docs/user_guide/how_to_build_and_run_examples.dox

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namespace arm_compute
{
/** @page how_to_build How to Build and Run Examples

@tableofcontents

@section S1_1_build_options Build options

scons 2.3 or above is required to build the library.
To see the build options available simply run ```scons -h```

@section S1_2_linux Building for Linux

@subsection S1_2_1_library How to build the library ?

For Linux, the library was successfully built and tested using the following Linaro GCC toolchain:

 - gcc-linaro-6.3.1-2017.05-x86_64_arm-linux-gnueabihf
 - gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu

To cross-compile the library in debug mode, with Arm® Neon™ only support, for Linux 32bit:

	scons Werror=1 -j8 debug=1 neon=1 opencl=0 os=linux arch=armv7a

To cross-compile the library in asserts mode, with OpenCL only support, for Linux 64bit:

	scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=1 embed_kernels=1 os=linux arch=armv8a

You can also compile the library natively on an Arm device by using <b>build=native</b>:

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv8a build=native
	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a build=native

@note g++ for Arm is mono-arch, therefore if you want to compile for Linux 32bit on a Linux 64bit platform you will have to use a cross compiler.

For example on a 64bit Debian based system you would have to install <b>g++-arm-linux-gnueabihf</b>

	apt-get install g++-arm-linux-gnueabihf

Then run

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a build=cross_compile

or simply remove the build parameter as build=cross_compile is the default value:

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=linux arch=armv7a

@subsection S1_2_2_examples How to manually build the examples ?

The examples get automatically built by scons as part of the build process of the library described above. This section just describes how you can build and link your own application against our library.

@note The following command lines assume the arm_compute libraries are present in the current directory or in the system library path. If this is not the case you can specify the location of the pre-built libraries with the compiler option -L. When building the OpenCL example the commands below assume that the CL headers are located in the include folder where the command is executed.

To cross compile a Arm® Neon™ example for Linux 32bit:

	arm-linux-gnueabihf-g++ examples/neon_cnn.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -mfpu=neon -L. -larm_compute -o neon_cnn

To cross compile a Arm® Neon™ example for Linux 64bit:

	aarch64-linux-gnu-g++ examples/neon_cnn.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -L. -larm_compute -o neon_cnn

(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)

To cross compile an OpenCL example for Linux 32bit:

	arm-linux-gnueabihf-g++ examples/cl_sgemm.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -mfpu=neon -L. -larm_compute -o cl_sgemm -DARM_COMPUTE_CL

To cross compile an OpenCL example for Linux 64bit:

	aarch64-linux-gnu-g++ examples/cl_sgemm.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -L. -larm_compute -o cl_sgemm -DARM_COMPUTE_CL

(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)

To cross compile the examples with the Graph API, such as graph_lenet.cpp, you need to link the examples against arm_compute_graph.so too.

i.e. to cross compile the "graph_lenet" example for Linux 32bit:

	arm-linux-gnueabihf-g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp utils/CommonGraphOptions.cpp -I. -Iinclude -std=c++14 -mfpu=neon -L. -larm_compute_graph -larm_compute -Wl,--allow-shlib-undefined -o graph_lenet

i.e. to cross compile the "graph_lenet" example for Linux 64bit:

	aarch64-linux-gnu-g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp utils/CommonGraphOptions.cpp -I. -Iinclude -std=c++14 -L. -larm_compute_graph -larm_compute -Wl,--allow-shlib-undefined -o graph_lenet

(notice the only difference with the 32 bit command is that we don't need the -mfpu option and the compiler's name is different)

@note If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, arm_compute

To compile natively (i.e directly on an Arm device) for Arm® Neon™ for Linux 32bit:

	g++ examples/neon_cnn.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -mfpu=neon -larm_compute -o neon_cnn

To compile natively (i.e directly on an Arm device) for Arm® Neon™ for Linux 64bit:

	g++ examples/neon_cnn.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -larm_compute -o neon_cnn

