Compute Library
 22.08
How to Build and Run Examples

Table of Contents

Build options

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

Building for Linux

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 build=native: 

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 g++-arm-linux-gnueabihf 

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

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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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, arm_compute_core

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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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 -larm_compute_core -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, arm_compute_core
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/)
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 –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 :

/* Common graph parameters
*
* --help : Print the example's help message.
* --threads : The number of threads to be used by the example during execution.
* --target : Execution target to be used by the examples. Supported target options: Neon, CL, CLVK.
* --type : Data type to be used by the examples. Supported data type options: QASYMM8, F16, F32.
* --layout : Data layout to be used by the examples. Supported data layout options : NCHW, NHWC.
* --enable-tuner : Toggle option to enable the OpenCL dynamic tuner.
* --enable-cl-cache : Toggle option to load the prebuilt opencl kernels from a cache file.
* --fast-math : Toggle option to enable the fast math option.
* --data : Path that contains the trainable parameter files of graph layers.
* --image : Image to load and operate on. Image types supported: PPM, JPEG, NPY.
* --labels : File that contains the labels that classify upon.
* --validation-file : File that contains a list of image names with their corresponding label id (e.g. image0.jpg 5).
* This is used to run the graph over a number of images and report top-1 and top-5 metrics.
* --validation-path : The path where the validation images specified in the validation file reside.
* --validation-range : The range of the images to validate from the validation file (e.g 0,9).
* If not specified all the images will be validated.
* --tuner-file : The file to store the OpenCL dynamic tuner tuned parameters.
* --tuner-mode : Select tuner mode. Supported modes: Exhaustive,Normal,Rapid
* * Exhaustive: slowest but produces the most performant LWS configuration.
* * Normal: slow but produces the LWS configurations on par with Exhaustive most of the time.
* * Rapid: fast but produces less performant LWS configurations
*
* Note that data, image and labels options should be provided to perform an inference run on an image.
* Note that validation-file and validation-path should be provided to perform a graph accuracy estimation.
*
* Example execution commands:
*
* Execute a single inference given an image and a file containing the correspondence between label ids and human readable labels:
* ./graph_vgg16 --data=data/ --target=cl --layout=nhwc --image=kart.jpeg --labels=imagenet1000_clsid_to_human.txt
*
* Perform a graph validation on a list of images:
* ./graph_vgg16 --data=data/ --target=neon --threads=4 --layout=nchw --validation-file=val.txt --validation-path=ilsvrc_test_images/
*
* File formats:
*
* Validation file should be a plain file containing the names of the images followed by the correct label id.
* For example:
*
* image0.jpeg 882
* image1.jpeg 34
* image2.jpeg 354
*
* Labels file should be a plain file where each line is the respective human readable label (counting starts from 0).
* For example:
*
* 0: label0_name label0_name
* 1: label1_name or label1_name
* 2: label2_name label2_name
*/

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:

  1. GCC: https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/sve-support
  2. 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-

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

For NDK r18 or older, here is a guide to create your Android standalone toolchains from the NDK:

  • 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 Download 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/.

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.
For this particular example, you can specify: 
CC=clang CXX=clang++ scons toolchain_prefix=aarch64-linux-android21-
or: 
CC=aarch64-linux-android21-clang CXX=aarch64-linux-android21-clang++ scons toolchain_prefix=""
Attention
We used to use gnustl but as of NDK r17 it is deprecated so we switched to libc++
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

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

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 -larm_compute_core-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 -larm_compute_core-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 -larm_compute_core-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 -larm_compute_core-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 -larm_compute_core-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 -larm_compute_core-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.
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 –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.

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

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 armv7a and armv8a.

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

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

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 here.

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.

Bash on Ubuntu on Windows (cross-compile)

The best and easiest option is to use Ubuntu on Windows. 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.

Cygwin (cross-compile)

If the Windows subsystem for Linux is not available Cygwin 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 GCC cross-compilers 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.

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:

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

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

OpenCL DDK 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 -cl-arm-non-uniform-work-group-size).

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

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 cl_arm_integer_dot_product_int8, cl_arm_integer_dot_product_accumulate_int8 and cl_arm_integer_dot_product_accumulate_int16.

OpenCL kernel level debugging can be simplified with the use of printf, this requires the 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.