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Bifrost

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A stream processing framework for high-throughput applications.

Paper

Bifrost Documentation

Bifrost Roadmap

A simple pipeline

Here's a snippet that reads Sigproc filterbank files, applies a Fast Dispersion Measure Transform (FDMT) on the GPU, and writes the results to a set of dedispersed time series files:

import bifrost as bf
import sys

filenames = sys.argv[1:]

print "Building pipeline"
data = bf.blocks.read_sigproc(filenames, gulp_nframe=128)
data = bf.blocks.copy(data, 'cuda')
data = bf.blocks.transpose(data, ['pol', 'freq', 'time'])
data = bf.blocks.fdmt(data, max_dm=100.)
data = bf.blocks.copy(data, 'cuda_host')
bf.blocks.write_sigproc(data)

print "Running pipeline"
bf.get_default_pipeline().run()
print "All done"

A more complex pipeline

Below is a longer snippet that demonstrates some additional features of Bifrost pipelines, including the BlockChainer tool, block scopes, CPU and GPU binding, data views, and dot graph output. This example generates high-resolution spectra from Guppi Raw data:

import bifrost as bf
import sys

filenames = sys.argv[1:]
f_avg = 4
n_int = 8

print "Building pipeline"
bc = bf.BlockChainer()
bc.blocks.read_guppi_raw(filenames, core=0)
bc.blocks.copy(space='cuda', core=1)
with bf.block_scope(fuse=True, gpu=0):
    bc.blocks.transpose(['time', 'pol', 'freq', 'fine_time'])
    bc.blocks.fft(axes='fine_time', axis_labels='fine_freq', apply_fftshift=True)
    bc.blocks.detect('stokes')
    bc.views.merge_axes('freq', 'fine_freq')
    bc.blocks.reduce('freq', f_avg)
    bc.blocks.accumulate(n_int)
bc.blocks.copy(space='cuda_host', core=2)
bc.blocks.write_sigproc(core=3)

pipeline = bf.get_default_pipeline()
print pipeline.dot_graph()
print "Running pipeline"
pipeline.shutdown_on_signals()
pipeline.run()
print "All done"

Feature overview

  • Designed for sustained high-throughput stream processing
  • Python API wraps fast C++/CUDA backend
  • Fast and flexible ring buffer specifically designed for processing continuous data streams
  • Native support for both system (CPU) and CUDA (GPU) memory spaces and computation
  • Fast kernels for transposition, dedispersion, correlation, beamforming and more
  • bfMap: JIT-compiled ND array transformations
  • Fast UDP data capture
  • A growing library of ready-to-use pipeline 'blocks'
  • Rich metadata enables seamless interoperability between blocks

Installation

C dependencies

$ sudo apt-get install exuberant-ctags

Python dependencies

  • numpy
  • contextlib2
  • pint
  • ctypesgen
$ sudo pip install numpy contextlib2 pint git+https://github.com/olsonse/ctypesgen.git@9bd2d249aa4011c6383a10890ec6f203d7b7990f

Bifrost installation

Edit user.mk to suit your system, then run:

$ make -j
$ sudo make install

which will install the library and headers into /usr/local/lib and /usr/local/include respectively.

You can call the following for a local Python installation:

$ sudo make install PYINSTALLFLAGS="--prefix=$HOME/usr/local"

Docker container

Install dependencies:

Build Docker image:

$ make docker

Launch container:

$ nvidia-docker run --rm -it ledatelescope/bifrost

For CPU-only builds:

$ make docker-cpu
$ docker run --rm -it ledatelescope/bifrost

Running tests

To run all CPU and GPU tests:

$ make test

Documentation

Online Bifrost Documentation

Building the docs with Docker

To quickly build the docs using Docker, ensure that you have built a Bifrost container as ledatelescope/bifrost. Then, inside the docs folder, execute ./docker_build_docs.sh, which will create a container called bifrost_docs, then run it, and have it complete the docs-building process for you, outputting the entire html documentation inside docs/html on your machine.

Building the docs from scratch

Install sphinx and breathe using pip, and also install Doxygen.

Doxygen documentation can be generated by running:

$ make doc

This documentation can then be used in a Sphinx build by running

$ make html

inside the /docs directory.

Contributors

  • Ben Barsdell
  • Daniel Price
  • Miles Cranmer
  • Hugh Garsden
  • Jayce Dowell