(notice the only difference with the 32 bit command is that we don't need the -mfpu option)

To compile natively (i.e directly on an Arm device) for OpenCL for Linux 32bit or Linux 64bit:

	g++ examples/cl_sgemm.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -larm_compute -o cl_sgemm -DARM_COMPUTE_CL

To compile natively the examples with the Graph API, such as graph_lenet.cpp, you need to link the examples against arm_compute_graph.so too.

i.e. to natively compile the "graph_lenet" example for Linux 32bit:

	g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp utils/CommonGraphOptions.cpp -I. -Iinclude -std=c++14 -mfpu=neon -L. -larm_compute_graph -larm_compute -Wl,--allow-shlib-undefined -o graph_lenet

i.e. to natively compile the "graph_lenet" example for Linux 64bit:

	g++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp utils/CommonGraphOptions.cpp -I. -Iinclude -std=c++14 -L. -larm_compute_graph -larm_compute -Wl,--allow-shlib-undefined -o graph_lenet

(notice the only difference with the 32 bit command is that we don't need the -mfpu option)

@note If compiling using static libraries, this order must be followed when linking: arm_compute_graph_static, arm_compute

@note These two commands assume libarm_compute.so is available in your library path, if not add the path to it using -L (e.g. -Llib/linux-armv8a-neon-cl-asserts/)
@note You might need to export the path to OpenCL library as well in your LD_LIBRARY_PATH if Compute Library was built with OpenCL enabled.

To run the built executable simply run:

	LD_LIBRARY_PATH=build ./neon_cnn

or

	LD_LIBRARY_PATH=build ./cl_sgemm

@note Examples accept different types of arguments, to find out what they are run the example with \a --help as an argument. If no arguments are specified then random values will be used to execute the graph.

For example:

	LD_LIBRARY_PATH=. ./graph_lenet --help

Below is a list of the common parameters among the graph examples :
@snippet utils/CommonGraphOptions.h Common graph examples parameters

@subsection S1_2_3_sve Build for SVE or SVE2

In order to build for SVE or SVE2 you need a compiler that supports them. You can find more information in the following these links:
    -# GCC: https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/sve-support
    -# LLVM: https://developer.arm.com/tools-and-software/open-source-software/developer-tools/llvm-toolchain/sve-support

@note You the need to indicate the toolchains using the scons "toolchain_prefix" parameter.

An example build command with SVE is:

        scons arch=armv8.2-a-sve os=linux build_dir=arm64 -j55 standalone=0 opencl=0 openmp=0 validation_tests=1 neon=1 cppthreads=1 toolchain_prefix=aarch64-none-linux-gnu-

@subsection S1_2_4_sme Build for SME2

In order to build for SME2 you need to use a compiler that supports SVE2 and enable SVE2 in the build as well.

@note You the need to indicate the toolchains using the scons "toolchain_prefix" parameter.

An example build command with SME2 is:

        scons arch=armv8.6-a-sve2-sme2 os=linux build_dir=arm64 -j55 standalone=0 opencl=0 openmp=0 validation_tests=1 neon=1 cppthreads=1 toolchain_prefix=aarch64-none-linux-gnu-

@subsection S1_2_5_clang_build_linux Building with LLVM+Clang Natively on Linux

The library can be built with LLVM+Clang by specifying CC and CXX environment variables appropriately as below. The **minimum** supported clang version is 11, as LLVM 11 introduces SVE/SVE2 VLA intrinsics: https://developer.arm.com/Tools%20and%20Software/LLVM%20Toolchain#Supported-Devices.

	CC=clang CXX=clang++ <build command>

Or, if the environment has multiple clang versions:

	CC=clang-16 CXX=clang++-16

Examples for different build tools look like below.

