Scenario

Collect application characterization metrics on CPU: run the Survey analysis, the Trip Counts and FLOP analysis, and optionally, the Dependencies analysis.
Based on the metrics collected, estimate application execution time on a graphics processing unit (GPU) using an analytical model.

Ingredients

Performance analysis tools
:
Intel® Advisor
2021
Available for download at https://software.intel.com/content/www/us/en/develop/articles/oneapi-standalone-components.html as a standalone and at https://software.intel.com/content/www/us/en/develop/tools/oneapi/base-toolkit/download.html as part of the Intel® oneAPI Base Toolkit.
Application
: Mandelbrot is an application that generates a fractal image by matrix initialization and performs pixel-independent computations.
There are two implementations available for download:
A native C++ implementation, which you can analyze with
Offload Modeling
A
DPC++
implementation, which you can run on a GPU and compare its performance with the
Intel Advisor
predictions
Select a device to run the application on by setting the
SYCL_DEVICE_TYPE=
<CPU|GPU|FPGA|HOST>
environment variable.
Compiler
:
Intel® C++ Compiler Classic
2021 and
Intel® oneAPI
DPC++/C++
Compiler
2021
Available for download as part of the Intel® oneAPI HPC Toolkit at https://software.intel.com/content/www/us/en/develop/tools/oneapi/hpc-toolkit/download.html.
Operating system
: Microsoft Windows* 10 Enterprise
CPU
: Intel® Core™ i7-8665U processor
GPU
: Intel® UHD Graphics 620 (Gen9 GT2 architecture configuration)

Prerequisites

<oneapi-install-dir>\setvars.bat

Compile the C++ Mandelbrot Sample

icx.exe /Qm64 /Zi /nologo /W3 /O2 /Ob1 /Oi /D NDEBUG /D _CONSOLE /D _UNICODE /D UNICODE /EHsc /MD /GS /Gy /Zc:forScope /Fe"mandelbrot_base.exe" /TP src\main.cpp src\mandelbrot.cpp src\timer.cpp

Run

Offload Modeling

without Dependencies Analysis

Run Survey analysis to get baseline performance data:
advisor --collect=survey --stackwalk-mode=online --static-instruction-mix --project-dir=.\advisor_results --search-dir sym=.\x64\Release --search-dir bin=.\x64\Release --search-dir src=. -- .\x64\Release\mandelbrot_base.exe
Run Trip Counts and FLOP analysis to get call count data and model cache for the Gen9 GT2 configuration:
advisor --collect=tripcounts --flop --stacks --enable-cache-simulation --data-transfer=light --target-device=gen9_gt2 --project-dir=.\advisor_results --search-dir sym=.\x64\Release --search-dir bin=.\x64\Release --search-dir src=. -- .\x64\Release\mandelbrot_base.exe
Model application performance on the GPU with the Gen9 GT2 configuration ignoring assumed dependencies:
advisor --collect=projection --config=gen9_gt2 --no-assume-dependencies --project-dir=.\advisor_results
The
--no-assume-dependencies
option allows to minimize the estimated time and assumes a loop is parallel without dependencies.

View Estimated Performance Results

Run the following from the command prompt to open the
Intel Advisor
:
advisor-gui
Go to
File
Open
Project...
, navigate to the
advisor_results
project directory where you stored results, and open the
.advixeproj
project file.
If the
Offload Modeling
report does not open, click
Show Result
on the Welcome pane.
The
Summary
results collected for the
advisor_results
project should open.

In the
Top Metrics
and
Program Metrics
panes, review per-program performance estimations and comparison with the baseline application performance on CPU.
In the
Offload Bounded By
pane, review characterization metrics and factors that limit the performance of regions in relation to their execution time.
In the
Top Offloaded
pane, review top five regions recommended for offloading to the selected target device.
In the
Top Non-Offloaded
pane, review top five non-offloaded regions and the reasons why they are not recommended to be run on the target.

The estimated execution time on the target GPU is 0.05 s.
The loop at
mandelbrot.cpp:56
is recommended to be offloaded to the GPU.
Its execution time is 81% of the total execution time of the whole application (see
Fraction of Accelerated Code
).
If this loop runs on the target GPU, it is executed 13.9 times faster than on the CPU (see
Speed Up for Accelerated Code
).
If this loop is offloaded, the whole application runs 4.1 times faster, according to the Amdahl’s Law (see
Amdahl’s Law Speed Up
).
The loop at
stb_image_write.h:885
is not profitable for offloading because the overhead for offloading it to the GPU is high.

Click the
Accelerated Regions
tab at the top of the report.
Click a loop/function name hyperlink in the
Top Offloaded
or in
Top Non-Offloaded
pane.

