• 2022
  • 09/08/2022
  • Public Content

Intel® Advisor
Command Line Interface to Analyze a GPU Application

This recipe illustrates how to use
Intel® Advisor
command line interface (CLI) to run
GPU Roofline Insights
perspective on a SYCL implementation of the Mandelbrot application running on a graphics processing unit (GPU) and visualize results in command line output, Python* API, graphical user interface (GUI), and Interactive HTML report.
Intel Advisor
provides the
GPU Roofline Insights
perspective to evaluate and improve performance of GPU kernels in SYCL, C++/Fortran with OpenMP* target, Intel® oneAPI Level Zero API (Level Zero), and OpenCL™ applications. Use
GPU Roofline Insights
perspective to do the following:
  • Evaluate code executed on a GPU to see how close the performance is to the current hardware-imposed ceilings.
  • Detect and prioritize bottlenecks by estimated performance gain and understand their likely causes, such as memory bound vs compute bound.
  • Pinpoint the exact compute peak or memory level (caches, memory, or compute throughput) causing a bottleneck.
  • Identify which optimizations will pay off the most and apply actionable code restructuring recommendations specific to your application.
  • Visualize optimization progress and compare different code versions plotted on a single Roofline chart.


This section lists the hardware and software used to produce the specific results shown in this recipe:
  • Performance analysis tool:
    Intel Advisor
    Available for download as a standalone installation and as part of
    Intel® oneAPI Base Toolkit
  • Application:
    Mandelbrot is a SYCL application that generates a fractal image by matrix initialization and performs pixel-independent computations.
  • Compiler:
    Intel® oneAPI DPC++/C++ Compiler 2021
    Available for download as part of
    Intel® oneAPI Base Toolkit
  • Operating system:
    Ubuntu* 20.04.2 LTS
  • CPU:
    Intel® Core™ i7-8559U
  • GPU:
    Intel® Iris® Plus Graphics 655
You can download a precollected GPU Roofline report for the SYCL Mandelbrot application to follow this recipe and examine the analysis results.


  1. Set up environment:
    source <oneapi-install-dir>/
  2. Compile the sample application:
    cd mandelbrot/ && mkdir build && cd build && cmake .. && make
  3. Configure your system to analyze GPU kernels.

GPU Roofline Insights

To collect GPU Roofline data, run the following command line:
advisor --collect=roofline --profile-gpu --project-dir=./adv_gpu_roofline -- ./src/mandelbrot
After you run the command,
Intel Advisor
collects data both for GPU kernels and CPU loops/functions in your application to plot a Roofline chart.
Continue to view the collected results using one of the methods described below.
If you want to analyze an MPI application or an application with specific limitations, such as collecting floating-point/integer operations and trip counts data for certain application parts only with collection control APIs you should run the Survey and Characterization commands separately. The shortcut command does not support such applications. See Run
GPU Roofline Insights
Perspective from Command Line
for details.

