memory levels.

Select a code region from the table and review the details about data transferred between host and device memory in the
Data Transfer Estimations
pane.
In the
Transferred Data & Tax
histogram, see the distribution of data transferred between the host and target devices in each direction.
See hints about optimizing data transfers in the selected code region.

In the
Recommendations
tab, get guidance for offloading your code to a target device and optimizing it so that your code benefits the most. If the code region has room for optimization or underutilizes the capacity of the target device,
Intel Advisor
provides you with hints and code snippets that might be helpful to you for further code improvement.

Next Steps

Set the data transfer simulation under the characterization analysis to
Medium
and run the perspective. The result should have the
Data Transfer Estimations
pane extended with new data reporting information about memory objects in each code region.
Offloaded Objects
pane shows a list of memory objects with data about each object aggregated between different instances of one region.

Analytics
histogram shows the number of memory objects that the selected region accessed distributed by their size.

Set the data transfer simulation under the characterization analysis to
High
and enable the
Data Reuse Analysis
checkbox under the Performance Modeling analysis. With data reuse analysis,
Intel Advisor
detects groups of parallel code regions that can reuse memory objects transferred to a target GPU device. Such memory objects can be transferred to GPU only once and reused, which can improve data transfer efficiency.
The result should have data transfer metrics in the
Code Regions
pane estimated with and without data reuse for each code region. Examine the metrics in the
Estimated Bounded By
and
Estimated Data Transfer with Reuse
columns to check if a code region can benefit from applying data reuse.
For code regions that can benefit from data reuse, you should see
Apply Data Reuse
guidance in the
Recommendations
tab. The guidance shows the data transfer estimated with and without data reuse and the performance gain from applying the data reuse. It also explains how you can apply the data reuse technique to your code.

If you think that the estimated speedup is enough and the application is ready to be offloaded, rewrite your code to offload profitable code regions to a target platform and measure performance of GPU kernels with GPU Roofline Insights perspective.

See Also

Optimize Your GPU Application with the Intel® oneAPI Base Toolkit
Intel® oneAPI Programming Guide