This documentation summarises the content of the Astro-Accelerate software.
Please also refer to the wiki pages.
If you use AstroAccelerate, please cite the code using the following DOI and the relevant papers:
If you use de-dispersion please also cite:
If you use single-pulse detection please also cite:
If you use Fourier domain acceleration search please also cite:
Other:
Astro-Accelerate is used for real-time astronomy data processing. Its features include:
After following the steps below, consider using the Python interface.
An example is provided in the python/ folder.
Configurating pipelines using the Python interface is otherwise identical to an input_file (described below).
The software input is a sample data file.
To process the data file, astro-accelerate makes use of a configuration file.
Please see the section Creating An Input Configuration File for instructions on how to create a configuration file to process an input data file.
The software output is dependent on the choice of analysis component that is run.
Checking the Configuration of the Graphics Processing Unit (GPU) and Support for CUDA
In a terminal window, type
nvidia-smiThe output will look similar to the following example
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.20 Driver Version: 375.20 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla P100-PCIE... On | 0000:02:00.0 Off | 0 |
| N/A 32C P0 26W / 250W | 0MiB / 16308MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla P100-PCIE... On | 0000:03:00.0 Off | 0 |
| N/A 34C P0 25W / 250W | 0MiB / 16308MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+If no output is shown, or if an error appears, then it may indicate that a GPU has not been detected, or that the CUDA toolkit is not properly installed.
If you have a multi-GPU system, you need can select the card by setting it in the input_file. The setting to add to the input_file is
selected_card_id X
where X is a non-negative integer number which corresponds to the ID number of the GPU on your machine.
CUDA: CUDA 8.0 (see https://developer.nvidia.com/cuda-downloads)
C/C++ (version): As supported and required by CUDA.
Compiler: As supported by CUDA, but requiring also OpenMP support (compiler support can be found here).
An example input configuration file is shown below:
range 0 150 0.1 1 1
range 150 300 0.2 1 1
range 300 500 0.25 1 1
range 500 900 0.4 2 2
range 900 1200 0.6 4 4
range 1200 1500 0.8 4 4
range 1500 2000 1.0 4 4
range 2000 3000 2.0 8 8
sigma_cutoff 6
analysis
-acceleration
-periodicity
-output_dmt
-zero_dm
-zero_dm_with_outliers
-rfi
fdas_custom_fft
-fdas_inbin
fdas_norm
debug
file /home/wa78/filterbank/ska-mid-b2.fil`Features can be turned on or off by adding a character at the beginning of the line (here "-" is used).
rangerange tells the code to dedisperse and has input.
The format for the range parameter is
range dm_low dm_high dm_step downsampling_in_input_time downsampling_in_output_time
where dm_low, dm_high, dm_step, downsampling_in_input_time, and downsampling_in_output_time are to be replaced with suitable numerical values.
For example, a valid input for the range parameter is
range 500 900 0.4 2 2astro-accelerate will parse this input and dedisperse from a dm of 500 to a dm of 900 in steps of 0.4 (making (900-500)/0.4 dm trials), with input data
downsampled in time by 2 (e.g. 64 uS would be binned into 128 uS samples).
sigma_cutoffsigma_cutoff is the Signal-to-Noise Ratio (SNR) cutoff for your single pulse search.
Setting analysis
=
analysis this tells the code to analyse the dedispersed data, outputting data into the output directory. The output data are binary files and so which can be read in gnuplot and python.
For example, you could use python as follows splot "./57663_22588_B0531+21_000007.fil/global_analysed_frb.dat" binary format="%float%float%float%float" u 1:2:3 palette
accelerationacceleration this does a Fourier domain acceleration search on the data.
periodicityperiodicity sets a search for periodic objects.
output_dmtoutput_dmt outputs the entire dedispersed data to a file (in ASCII).
Setting zero_dm
=
zero_dm you can guess
zero_dm_with_outlierszero_dm_with_outliers is part of the RFI mitigation routine.
rfirfi tries to eliminate RFI.
(Astro-Accelerate welcomes developers to supply the team with data that includes RFI, which would be very helpful.)
fdas_custom_fftfdas_custom_fft runs fourier domain acceleration search with a custom FFT.
fdas_inbinfdas_inbin performs interbinning on the complex output.
fdas_normfdas_norm performs PRESTO block median normalization.
Setting debug
debug this gives detailed output.
Setting file
Please supply the input data file by using the file setting followed by the path to a valid
input data file
file <your input file>Input data files are by definition assumed to be formatted with 8-bit data. Please contact Astro-Accelerate if you have input data that is not 8-bits let me know (development for support for this is in progress and we may be able to help).
