Skip to main content

Performance prediction for Fortran kernels.

Project description

# Habakkuk #
Fortran code analysis for performance prediction

## Getting started ##

You will need the 'git' revision control system and the python package
manager, pip, installed. You can then clone the repository to your
local machine:

git clone https://github.com/arporter/habakkuk.git

and install it using pip (you can omit the --user flag if you have
root access and want to do a system-wide install):

cd habakkuk
pip install --user

If you used the --user flag then the habakkuk script will be installed
in ~/.local/bin and the associated modules in
~/.local/lib/pythonx.x/site-packages/habakkuk (where x.x is the
version of python you are using).

On redhat-based Linux systems, this should be all that is required. However, Ubuntu-based Linux systems are not generally configured to pick up locally-installed python packages. You must therefore do:

export PATH=${HOME}/.local/bin:${PATH}
export PYTHONPATH=${HOME}/.local/lib/pythonx.x/site-packages:${PYTHONPATH}

Having done this you should be all set to try the tool on
some Fortran code. There are various examples in src/tests/test_files.
The tool may be run like so:

cd habakkuk
habakkuk tests/test_files/triple_product.f90

You should then see output similar to the following:

Wrote DAG to test_triple_product.gv
Stats for DAG test_triple_product:
0 addition operators.
0 subtraction operators.
2 multiplication operators.
0 division operators.
0 fused multiply-adds.
2 FLOPs in total.
0 array references.
0 distinct cache-line references.
Did not find any array/memory references
Whole DAG in serial:
Sum of cost of all nodes = 2 (cycles)
2 FLOPs in 2 cycles => 1.0000*CLOCK_SPEED FLOPS
Everything in parallel to Critical path:
Critical path contains 4 nodes, 2 FLOPs and is 2 cycles long
FLOPS (ignoring memory accesses) = 1.0000*CLOCK_SPEED
Wrote DAG to test_triple_product_step0.gv
Wrote DAG to test_triple_product_step1.gv
Wrote DAG to test_triple_product_step2.gv
Schedule contains 2 steps:
0 * None (cost = 1)
1 * None (cost = 1)
Estimate using computed schedule:
Cost of schedule as a whole = 2 cycles
FLOPS from schedule (ignoring memory accesses) = 1.0000*CLOCK_SPEED
Estimate using perfect schedule:
Cost if all ops on different execution ports are perfectly overlapped = 2 cycles
e.g. at 3.85 GHz, these different estimates give (GFLOPS):
No ILP | Computed Schedule | Perfect Schedule | Critical path
3.85 | 3.85 | 3.85 | 3.85
No opportunities to fuse multiply-adds

The tool produces a Directed Acyclic Graph (DAG) for the body of the
inner-most loop of every loop-nest it encounters. If a routine (or main
program unit) contains no loops then a DAG is generated for the executable
part of that routine. Each DAG is written to file in the dot language (e.g.
test_triple_product.gv in the above example). If you have dot installed
(part of the graphviz package) then you can process these files to produce
an image of the DAG, e.g.:

cat triple_product_test.gv | dot -Tpng > triple.png

## Code Metrics ##

[![Coverage Status](https://coveralls.io/repos/github/arporter/habakkuk/badge.svg)](https://coveralls.io/github/arporter/habakkuk)

[![Code Health](https://landscape.io/github/arporter/habakkuk/master/landscape.svg?style=flat)](https://landscape.io/github/arporter/habakkuk/master)

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for Habakkuk, version 0.1.0
Filename, size File type Python version Upload date Hashes
Filename, size Habakkuk-0.1.0.tar.gz (48.3 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page