(experimental) CMake:

	mkdir build
	cd build
	CC=clang CXX=clang++ cmake .. -DCMAKE_BUILD_TYPE=Release -DARM_COMPUTE_OPENMP=1 -DARM_COMPUTE_WERROR=0 -DARM_COMPUTE_BUILD_EXAMPLES=1 -DARM_COMPUTE_BUILD_TESTING=1 -DCMAKE_INSTALL_LIBDIR=.
	CC=clang CXX=clang++ cmake --build . -j32

(experimental) Bazel:

	CC=clang CXX=clang++ bazel build //...

Scons:

	CC=clang CXX=clang++ scons -j32 Werror=1 debug=0 neon=1 openmp=1 cppthreads=1 os=linux arch=armv8a multi_isa=1 build=native validation_tests=1

Configurations supported are limited to the configurations supported by our CMake, Bazel and Multi ISA Scons builds. For more details on CMake and Bazel builds, please see @ref S1_8_experimental_builds

@section S1_3_android Building for Android

For Android, the library was successfully built and tested using Google's standalone toolchains:
 - clang++ from NDK r20b for armv8a
 - clang++ from NDK r20b for armv8.2-a with FP16 support

(From 23.02, NDK >= r20b is highly recommended) For NDK r18 or older, here is a guide to <a href="https://developer.android.com/ndk/guides/standalone_toolchain.html">create your Android standalone toolchains from the NDK</a>:
- Download the NDK r18b from here: https://developer.android.com/ndk/downloads/index.html to directory $NDK
- Make sure you have Python 2.7 installed on your machine.
- Generate the 32 and/or 64 toolchains by running the following commands to your toolchain directory $MY_TOOLCHAINS:

	$NDK/build/tools/make_standalone_toolchain.py --arch arm64 --install-dir $MY_TOOLCHAINS/aarch64-linux-android-ndk-r18b --stl libc++ --api 21

	$NDK/build/tools/make_standalone_toolchain.py --arch arm --install-dir $MY_TOOLCHAINS/arm-linux-android-ndk-r18b --stl libc++ --api 21

For NDK r19 or newer, you can directly <a href="https://developer.android.com/ndk/downloads">Download</a> the NDK package for your development platform, without the need to launch the make_standalone_toolchain.py script. You can find all the prebuilt binaries inside $NDK/toolchains/llvm/prebuilt/$OS_ARCH/bin/.

@parblock
@attention The building script will look for a binary named "aarch64-linux-android-clang++", while the prebuilt binaries will have their API version as a suffix to their filename (e.g. "aarch64-linux-android21-clang++"). You can instruct scons to use the correct version by using a combination of the toolchain_prefix and the "CC" "CXX" environment variables.
@attention For this particular example, you can specify:

	CC=clang CXX=clang++ scons toolchain_prefix=aarch64-linux-android21-

@attention or:

	CC=aarch64-linux-android21-clang CXX=aarch64-linux-android21-clang++ scons toolchain_prefix=""

@endparblock

@parblock
@attention We used to use gnustl but as of NDK r17 it is deprecated so we switched to libc++
@endparblock

@note Make sure to add the toolchains to your PATH:

	export PATH=$PATH:$MY_TOOLCHAINS/aarch64-linux-android-ndk-r18b/bin:$MY_TOOLCHAINS/arm-linux-android-ndk-r18b/bin

@subsection S1_3_1_library How to build the library ?

To cross-compile the library in debug mode, with Arm® Neon™ only support, for Android 32bit:

	CXX=clang++ CC=clang scons Werror=1 -j8 debug=1 neon=1 opencl=0 os=android arch=armv7a

To cross-compile the library in asserts mode, with OpenCL only support, for Android 64bit:

	CXX=clang++ CC=clang scons Werror=1 -j8 debug=0 asserts=1 neon=0 opencl=1 embed_kernels=1 os=android arch=armv8a

@subsection S1_3_2_examples How to manually build the examples ?

The examples get automatically built by scons as part of the build process of the library described above. This section just describes how you can build and link your own application against our library.