The
CPU+GPU
table shows the result of modeling execution of each code region on the GPU: it reports performance metrics measured on the baseline CPU platform and metrics estimated for application performance modeled on the target GPU, such as expected execution time and what a bottleneck is (for example, if a code region is compute or memory bound). You can expand data columns and scroll the grid to see more projected metrics.
For the Mandelbrot application, the loop at
mandelbrot.cpp:56
is recommended for offloading to the GPU. It is compute bound and its estimated execution time on the Gen9 GT2 GPU is 12.3 ms. This region transfers 2.1 MB of data, mostly from GPU to CPU (write data transfers), but it does not add overhead since the GPU is integrated.

Click the
mandelbrot.cpp:56
code regions in the
CPU+GPU
table to see its source code in the
Source
view with several offload parameters.

Switch to the
Top-Down
tab to locate the
mandelbrot.cpp:56
region in the application call tree. Use this pane to review the loop metrics together with its callstack.

Run

Offload Modeling

with Dependencies Analysis

In the

CPU+GPU

table, expand the loop at

mandelbrot.cpp:56

to see its child loops.

Expand the

Measured

column group.

The

Dependency Type

column reports

Parallel: Assumed

for the

mandelbrot.cpp:56

and its child loops. This means that

Intel Advisor

marks these loops as parallel because you used the

--no-assume-dependencies

option for the performance modeling, but it

does not

have information about their actual dependency type.



If you are sure that loops in your application are parallel, you can skip the Dependencies analysis. Such loops should have a

Parallel:

<reason>

value in the

Dependency Type

column, where

<value>

is

Explicit

,

Proven

,

Programming Model

, or

Workload

.

To check if the loops have real dependencies, run the Dependencies analysis.

To minimize the Dependencies analysis overhead, select the loops with the

Parallel: Assumed

value to check their dependency type, for example, using loop IDs. Run the following command to get IDs of those loops:

advisor --report=survey --project-dir=.\advisor_results -- .\x64\Release\mandelbrot_base.exe

This command prints the Survey analysis results with loop IDs to the command prompt. The

mandelbrot.cpp:57

and

mandelbrot.cpp:56

loops have IDs 2 and 3.



Run the Dependencies analysis with the

--mark-up-list=2,3

option to analyze only the loops of interest:

advisor --collect=dependencies --mark-up-list=2,3 --loop-call-count-limit=16 --filter-reductions --project-dir=.\advisor_results -- .\x64\Release\mandelbrot_base.exe

Rerun the performance modeling to get the refined performance estimation:

advisor --collect=projection --config=gen9_gt2 --project-dir=.\advisor_results

Open the

advisor_results

projects with refined results in the GUI:

advisor-gui .\advisor_results

Rewrite the Code in Data Parallel C++

Selecting a device
Declaring a device queue
Declaring a data buffer
Submitting a job to the device queue
Executing the calculation in parallel

using namespace sycl;
// Create a queue on the default device. Set SYCL_DEVICE_TYPE environment
// variable to (CPU|GPU|FPGA|HOST) to change the device
queue q(default_selector{}, dpc_common::exception_handler);
// Declare data buffer
buffer data_buf(data(), range(rows, cols));
// Submit a command group to the queue
q.submit([&](handler &h) {
// Get access to the buffer
auto b = data_buf.get_access(h,write_only);
// Iterate over image and write to data buffer
h.parallel_for(range<2>(rows, cols), [=](auto index) {
…
b[index] = p.Point(c);
});
    });

constexpr int row_size = 2048;
constexpr int col_size = 1024;

Compare Estimations and Real Performance on GPU

Compile the
DPC++
Mandelbrot sample as follows:
dpcpp.exe /W3 /O2 /nologo /D _UNICODE /D UNICODE /Zi /WX- /EHsc /MD /I"$(ONEAPI_ROOT)\dev-utilities\latest\include" /Fe"mandelbrot_dpcpp.exe" src\main.cpp
Run the compiled
mandelbrot_dpcpp
application:
mandelbrot_dpcpp.exe
Review the application output printed to the command prompt. It reports application execution time:
Parallel time: 0.0121385s
Mandelbrot calculation in the offloaded loop takes
12.1 ms
on the GPU. This is close to the
12.3 ms
execution time predicted by the
Intel Advisor
.

Key Take-Aways

Offload Modeling feature of the Intel Advisor can help you to design and prepare your application for executing on a GPU before you have the hardware. It can model your application performance on a selected GPU, calculate potential speedup and execution time, identify offload opportunities, and locate potential bottlenecks on the target hardware.
Loops with dependencies cannot be executed in parallel and offloaded to a target GPU. When modeling application performance with the Intel Advisor, you can select to ignore potential dependencies or consider all potential dependencies. You can run the Dependencies analysis to get more accurate modeling results if your application might not be effectively vectorized or parallelized.

See Also

User Guide:
Offload Modeling
Perspective
oneAPI GPU Optimization Guide