Obtain the Astro-Accelerate code by doing git clone https://github.com/AstroAccelerateOrg/astro-accelerate
Ensure you have the correct environment and pre-requisites. Set-up the environment (which will add CUDA to PATH and LD_LIBRARY_PATH)
source setup.shsetup.sh contains a hardcoded version number and a variable string to identify
whether the system is a 64-bit or 32-bit architecture. The user may need to edit
setup.sh to suit the CUDA version number, library paths, and the architecture number
in order to suit their needs. Users who already have all relevant CUDA paths configured
do not need to source setup.sh.
At this point, the user has a choice, they can either 1.) use the pre-configured Makefile that comes with the repository by default, or they can 2.) configure the build system themselves using CMake.
Note that in the case of using CMake, the Makefile that CMake produces will overwrite the default Makefile if the build is performed in source.
To run using the default Makefile, simply type
makeTo configure the build system using CMake, create a build directory
mkdir buildand then
cd build/run CMake
cmake ../The CUDA architecture can be specified with the -DCUDA_ARCH flag. For example, for architecture 7.0, do
cmake -DCUDA_ARCH="7.0" ../The software can then be compiled using the generated Makefile. To do so, simply type
makeIn both cases, the compilation process indicates which components are being compiled. The result is an executable called
astro-acceleratein the directory from which the build was performed. In the case of using the default Makefile, the library is compiled as a static library called
libastroaccelerate.aagainst which the executable is linked. In the case of using CMake to configure the build system, the library is compiled as a shared object library called
libastroaccelerate.soagainst which the executable is linked. In both cases, the library file will be located in the astro-accelerate build directory.
The user or developer may also with to run unit tests as part of the build. In this case, CMake should first be run with -DENABLE_TESTS=ON (the default is OFF) in order to enable the compilation of the tests, as follows
cmake -DCUDA_ARCH="7.0" -DENABLE_TESTS=ON ../The tests can then be run as follows
make testThe test results will be printed to the console. The test executables are located in a separate folder in the build directory and are separate from the main standalone executable, they do not form a part of the compiled library or standalone astro-accelerate executable.
Astro-Accelerate assumes its input is ready and compatible. To obtain compatible input, please follow the steps below. Please also ensure that the aforementioned environment variables have been set.
Run astro-accelerate using the format
./astro-accelerate /path/to/input_file.txtBy default, the output of astro-accelerate will be located in the same directory in which astro-accelerate was executed. Configuration files may be used to further specify, set, and change options. By default, the astro-accelerate executable looks for a configuration file in the same directory as the executable. A configuration file is required in order to run astro-accelerate. A number of example configuration files are included in the repository.
To print results from the analysis and periodicity modules using gnuplot, use the following command
splot "../path/to/output_file.dat" binary format="%f%f%f%f" u 1:2:3 palettewhich will plot the raw output data as saved to disk.
Astro-Accelerate comes with the facility to tune the software to the input that the user provides.
To do this, cd to the scripts directory.
Modify profiling.sh, changing the line that says
./astro-accelerate.sh ../input_files/ska_tune.txtto
./astro-accelerate.sh ../input_files/<your input file>where <your input file> must be replaced by the input configuration file.
The next step is to run the profiler tool that is provided in the repository
./profiling.shThis will create an optimised code for your search and GPU type.
Then, astro-accelerate can be run as usual by doing
./astro-accelerate.sh ../input_files/<your input file>where <your input file> must be replaced by the path to the input configuration file as specified in the previous step.
More detailed information can be found on the Wiki page of the repository and the Astro-Accelerate webpage.
Astro-accelerate can be compiled and linked against as a library. A good demonstration of the user interface is provided in main.cpp. For more advanced use cases, a good example boilerplate code is provided in aa_pipeline_generic.cpp.
The user interface is centred around the user requesting a series of components that the library will compute as a pipeline. The ordering of the pipeline components is determined by the library, however the user may create a series of pipelines to create their own custom ordering.
Return types are provided as a boolean to indicate whether a method was successful or not. When a method in the pipeline configuration process returns false, the pipeline will not run, and the user should revisit their settings.
When a method returns an object, then if the library cannot create a valid object, it will return an empty or trivial object. When an empty or trivial object is passed to the library at a later point, the relevant method will return false, or provide another empty or trivial object. Such a scenario prevents the astro-accelerate pipeline from running, in which case the user should revisit their settings.
The user can read .fil files or provide a std::vector<unsigned short> or a raw pointer of type unsigned short, but must in either case provide a valid aa_filterbank_metadata object that matches a filterbank data file (sigproc format).
User-side
Library-Side
If you notice any errors or have suggestions, please contact someone on the astro-accelerate team, or file an issue or bug report on the repository.
A number of code files may be covered by different licences. Please refer to these seperately. Please also refer to the Astro-Accelerate licence file provided.
Copyright © 2018 Astro-Accelerate. All rights reserved.