@note The following command lines assume the arm_compute libraries are present in the current directory or in the system library path. If this is not the case you can specify the location of the pre-built libraries with the compiler option -L. When building the OpenCL example the commands below assume that the CL headers are located in the include folder where the command is executed.

Once you've got your Android standalone toolchain built and added to your path you can do the following:

To cross compile a Arm® Neon™ example:

	#32 bit:
	arm-linux-androideabi-clang++ examples/neon_cnn.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -larm_compute-static -L. -o neon_cnn_arm -static-libstdc++ -pie
	#64 bit:
	aarch64-linux-android-clang++ examples/neon_cnn.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -larm_compute-static -L. -o neon_cnn_aarch64 -static-libstdc++ -pie

To cross compile an OpenCL example:

	#32 bit:
	arm-linux-androideabi-clang++ examples/cl_sgemm.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -larm_compute-static -L. -o cl_sgemm_arm -static-libstdc++ -pie -DARM_COMPUTE_CL
	#64 bit:
	aarch64-linux-android-clang++ examples/cl_sgemm.cpp utils/Utils.cpp -I. -Iinclude -std=c++14 -larm_compute-static -L. -o cl_sgemm_aarch64 -static-libstdc++ -pie -DARM_COMPUTE_CL

To cross compile the examples with the Graph API, such as graph_lenet.cpp, you need to link the library arm_compute_graph also.

	#32 bit:
	arm-linux-androideabi-clang++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp utils/CommonGraphOptions.cpp -I. -Iinclude -std=c++14 -Wl,--whole-archive -larm_compute_graph-static -Wl,--no-whole-archive -larm_compute-static -L. -o graph_lenet_arm -static-libstdc++ -pie -DARM_COMPUTE_CL
	#64 bit:
	aarch64-linux-android-clang++ examples/graph_lenet.cpp utils/Utils.cpp utils/GraphUtils.cpp utils/CommonGraphOptions.cpp -I. -Iinclude -std=c++14 -Wl,--whole-archive -larm_compute_graph-static -Wl,--no-whole-archive -larm_compute-static -L. -o graph_lenet_aarch64 -static-libstdc++ -pie -DARM_COMPUTE_CL

@note Due to some issues in older versions of the Arm® Mali™ OpenCL DDK (<= r13p0), we recommend to link arm_compute statically on Android.
@note When linked statically the arm_compute_graph library currently needs the --whole-archive linker flag in order to work properly

Then you need to do is upload the executable and the shared library to the device using ADB:

	adb push neon_cnn_arm /data/local/tmp/
	adb push cl_sgemm_arm /data/local/tmp/
	adb push gc_absdiff_arm /data/local/tmp/
	adb shell chmod 777 -R /data/local/tmp/

And finally to run the example:

	adb shell /data/local/tmp/neon_cnn_arm
	adb shell /data/local/tmp/cl_sgemm_arm
	adb shell /data/local/tmp/gc_absdiff_arm

For 64bit:

	adb push neon_cnn_aarch64 /data/local/tmp/
	adb push cl_sgemm_aarch64 /data/local/tmp/
	adb push gc_absdiff_aarch64 /data/local/tmp/
	adb shell chmod 777 -R /data/local/tmp/

And finally to run the example:

	adb shell /data/local/tmp/neon_cnn_aarch64
	adb shell /data/local/tmp/cl_sgemm_aarch64
	adb shell /data/local/tmp/gc_absdiff_aarch64

@note Examples accept different types of arguments, to find out what they are run the example with \a --help as an argument. If no arguments are specified then random values will be used to execute the graph.

For example:
	adb shell /data/local/tmp/graph_lenet --help

In this case the first argument of LeNet (like all the graph examples) is the target (i.e 0 to run on Neon™, 1 to run on OpenCL if available, 2 to run on OpenCL using the CLTuner), the second argument is the path to the folder containing the npy files for the weights and finally the third argument is the number of batches to run.

@section S1_4_macos Building for macOS

The library was successfully natively built for Apple Silicon under macOS 11.1 using clang v12.0.0.

