Getting Started

Getting Started

Build GraphBrew and run your first benchmark.

Requirements

Component	Minimum	Notes
OS	Linux (Ubuntu 20.04+) or macOS	tested on Ubuntu 22.04 LTS
Compiler	GCC 7+	C++17 required
RAM	8 GB	16 GB+ for large graphs
Disk	50 GB	benchmark graphs
Python	3.8+ (optional)	analysis scripts use stdlib only

Optional: numpy, matplotlib, pandas for plotting and analysis.

Install dependencies

# Ubuntu / Debian
sudo apt-get install -y build-essential g++ libboost-all-dev \
    libnuma-dev google-perftools python3

# Fedora / RHEL
sudo dnf install -y gcc-c++ make boost-devel numactl-devel gperftools python3

# macOS
xcode-select --install
brew install gcc boost google-perftools

Verify with python3 scripts/graphbrew_experiment.py --check-deps.

Build

git clone https://github.com/UVA-LavaLab/GraphBrew.git
cd GraphBrew
make all        # production binaries → bench/bin/
make all-sim    # cache-sim binaries → bench/bin_sim/

Build flags:

Flag	Effect
RABBIT_ENABLE=0	drop the Boost-backed Rabbit variant. The CSR variant stays enabled.
DEBUG=1	debug symbols
SANITIZE=1	address sanitizer

Boost 1.58 is only needed for -o 8:boost; the default -o 8 (CSR Rabbit) needs no Boost. If you do need it: python3 scripts/graphbrew_experiment.py --install-boost downloads and installs it to /opt/boost_1_58_0.

Verify

ls bench/bin/   # expect: bc bfs cc cc_sv converter pr pr_spmv sssp tc tc_p
ls bench/bin_sim/   # expect: bc bfs cc cc_sv pr pr_spmv sssp tc

# Smoke test on the included tiny graph
./bench/bin/pr -f scripts/test/graphs/tiny/tiny.el -s -n 3

You should see Read Time, Build Time, three Trial Time lines, and Average Time.

First benchmark

PageRank on the tiny graph with three reordering choices:

GRAPH=scripts/test/graphs/tiny/tiny.el

# No reordering (baseline)
./bench/bin/pr -f $GRAPH -s -o 0 -n 3

# Cheap degree/bucket control
./bench/bin/pr -f $GRAPH -s -o 7 -n 3

# Explicit GraphBrew composition
./bench/bin/pr -f $GRAPH -s \
  -o 12:leiden:compose:sg_none:comm_size_desc:intra_gorder:gw8 \
  -n 3

-o N selects the reordering algorithm (see Reordering-Algorithms). -s symmetrises a directed input. -n N runs N trials.

The explicit recipe above demonstrates the composition grammar. The running example also shows BFS to illustrate that local layouts are independently configurable.

Run a real graph

# Download a small social network from SNAP
wget https://snap.stanford.edu/data/ego-Facebook.txt.gz
gunzip ego-Facebook.txt.gz
mv ego-Facebook.txt facebook.el

./bench/bin/pr -f facebook.el -s \
  -o 12:leiden:compose:sg_none:comm_size_desc:intra_gorder:gw8 \
  -n 5

GraphBrew recipes explicitly select the partitioner, block layout, and local vertex layout. Rabbit-dependent presets remain available as baselines. Start with the GraphBrew Running Example, then use GraphBrewOrder as the token reference.

Common flags

Flag	Purpose
-f <file>	input graph
-s	symmetrise directed input
-o <algo>	reordering algorithm (or -o 12:variant)
-n <N>	number of trials
-r <vertex>	root for BFS / SSSP
-i <iters>	iterations per trial (PR)

Full list: Command-Line-Reference.

Automated pipeline

For batch experiments (download → build → benchmark → analyse):

# Quick test (~10 min): small graphs only
python3 scripts/graphbrew_experiment.py --target-graphs 30 --size small

# Standard (~1 h): medium graphs
python3 scripts/graphbrew_experiment.py --target-graphs 80 --size medium

# Full (~4-8 h): all sizes, 150 graphs per bucket
python3 scripts/graphbrew_experiment.py --target-graphs 150

# Preview only — print commands without running
python3 scripts/graphbrew_experiment.py --target-graphs 150 --dry-run

--target-graphs N is shorthand for --full --catalog-size N --auto --all-variants. See Benchmark-Suite for size buckets.

Common build issues

Error	Fix
fatal error: boost/range/algorithm.hpp	sudo apt-get install libboost-all-dev
-fopenmp not supported	sudo apt-get install libomp-dev
g++ too old (< 7)	install g++-11, export CXX=g++-11, rebuild
OOM during build	drop parallelism: make -j2 all

More in Troubleshooting.

Next steps

• Reordering-Algorithms — what each algorithm does and when to use it
• GraphBrew Running Example — one graph carried through all six stages
• GraphBrewOrder — the explicit composition grammar and cost controls
• Running-Benchmarks — manual benchmark workflow
• Reproducible Experiments — controlled measurement workflow