User Guide

Contents

collect.py Options

Depending on options specified, collect basic data, do markup, and collect refinement data. By default, execute all steps. For any step besides markup, you must specify an application argument.

Usage

advisor-python <APM>/collect.py
<project-dir>
[--options] -- <target> [target-options]
Replace
<APM>
with
$APM
on Linux* OS or
%APM%
on Windows* OS.

Options

The following table describes options that you can use with the
collect.py
script. The target application to analyze and application options, if any, must be preceded by two dashes and a space.
Option
Description
<project-dir>
Required. Specify the path to the
Intel® Advisor
project directory.
-h
--help
Show all script options.
-v
<verbose>
--verbose
<verbose>
Specify output verbosity level:
  • 1 - Show only error messages. This is the least verbose level.
  • 2 - Show warning and error messages.
  • 3 (default) - Show information, warning, and error messages.
  • 4 - Show debug, information, warning, and error messages. This is the most verbose level.
This option affects the console output, but does not affect logs and report results.
-c
{basic, refinement, full}
--collect
{basic, refinement, full}
Specify the type of data to collect for an application:
  • basic
    - Collect basic performance data (Survey, Trip Counts, FLOP), analyze data transfer between host and device memory, attribute memory objects to loops, and track accesses to stack memory.
  • refinement
    - Collect refined data (Dependencies) for marked loops only. Do not analyze data transfers.
  • full
    (default) - Collect both basic data for application and refined data for marked loops, analyze data transfer between host and device memory and potential data reuse, attribute memory objects to loops, and track accesses to stack memory.
For
--collect full
, make sure to use
--data-reuse-analysis
and
--track-memory-objects
for the Performance modeling with
analyze.py
or
advisor --collect=projection
.
For
--collect basic
, make sure to use the
--track-memory-objects
for the Performance modeling with
analyze.py
or
advisor --collect=projection
.
--config
<config>
Specify a configuration file by absolute path or name. If you choose the latter, the model configuration directory is searched for the file first, then the current directory.
You can specify several configurations by using the option more than once.
--data-reuse-analysis
|
--no-data-reuse-analysis
(default)
Estimate data reuse between offloaded regions. Disabling can decrease analysis overhead.
--collect basic
and
--collect full
overwrite this option. To add the data reuse analysis results to the Offload Modeling report, make sure to use the
--data-reuse-analysis
option for the Performance modeling with
analyze.py
or
advisor --collect=projection
.
--data-transfer
(default) |
--no-data-transfer
Analyze data transfer.
Disabling can decrease analysis overhead.
--dry-run
Show the
Intel® Advisor
CLI commands for
advisor
appropriate for the specified configuration. No actual collection is performed.
--enable-edram
Enable eDRAM modeling in the memory hierarchy model.
Make sure to use this option with both
collect.py
and
analyze.py
.
--enable-slm
Enable SLM modeling in the memory hierarchy model.
Make sure to use this option with both
collect.py
and
analyze.py
.
--executable-of-interest
<executable-name>
Specify the executable process name to profile if it is not the same as the application to run. Use this option if you run your application via script or other binary.
Specify the
name
only, not the full path.
--flex-cachesim
<cache-configuration>
Use flexible cache simulation to model cache data for several target devices. The flexible cache simulation allows you to change a device for an analysis without recollecting data. By default, when no configuration is set, cache data is simulated for all supported target platforms.
You can also specify a list of cache configurations separated with a forward slash in the format
<size_of_level1>:<size_of_level2>:<size_of_level3>
. For each memory level size, specify a unit of measure as
b
- bytes,
k
- kilobytes, or
m
- megabytes.
For example,
8k:512k:8m/24k:1m:8m/32k:1536k:8m
.
--gpu
(recommended) |
--profile-gpu
|
--analyze-gpu-kernels-only
Model performance only for code regions running on a GPU. Use
one
of the three options.
Make sure to specify this option for both
collect.py
and
analyze.py
.
This is a
preview
feature.
--analyze-gpu-kernels-only
is deprecated and will be removed in futire releases.
--no-profile-jit
(default)
Disable JIT function analysis.