View GPU Roofline Results

Intel Advisor
stores the results of analyses with analysis configurations in the
directory specified with
option. You can visualize the collected results in several output formats.
View Results in CLI
After you run the command, the result summary is printed to the terminal. It contains a summary of metrics for the whole application and for its CPU and GPU parts. The information about top GPU hotspots is displayed as a table with execution time, number of operations per second, number of calls, and execution units-related metrics for each GPU hotspot.
To see metrics for
GPU hotspots, run the following
command with
advisor --report=survey --gpu --project-dir=./adv_gpu_roofline
To view more data columns, add
advisor --report=survey --gpu --show-all-columns --project-dir=./adv_gpu_roofline
Export Results as an Interactive HTML Report
Generate an interactive HTML report that you can share and view in your web browser:
advisor --report=all --project-dir=./adv_gpu_roofline --report-output=./gpu_roofline_report.html
This command creates an interactive HTML report that has the similar structure of results as GUI. The interactive HTML report contains GPU metrics presented in the grid view and plotted on a Roofline chart. The Roofline chart displays data for floating-point operations, integer operations, and all memory levels that are available in
View Results in GUI
The easiest way to view results is to open them on the same machine where they were collected if it has
Intel Advisor
GUI installed. In this case, you open an existing
Intel Advisor
result without creating any additional files or reports.
To open a result in the GUI, run the following command:
advisor-gui ./adv_gpu_roofline
If the report does not open, click
Show Result
on the Welcome page or just open
Intel Advisor
GUI and select the project in
File > Open > Project
Save a Read-Only Result Snapshot (Instead of HTML and GUI Reports)
If you do not have
Intel Advisor
GUI installed on the target machine, copy your results to a shared drive and open it on another machine or copy the results directly to the client machine.
Use read-only snapshots for decreasing the size of copied files.
To create a snapshot and pack it into an archive including sources and binaries, run the following command:
advisor --snapshot --project-dir=./adv_gpu_roofline --pack --cache-sources --cache-binaries -- ./my_snapshot
The snapshot archive named
is saved into the directory specified by the
option. lt is stored in the
To open the result snapshot in
Intel Advisor
GUI, run the following command:
advisor-gui ./my_snapshot
You can open a snapshot from
Intel Advisor
GUI by selecting the snapshot in the
File > Open > Result
Intel Advisor
for macOS*
to view the collected results in the GUI.
For more information, see Snapshot.

Examine the Application Performance on GPU

By default,
Intel Advisor
displays the
tab. It shows the general metrics of the whole application, of its GPU and CPU parts. You can also examine the preview Roofline charts for your application.
To get a more detailed per-kernel view, click the compute task of interest in the
Top Hotspots
pane or click the
GPU Roofline Regions
tab. This tab contains the GPU Roofline chart on the left side and a detailed overview of a selected kernel with its GPU Source and Assembly views on the right side. In the bottom of GPU Roofline Regions tab, there is a
pane containing a grid view with the list of the kernels and related raw collected data (including memory-related data, EU active/stalled/idle data, EU thread occupancy, number of threads). For more information, see Examine Bottlenecks on GPU Roofline Chart.
Right-click the compute task of interest in the
table and select
View Source
or click the
Source View
tab. It shows the matching of sources with assembly code.