To natively compile the library with accelerated CPU support:

	scons Werror=1 -j8 neon=1 opencl=0 os=macos arch=armv8a build=native

@note Initial support disables feature discovery through HWCAPS and thread scheduling affinity controls

@section S1_5_bare_metal Building for bare metal

For bare metal, the library was successfully built using linaro's latest (gcc-linaro-6.3.1-2017.05) bare metal toolchains:
 - arm-eabi for armv7a
 - aarch64-elf for armv8a

Download linaro for <a href="https://releases.linaro.org/components/toolchain/binaries/6.3-2017.05/arm-eabi/">armv7a</a> and <a href="https://releases.linaro.org/components/toolchain/binaries/6.3-2017.05/aarch64-elf/">armv8a</a>.

@note Make sure to add the toolchains to your PATH: export PATH=$PATH:$MY_TOOLCHAINS/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-elf/bin:$MY_TOOLCHAINS/gcc-linaro-6.3.1-2017.05-x86_64_arm-eabi/bin

@subsection S1_5_1_library How to build the library ?

To cross-compile the library with Arm® Neon™ support for baremetal armv8a:

	scons Werror=1 -j8 debug=0 neon=1 opencl=0 os=bare_metal arch=armv8a build=cross_compile cppthreads=0 openmp=0 standalone=1

@subsection S1_5_2_examples How to manually build the examples ?

Examples are disabled when building for bare metal. If you want to build the examples you need to provide a custom bootcode depending on the target architecture and link against the compute library. More information about bare metal bootcode can be found <a href="http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.dai0527a/index.html">here</a>.

@section S1_6_windows_host Building on a Windows® host system (cross-compile)

Using `scons` directly from the Windows® command line is known to cause
problems. The reason seems to be that if `scons` is setup for cross-compilation
it gets confused about Windows® style paths (using backslashes). Thus it is
recommended to follow one of the options outlined below.

@subsection S1_6_1_ubuntu_on_windows Bash on Ubuntu on Windows® (cross-compile)

The best and easiest option is to use
<a href="https://msdn.microsoft.com/en-gb/commandline/wsl/about">Ubuntu on Windows®</a>.
This feature is still marked as *beta* and thus might not be available.
However, if it is building the library is as simple as opening a *Bash on
Ubuntu on Windows®* shell and following the general guidelines given above.

@subsection S1_6_2_cygwin Cygwin (cross-compile)

If the Windows® subsystem for Linux is not available <a href="https://www.cygwin.com/">Cygwin</a>
can be used to install and run `scons`, the minimum Cygwin version must be 3.0.7 or later. In addition
to the default packages installed by Cygwin `scons` has to be selected in the installer. (`git` might
also be useful but is not strictly required if you already have got the source
code of the library.) Linaro provides pre-built versions of
<a href="http://releases.linaro.org/components/toolchain/binaries/">GCC cross-compilers</a>
that can be used from the Cygwin terminal. When building for Android the
compiler is included in the Android standalone toolchain. After everything has
been set up in the Cygwin terminal the general guide on building the library
can be followed.

@subsection S1_6_3_WoA Windows® on Arm™ (native build)

    Native builds on Windows® are experimental and some features from the library interacting with the OS are missing.

It's possible to build Compute Library natively on a Windows® system running on Arm™.

Windows® on Arm™ (WoA) systems provide compatibility emulating x86 binaries on aarch64. Unfortunately Visual Studio 2022 does not work on aarch64 systems because it's an x86_64bit application and these binaries cannot be exectuted on WoA yet.

Because we cannot use Visual Studio to build Compute Library we have to set up a native standalone toolchain to compile C++ code for arm64 on Windows®.

