Install the following packages (on Ubuntu LTS).

apt-get install python3-gv python3-pydot python3-networkx python3-pygraphviz libgv-python python-numpy python-matplotlib

I had to do some manual config to make it work:

(cd /usr/lib/python2.7/dist-packages; sudo ln -s libgv_python27.x86_64-linux-gnu.so _gv.so)

Then, you have to manually create directories and install the model:

mkdir graphs/ visu/ model/
wget 'http://people.inf.ethz.ch/skaestle/machinemodel.gz' -O "machinemodel.gz"
tar -xzf "machinemodel.gz" -C "machinedb/"

In order for the Simulator to find good configurations for distributed algorithms (such as tree topologies for atomic broadcasts), it needs to understand what a machine looks like.

Our machine database is based on Gerd's NetOS rack script, that boots Linux on each rack machine and executes a couple of programs to determine machine characteristics. There is a script that acquires that information for each machine that is defined in the database.

We enrich this with a set of low-level benchmarks; currently, this is only our pairwise UMP send/receive benchmark.

lscpu > lscpu.txt

There is a version of likwid ready to go at /mnt/scratch/skaestle/software/likwid-likwid-4.0.1/. If this does not work out of the box, try recompiling: make clean && make && make local.

Ideally, you have to call only: (cd /mnt/scratch/skaestle/software/likwid-likwid-4.0.1/; LD_LIBRARY_PATH=.:./ext/lua/:./ext/hwloc/ ./likwid-topology ) > likwid.txt

Last, the hierarchy of caches and affinity of cores to them is needed.

This is now also directly read from the machine database described above.

This Simulator is used to simulate and configure how messages should be send by Barrelfish's quorum program at runtime. The simulator implements several trees and a ring topology.

The Simulator can also generate shared memory models. Currently, it cannot simulate shared memory models, but it can generate configuration.

In order to generate a overlays, call the Simulator with the desired topology and machine name.

./simulator.py gruyere mst

In order to generate a hybrid model:

./simulator.py gruyere hybrid_bintree

It is also possible to generate several configurations in the same configuration file. The configuration can then be chanced at runtime from within the quorum program. This does not just speed up benchmarking due to fewer machine reboots, but could also be useful for reconfigurable hardware. An example is:

./simulator.py gruyere shm hybrid_bintree bintree adaptivetree cluster

while [[ 1 ]]; do ./simulator.py --server; sleep 2; done

Note: If the Simulator is running as a service within the ETH network, access from the outside world (pluton) and the Barrelfish rack cannot directly access it. Instead, on these machines, you have to setup a SSH tunnel.

This is how you setup the tunnel (directly on the machine where you want to execute Smelt).

ssh -L 25041:$SIMSERVER:25041 $TUNNELSERVER -N

$TUNNELSERVER here is the IP address of a machine that can be reached from the machine running Smelt and can itself reach the Simulator host machine.

When an SSH tunnel is used: export SMLT_HOSTNAME=localhost.