Explore Detailed GPU Metrics with Intel Advisor Python* API

To visualize the already collected GPU Roofline results in CLI, use Python scripts from
. The scripts use
Intel Advisor
Python API to print raw metrics that may be post processed via user custom scripts. Refer to the examples below.
is the default
Intel Advisor
installation directory. Replace it with your installation directory if you installed the
Intel Advisor
to a different location.
Print GPU Roof Values
Run the
script sample to check the values of GPU roofs measured during GPU Roofline collection.
advisor-python /opt/intel/oneapi/advisor/latest/pythonapi/examples/ ./adv_gpu_roofline
A list of GPU roofs is printed to the terminal similar to the following:
DP Vector FMA Peak 219 GFLOPS DP Vector Add Peak 110 GFLOPS SP Vector FMA Peak 872 GFLOPS SP Vector Add Peak 439 GFLOPS Int64 Vector Add Peak 110 GFLOPS Int32 Vector Add Peak 438 GFLOPS Int16 Vector Add Peak 873 GFLOPS Int8 Vector Add Peak 432 GFLOPS SLM Bandwidth 404 GB/s L3 Bandwidth 346 GB/s DRAM Bandwidth 32 GB/s GTI Bandwidth 76 GB/s
View Detailed Per-Kernel Metrics
Run the
script sample to view the detailed list of metrics for each kernel from the pre-collected GPU profile, such as detailed kernel instruction mix.
advisor-python /opt/intel/oneapi/advisor/latest/pythonapi/examples/ ./adv_gpu_roofline
A list of GPU metrics is printed to the terminal similar to the following:
============================================================ Main GPU Dataset ============================================================ … ============================================================ … carm_l3_cache_line_utilization_______________: 1 carm_slm_cache_line_utilization______________: 0 carm_traffic_gb______________________________: 0.105906 computing_task_______________________________: MandelParallel::Evaluate(cl::sycl::queue&)::{lambda(cl::sycl::handler&)@235:14}::operator()(cl::sycl::handler&) const::{lambda()@240:44} computing_task_average_time__________________: 0.000498183 … elapsed_time_________________________________: 0.0503165 … gpu_compute_performance_fp_ai________________: 852.558 gpu_compute_performance_gflop________________: 11.7716 gpu_compute_performance_gflops_______________: 233.951 gpu_compute_performance_gintop_______________: 0.617538 gpu_compute_performance_gintops______________: 12.2731 gpu_compute_performance_gmixop_______________: 12.3891 gpu_compute_performance_gmixops______________: 246.224 gpu_compute_performance_int_ai_______________: 44.7253 gpu_compute_performance_mix_ai_______________: 897.283 gpu_memory_bandwidth_gb_sec__________________: 0.27441 gpu_memory_bandwidth_gb_sec_read_____________: 0.233971 gpu_memory_bandwidth_gb_sec_write____________: 0.0404391 gpu_memory_data_transferred_gb_______________: 0.0138074 gpu_memory_data_transferred_gb_read__________: 0.0117726 gpu_memory_data_transferred_gb_write_________: 0.00203475 … work_size_global_____________________________: 512 x 512 work_size_local______________________________: 256 x 1 ============================================================ Instruction Mix Dataset ============================================================ zeCommandListAppendMemoryCopyRegion: 0 ============================================================ zeCommandListAppendBarrier: 2 ============================================================ MandelParallel::Evaluate(cl::sycl::queue&)::{lambda(cl::sycl::handler&)@235:14}::operator()(cl::sycl::handler&) const::{lambda()@240:44}: 1 Type: Size: Op Type : Callcount : Exec Count : Dynamic Count INT : 32 : MOVE : 104,403,397 : 1,488,428,112 : 1,351,879,647 INT : 32 : BIT : 3,309,568 : 28,131,328 : 28,131,328 INT : 32 : BASIC : 190,604,170 : 509,977,482 : 509,977,482 : : OTHER : 165,063,391 : 2,641,014,256 : 2,505,733,341 : : CONTROL : 335,418,273 : 4,110,086,021 : 2,400,317,722 FP : 32 : MOVE : 13,238,272 : 112,525,312 : 112,525,312 FP : 32 : MATH : 3,309,568 : 3,309,568 : 3,309,568 FP : 32 : BASIC : 165,999,156 : 2,655,986,496 : 2,385,424,666 FP : 32 : FMA : 327,033,960 : 5,232,543,360 : 4,691,419,700 INT : 64 : BASIC : 3,309,568 : 26,476,544 : 26,476,544 INT : 32 : FMA : 3,309,568 : 26,476,544 : 26,476,544 INT : 16 : STORE : 1,654,784 : 26,476,544 : 26,476,544 ============================================================

Alternative Steps

GPU Roofline Insights
Perspective on a Multi-GPU Systems (Instead of Default Configuration)
If your system has more than one GPU device (for example, an integrated GPU and a discrete GPU, or several discrete GPU devices), specify a target GPU to collect profiling data:
  1. Get the list of GPU devices available on your system:
    advisor --help target-gpu
    The output shows the device configuration in the following format:
  2. Copy the device configuration that you want to analyze. For example,
  3. Type the following command to the terminal with the
    option and provide the copied device configuration as its argument to select the GPU of interest:
    advisor --collect=roofline --profile-gpu --target-gpu=0:0:2.0 --project-dir=./adv_gpu_roofline -- ./src/mandelbrot
  4. Run the command.
GPU Roofline Insights
Perspective for Kernels with Small Execution Time (Instead of Default Configuration)
Accuracy decreases with decreasing the ratio of kernel time to sampling interval. To achieve the best accuracy, ensure that
To avoid inaccurate metrics for
kernels with small execution time
, use
option to decrease the interval (in milliseconds) between GPU samples:
advisor --collect=roofline --profile-gpu --gpu-sampling-interval=0.1 --project-dir=./adv_gpu_roofline -- ./src/mandelbrot

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at