Native arm64 toolchain installation for WoA:
- LLVM+Clang-12 which can be downloaded from: https://github.com/llvm/llvm-project/releases/download/llvmorg-12.0.0/LLVM-12.0.0-woa64.exe
- Arm64 VC Runtime which can be downloaded from  https://aka.ms/vs/17/release/vc_redist.arm64.exe

- While full VS22 cannot be installed on WoA, we can install some components
    -# Desktop development with C++ and all Arm64 components for Visual Studio, refer to:  https://developer.arm.com/documentation/102528/0100/Install-Visual-Studio
    -# VS22 build tools: https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2022

There are some additional tools we need to install to build Compute Library:

- git https://git-scm.com/download/win
- python 3 https://www.python.org/downloads/windows/
- scons can be installed with pip install scons

In order to use clang to build Windows® binaries natively we have to initialize the environment variables from VS22 correctly so that the compiler could find the arm64 C++ libraries. This can be done by pressing the key windows + r  and running the command:

    cmd /k "C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\VC\Auxiliary\Build\vcvarsx86_arm64.bat"

To build Compute Library type:

     scons opencl=0 neon=1 os=windows examples=0 validation_tests=1 benchmark_examples=0 build=native arch=armv8a Werror=0 exceptions=1 standalone=1

@section S1_7_cl_requirements OpenCL DDK Requirements

@subsection S1_7_1_cl_hard_requirements Hard Requirements

Compute Library requires OpenCL 1.1 and above with support of non uniform workgroup sizes, which is officially supported in the Arm® Mali™ OpenCL DDK r8p0 and above as an extension (respective extension flag is \a -cl-arm-non-uniform-work-group-size).

Enabling 16-bit floating point calculations require \a cl_khr_fp16 extension to be supported. All Arm® Mali™ GPUs with compute capabilities have native support for half precision floating points.

@subsection S1_7_2_cl_performance_requirements Performance improvements

Integer dot product built-in function extensions (and therefore optimized kernels) are available with Arm® Mali™ OpenCL DDK r22p0 and above for the following GPUs : G71, G76. The relevant extensions are \a cl_arm_integer_dot_product_int8, \a cl_arm_integer_dot_product_accumulate_int8 and \a cl_arm_integer_dot_product_accumulate_int16.

OpenCL kernel level debugging can be simplified with the use of printf, this requires the \a cl_arm_printf extension to be supported.

SVM allocations are supported for all the underlying allocations in Compute Library. To enable this OpenCL 2.0 and above is a requirement.

@section S1_8_experimental_builds Experimental Bazel and CMake builds

In addition to the scons build the repository includes experimental Bazel and CMake builds.
These builds currently support a limited range of options. Both are similar to the scons multi_isa build. It compiles all libraries with Neon (TM) support, as well as SVE and SVE2 libraries. The build is CPU only, not including OpenCL support. Only Linux environment is targeted for now. Both were successfully built with gcc / g++ version 10.2.

@subsection S1_8_1_bazel_build Bazel build

@subsubsection S1_8_1_1_file_structure File structure

File structure for all files included in the Bazel build:

	.
	├──  .bazelrc
	├──  BUILD
	├──  WORKSPACE
	├── arm_compute
	│   └── BUILD
	├── examples
	│   └── BUILD
	├── include
	│   └── BUILD
	├── scripts
	│   ├── print_version_file.py
	│   └── BUILD
	├── src
	│   └── BUILD
	├── support
	│   └── BUILD
	├── tests
	│   ├── BUILD
	│   └── framework
	│       └── BUILD
	└── utils
		└── BUILD

@subsubsection S1_8_1_2_build_options Build options

Available build options:

	- debug: Enable ['-O0','-g','-gdwarf-2'] compilation flags
	- Werror: Enable -Werror compilation flag
	- logging: Enable logging
	- cppthreads: Enable C++11 threads backend
	- openmp: Enable OpenMP backend

@subsubsection S1_8_1_3_example_builds Example builds

Build everything (libraries, examples, tests):

	bazel build //...

