In the Microarchitecture Exploration viewpoint, click the

Summary

tab to switch to the Summary window.

The first section displays the summary statistics on the overall application execution per hardware-related metrics measured in Pipeline Slots or Clockticks. Metrics are organized by execution categories in a list and also represented as a µPipe diagram. To view a metric description, mouse over the help icon :

In the example above, mousing over the

L1 Bound

metric displays the metric description in the tooltip.

A flagged metric value signals a performance issue for the whole application execution. Mouse over the flagged value to read the issue description:

You may use the performance issues identified by the

VTune

Profiler

as a baseline for comparison of versions before and after optimization. Your primary performance indicator is the Elapsed time value.

Grayed out metric values indicate that the data collected for this metric is unreliable. This may happen, for example, if the number of samples collected for PMU events is too low. In this case, when you hover over such an unreliable metric value, the

VTune

Profiler

displays a message:

You may either ignore this data, or rerun the collection with the data collection time, sampling interval, or workload increased.

By default, the

VTune

Profiler

collects Microarchitecture Exploration data in the

Detailed

mode. In this mode, all metric names in the Summary view are hyperlinks. Clicking such a hyperlink opens the

Bottom-up

window and sorts the data in the grid by the selected metric. The lightweight

Summary

collection mode is limited to the Summary view statistics.

To view hardware issues per a program unit, switch to the Bottom-up pane. Each row represents a program unit and percentage of time used by this unit. Program units that take more than 5% of the CPU time are considered as

hotspots

. By default, the

VTune

Profiler

sorts the data in the descending order by Clockticks and provides the hotspots at the top of the list.

Most of the columns in the

Bottom-up

pane represent a hardware performance metric.

VTune

Profiler

calculates a metric based on the formula provided by Intel architects. Mouse over the column header to read the metric description. By default, metric values are represented as numbers. You can change the representation mode with the

Show Data As

context menu option.

The right pane displays a context summary for the selected function. Analyze per-function hardware metrics and their visual representation on the µPipe diagram to estimate the contribution of this particular function to the overall performance.

Each metric has a threshold value. If the metric value exceeds the threshold and the program unit is a hotspot, the

VTune

Profiler

highlights this value in pink as performance-critical. Mouse over each pink cell to read a description of the issue and recommended solution (if any).

In the example above, created on the Intel microarchitecture code name Skylake, the

VTune

Profiler

identified the

sphere_intersect

function as one of the biggest hotspots that took much CPU time.

VTune

Profiler

detected that the back-end portion of the pipeline caused the stalls. For the back-end, the

VTune

Profiler

identified

Memory Bound > L1 Bound

issue as a dominant bottleneck. 14.6% of Clockticks used in this function was stalled missing L1 data cache. This means that if you focus on this function hotspot and optimize it, you can potentially gain ~15% speed-up for this function.

VTune

Profiler

is able to identify the most common types of pipeline bottlenecks. You may go deeper for more details. If the deeper levels of the metrics do not display any data, it means that the

VTune

Profiler

cannot see a dominant bottleneck on the lower level.

When you identified a critical function, double-click it to open the

Source

/

Assembly

window and analyze the source code.

The

Source

/

Assembly

window displays locator metrics that show what code contributed the most to the issue represented by the metric. For example, if you have the Back-End Bound metric equal to 60% for your function, the source view for this function splits the 60% value across function source lines or instructions to help you identify a source line/instruction with the biggest value contributing the most to the total 60% Back-End Bound metric.

Use the hotspots navigation toolbar buttons to navigate to the biggest hotspot for each locator metric and identify the code to optimize.