-m
[{all, generic, regions, omp, dpcpp, daal, tbb}]
--markup
[{all, generic, regions, omp, dpcpp, daal, tbb}]
Mark up loops after survey or other data collection. Use this option to limit the scope of further collections by selecting loops according to a provided parameter:
  • all
    - Get lists of loop IDs to pass as the option for further collections.
  • generic
    (default) - Mark up all regions and select the most profitable ones.
  • regions
    - Select already existing parallel regions.
  • omp
    - Select outermost loops in OpenMP* regions.
  • dpcpp
    - Select outermost loops in Data Parallel C++ (DPC++) regions.
  • daal
    - Select outermost loops in
    Intel® oneAPI Data Analytics Library
    regions.
  • tbb
    - Select outermost loops in
    Intel® oneAPI Threading Building Blocks (oneTBB)
    regions.
omp
,
dpcpp
, or
generic
selects loops in the project so that the corresponding collection can be run without loop selection options.
You can specify several parameters in a comma-separated list. Loops are selected if they fit any of specified parameters.
--model-system-calls
(default) |
--no-model-system-calls
Analyze regions with system calls inside. The actual presence of system calls inside a region may reduce model accuracy.
--mpi-rank
<mpi-rank>
Specify a MPI rank to analyze if multiple ranks are analyzed.
--no-cache-sources
Disable keeping source code cache within a project.
--no-cachesim
Disable cache simulation during collection. The model assumes 100% hit rate for cache.
Usage decreases analysis overhead.
--no-stacks
Run data collection without collecting data distribution over stacks. You can use this option to reduce overhead at the potential expense of accuracy.
-o
<output-dir>
--out-dir
<output-dir>
Specify the directory to put all generated files into. By default, results are saved in
<advisor-project>/e
<NNN>
/pp
<MMM>
/data.0
. If you specify an existing directory or absolute path, results are saved in specified directory. The new directory is created if it does not exist.
If you only specify the directory
<name>
, results are stored in
<advisor-project>/e
<NNN>
/pp
<MMM>
/
<name>
.
If you use this options, you might not be able to open the analysis results in the Intel Advisor GUI.
-p
<output-name-prefix>
--out-name-prefix
<output-name-prefix>
Specify a string to add to the beginning output result filenames.
If you use this options, you might not be able to open the analysis results in the Intel Advisor GUI.
--set-parameter
<CLI-config>
Specify a single-line configuration parameter to modify in a format
"<group>.<parameter>=<new-value>"
. For example:
"min_required_speed_up=0"
,
"scale.Tiles_per_process=0.5"
. You can use this option more than once to modify several parameters.
Make sure to use this option for both
collect.py
and
analyze.py
with the same value.
--track-heap-objects
|
--no-track-heap-objects
Deprecated. Use
--track-memory-objects
.
--track-memory-objects
(default) |
--no-track-memory-objects
Attribute heap-allocated objects to the analyzed loops that accessed the objects.
This option is always enabled with
--collect basic
and
--collect full
. To add the data reuse analysis results to the Offload Modeling report, make sure to use also the
--track-memory-objects
option for the Performance modeling with
analyze.py
or
advisor --collect=projection
.
--track-stack-accesses
(default) |
--no-track-stack-accesses
Track accesses to stack memory.
This option is always enabled with
--collect basic
and
--collect full
. To add the data reuse analysis results to the Offload Modeling report, make sure to use also the
--track-memory-objects
option for the Performance modeling with
analyze.py
or
advisor --collect=projection
.

Examples

  • Collect full data on
    myApplication
    with default configuration and save the project to the
    ./advi
    directory.
    advisor-python $APM/collect.py ./advi_results -- ./myApplication
  • Collect refinement data for OpenMP* and DPC++ loops on
    myApplication
    with a custom configuration file
    config.toml
    and save the project to the
    ./advi
    directory.
    advisor-python $APM/collect.py ./advi_results --collect refinement --markup [omp,dpcpp] --config ./config.toml -- ./myApplication
  • Get commands appropriate for a custom configuration specified in the
    config.toml
    file to collect data separately with
    advisor
    . The commands are ready to copy and paste.
    advisor-python $APM/collect.py ./advi_results --dry-run --config ./config.toml

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.