Build libraries:

	bazel build //:all

Build arm_compute only:

	bazel build //:arm_compute

Build examples:

	bazel build //examples:all

Build resnet50 example:

	bazel build //examples:graph_resnet50

Build validation and benchmarking:

	bazel build //tests:all

@subsection S1_8_2_cmake_build CMake build

@subsubsection S1_8_2_1_file_structure File structure

File structure for all files included in the CMake build:

	.
	├──  CMakeLists.txt
	├── cmake
	│   ├── Options.cmake
	│   ├── Version.cmake
	│   └── toolchains
	│       └── aarch64_linux_toolchain.cmake
	├── examples
	│   └── CMakeLists.txt
	├── src
	│   └── CMakeLists.txt
	└── tests
		├── CMakeLists.txt
		├── benchmark
		│   └── CMakeLists.txt
		└── validation
			└── CMakeLists.txt

@subsubsection S1_8_2_2_build_options Build options

Available build options:

	- CMAKE_BUILD_TYPE: "Release" (default) enables ['-O3', '-DNDEBUG'] compilation flags, "Debug" enables ['-O0','-g','-gdwarf-2', '-DARM_COMPUTE_ASSERTS_ENABLED']
	- ARM_COMPUTE_WERROR: Enable -Werror compilation flag
	- ARM_COMPUTE_EXCEPTIONS: If disabled ARM_COMPUTE_EXCEPTIONS_DISABLED is enabled
	- ARM_COMPUTE_LOGGING: Enable logging
	- ARM_COMPUTE_BUILD_EXAMPLES: Build examples
	- ARM_COMPUTE_BUILD_TESTING: Build tests
	- ARM_COMPUTE_CPPTHREADS: Enable C++11 threads backend
	- ARM_COMPUTE_OPENMP: Enable OpenMP backend

@subsubsection S1_8_2_3_example_builds Example builds

To build libraries, examples and tests:

	mkdir build
	cd build
	cmake .. -DCMAKE_BUILD_TYPE=Release -DARM_COMPUTE_OPENMP=1 -DARM_COMPUTE_WERROR=0 -DARM_COMPUTE_BUILD_EXAMPLES=1 -DARM_COMPUTE_BUILD_TESTING=1 -DCMAKE_INSTALL_LIBDIR=.
	cmake --build . -j32

@section S1_9_fixed_format Building with support for fixed format kernels

@subsection S1_9_1_intro_to_fixed_format_kernels What are fixed format kernels?

The GEMM kernels used for convolutions and fully-connected layers in Compute Library employ memory layouts optimized for each kernel implementation. This then requires the supplied weights to be re-ordered into a buffer ready for consumption by the GEMM kernel. Where Compute Library is being called from a framework or library which implements operator caching, the re-ordering of the inputted weights into an intermediate buffer may no longer be desirable. When using a cached operator, the caller may wish to re-write the weights tensor, and re-run the operator using the updated weights. With the default GEMM kernels in Compute Library, the GEMM will be executed with the old weights, leading to incorrect results.

To address this, Compute Library provides a set of GEMM kernels which use a common blocked memory format. These kernels consume the input weights directly from the weights buffer and do not execute an intermediate pre-transpose step. With this approach, it is the responsibility of the user (in this case the calling framework) to ensure that the weights are re-ordered into the required memory format. @ref NEGEMM::has_opt_impl is a static function that queries whether there exists fixed-format kernel, and if so will return in the expected weights format. The supported weight formats are enumerated in @ref arm_compute::WeightFormat.

@subsection S1_9_2_building_fixed_format Building with fixed format kernels

Fixed format kernels are only available for the CPU backend. To build Compute Library with fixed format kernels set fixed_format_kernels=1:

        scons Werror=1 debug=0 neon=1 opencl=0 embed_kernels=0 os=linux multi_isa=1 build=native cppthreads=1 openmp=0 fixed_format_kernels=1

@section S1_10_doxygen Building the Doxygen Documentation

This documentation has been generated using the following shell command:

        $ ./scripts/generate_documentation.sh

This requires Doxygen to be installed and available on your system.

*/

} // namespace arm_compute