Many-core integration algorithm.
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README.md

README.md

Integration

Many-core integration algorithm, with classes to use them in C++.

Installation

Set the INSTALL_ROOT environment variable to the location of the pipeline sourcode. If this package is installed in $HOME/Code/APERTIF/Dedispersion this would be:

 $ export INSTALL_ROOT=$HOME/Code/APERTIF

Then build and test as follows:

 $ make install

Dependencies

Included programs

The integration step is typically compiled as part of a larger pipeline, but this repo contains two example programs in the bin/ directory to test and autotune an integration kernel.

IntegrationTest

Checks if the output of the CPU is the same for the GPU. The CPU is assumed to be always correct. Takes platform, layout, and kernel arguments, and has the following extra parameters:

  • print_code Print kernel source code
  • print_results Prints the integrated data
  • random Use random data instead of the default test data

IntegrationTuning

Tune the integration kernel's parameters by doing a complete sampling of the parameter space. Kernel configuration and runtime statistics are written to stdout. Takes platform, layout, and tuning arguments.

The output can be analyzed using the python scripts in in the analysis directory.

printCode

Prints the code for a specific integration kernel to stdout. Takes platform, layout, and kernel arguments.

Commandline arguments

Description of common commandline arguments for the separate binaries.

Compute platform specific arguments

  • opencl_platform OpenCL platform
  • opencl_device OpenCL device number
  • padding number of elements in the cacheline of the platform
  • vector vector size in number of elements

Data layout arguments

  • samples Number of samples; ie length of time dimension
  • dms Number of dispersion measures; ie length second dimension
  • integration Number of individual samples to add
  • dms_samples Ordering of the two dimensions: samples is fastest
  • samples_dms Ordering of the two dimensions: dms is fastest

Tuning parameters

  • iterations Number of times to run a specific kernel to improve statistics.
  • min_threads Minimum number of threads
  • max_threads Maximum number of threads
  • max_items Maximum number of variables that the automated code is allowed to use.

Kernel Configuration arguments

  • samples_per_block Number of samples per block
  • samples_per_thread Number of samples per thread

Analyzing tuning output

Kernel statistics can be saved to a database, and analyzed to find the optimal configuration.

Setup

MariaDB

Install mariadb, fi. via your package manager. Then:

  1. log in to the database: $ mysql
  2. create a database to hold our tuning data: create database AAALERT
  3. make sure we can use it (replace USER with your username): grant all privileges on AAALERT.* to 'USER'@'localhost';
  4. copy the template configuration file: cp analysis/config.py.orig analysis/config.py and enter your configuration.

Python

The analysis scripts use some python3 packages. An easy way to set this up is using virtualenv:

$ cd $INSTALL_ROOT/Integration/analysis`
$ virtualenv --system-site-packages --python=python3 env`
$ . env/bin/activate`

And then install the missing packages:

$ pip install pymysql

Run Analysis

The analysis is controlled by the analysis/dedispersin.py script. It prints data as space-separated data to stdout, where you can plot it with fi. gnuplot, or copy-paste it in your favorite spreadsheet. You can also write it to a file, that can then be read by the integration code.

  1. List current tables: ./integration.py list
  2. Create a table: ./integration.py create <table name>
  3. Enter a file create with DedispersionTuning into the database: ./integration load <table name> <file name>
  4. Find optimal kernel configuration: ./integration.py tune <table name> max <channels> <samples>

The tune subcommand also takes a number of different parameters: ./integration.py tune <table> <operator> <channels> <samples> [local|cache] [split|cont]

  • operator: max, min, avg, std (SQL aggergation commands)
  • channels: number of channels
  • samples: number of samples
  • local|cache When specified, only consider local or cache kernels. See tuning document.
  • split|cont When specified, only consider with or without the split_second option. See tuning document.

Included classes

configuration.hpp

The code is based on templates, for running the test pipeline we need to define some actual types. This file contains the datatypes used by this package.

Integration.hpp

  • integrationConf class
  • readTunedIntegrationConf
  • integrationDMsSamples
  • integrationSamplesDMs
  • getIntegrationDMsSamplesOpenCL
  • getIntegrationSamplesDMsOpenCL

License

Licensed under the Apache License, Version 2.0.