Command Line Reference

Command-Line Reference

Complete reference for all GraphBrew command-line options.

Benchmark Binaries

All binaries are located in bench/bin/. The automated pipeline uses six benchmarks:

Binary	Algorithm	Description
`pr`	PageRank	Page importance ranking
`bfs`	BFS	Breadth-first search
`cc`	Connected Components	Find connected subgraphs
`sssp`	Shortest Paths	Single-source shortest paths
`bc`	Betweenness Centrality	Centrality measure
`tc`	Triangle Counting	Count triangles
`converter`	-	Convert graph formats

Universal Options

These options work with all benchmarks:

Input/Output

Option	Description	Example
`-f <file>`	Input graph file (required)	`-f graph.el`
`-o <id>`	Reordering algorithm ID (0-17)	`-o 7`
`-s`	Make graph undirected (symmetrize)	`-s`
`-n <trials>`	Number of benchmark trials	`-n 5`

File Format Detection

GraphBrew automatically detects the file format from the file extension:

Extension	Format	Description
`.el`	Edge list	Text file with "src dst" pairs
`.wel`	Weighted edge list	Text file with "src dst weight"
`.mtx`	Matrix Market	Standard sparse matrix format
`.gr`	DIMACS	DIMACS graph format
`.sg`	Serialized graph	Binary format (unweighted)
`.wsg`	Weighted serialized	Binary format (weighted)
`.graph`	METIS	METIS adjacency format

Example:

./bench/bin/pr -f graph.el -s -n 5      # Auto-detected as edge list
./bench/bin/sssp -f graph.wel -s -n 5   # Auto-detected as weighted edge list

General Flags

Option	Description
`-h`	Show help message
`-v`	Verify results (slower)
`-g <scale>`	Generate 2^scale Kronecker graph
`-u <scale>`	Generate 2^scale uniform-random graph
`-k <degree>`	Average degree for synthetic graph (default: 16)

Reordering Algorithm IDs

Use with -o <id>:

ID	Algorithm	Category
0	ORIGINAL	None
1	Random	Basic
2	Sort	Basic
3	HubSort	Hub-based
4	HubCluster	Hub-based
5	DBG	DBG-based
6	HubSortDBG	DBG-based
7	HubClusterDBG	DBG-based
8	RabbitOrder	Community (variants: csr/boost, default: csr)
9	GOrder	Community
10	COrder	Community
11	RCMOrder	Community
12	GraphBrewOrder	Community
13	MAP	External mapping
14	AdaptiveOrder	ML
15	LeidenOrder	Leiden (igraph)
16	LeidenDendrogram	Leiden (variants: dfs/dfshub/dfssize/bfs/hybrid)
17	LeidenCSR	Leiden (variants: gve/dfs/bfs/hubsort/fast/modularity, default: gve)

Benchmark-Specific Options

PageRank (pr)

./bench/bin/pr [options]

Option	Description	Default
`-i <iter>`	Maximum iterations	20
`-t <tol>`	Convergence tolerance	1e-4

Note: The damping factor is hardcoded to 0.85 in the implementation.

Examples:

# Standard PageRank
./bench/bin/pr -f graph.el -s -n 5

# With custom parameters
./bench/bin/pr -f graph.el -s -i 100 -t 1e-8 -n 5

# With reordering
./bench/bin/pr -f graph.el -s -o 17 -n 5

BFS (bfs)

./bench/bin/bfs [options]

Option	Description	Default
`-r <root>`	Starting vertex	Random

Examples:

# BFS from vertex 0
./bench/bin/bfs -f graph.el -s -r 0 -n 5

# BFS from random roots
./bench/bin/bfs -f graph.el -s -n 5

# With reordering
./bench/bin/bfs -f graph.el -s -o 7 -r 0 -n 5

Connected Components (cc)

./bench/bin/cc [options]

No additional options.

Examples:

# Find components
./bench/bin/cc -f graph.el -s -n 5

# With verification
./bench/bin/cc -f graph.el -s -v -n 3

SSSP (sssp)

./bench/bin/sssp [options]

Option	Description	Default
`-r <root>`	Source vertex	0
`-d <delta>`	Delta for delta-stepping	Auto

Examples:

# SSSP from vertex 0
./bench/bin/sssp -f graph.wel -s -r 0 -n 5

# With custom delta
./bench/bin/sssp -f graph.wel -s -r 0 -d 2 -n 5

Note: SSSP requires weighted edges (.wel format).

Betweenness Centrality (bc)

./bench/bin/bc [options]

Option	Description	Default
`-r <root>`	Source vertex	Random
`-i <iterations>`	Number of source iterations	1

Examples:

# BC from single source
./bench/bin/bc -f graph.el -s -r 0 -n 5

# BC with multiple iterations (more accurate)
./bench/bin/bc -f graph.el -s -i 4 -n 5

Triangle Counting (tc)

./bench/bin/tc [options]

No additional options.

Examples:

# Count triangles
./bench/bin/tc -f graph.el -s -n 5

# With reordering (important for TC!)
./bench/bin/tc -f graph.el -s -o 7 -n 5

Converter

./bench/bin/converter [options]

Option	Description
`-f <input>`	Input file
`-s`	Symmetrize input
`-o <id>`	Apply reordering algorithm
`-b <file>`	Output serialized graph (.sg)
`-e <file>`	Output edge list (.el)
`-p <file>`	Output Matrix Market format (.mtx)
`-y <file>`	Output Ligra format (.ligra)
`-w`	Make output weighted (.wel/.wsg)
`-x <file>`	Output reordered labels as text (.so)
`-q <file>`	Output reordered labels as binary (.lo)

Examples:

# Convert to binary format
./bench/bin/converter -f graph.el -s -b graph.sg

# Use converted graph
./bench/bin/pr -f graph.sg -n 5

Environment Variables

Thread Control

# Set number of OpenMP threads
export OMP_NUM_THREADS=8
./bench/bin/pr -f graph.el -s -n 5

Perceptron Weights

# Override default weights file (overrides type matching)
export PERCEPTRON_WEIGHTS_FILE=/path/to/weights.json
./bench/bin/pr -f graph.el -s -o 14 -n 3

Note: If PERCEPTRON_WEIGHTS_FILE is not set, AdaptiveOrder automatically:

Computes graph features (modularity, degree variance, hub concentration, etc.)
Finds the best matching type file using Euclidean distance to centroids
Loads weights from scripts/weights/active/type_N.json
Falls back to hardcoded defaults if no type files exist

Example output:

Best matching type: type_0 (distance: 0.4521)
Perceptron: Loaded 5 weights from scripts/weights/active/type_0.json

NUMA Binding

# Bind to NUMA node 0
numactl --cpunodebind=0 --membind=0 ./bench/bin/pr -f graph.el -s -n 5

Output Format

Standard Output

Loading graph from graph.el...
Graph has 4039 nodes and 88234 edges
Reordering with HUBCLUSTERDBG...

Trial   Time(s)
1       0.0234
2       0.0231
3       0.0229
4       0.0232
5       0.0230

Average: 0.0231 seconds
Std Dev: 0.0002 seconds

With Verification

Loading graph from graph.el...
...
Verification: PASSED

BFS Output (includes throughput)

Source: 0
Trial   Time(s)   Edges Visited   MTEPS
1       0.0012    88234           73.5
2       0.0011    88234           80.2
3       0.0012    88234           73.5

Average: 0.0012 seconds, 75.7 MTEPS

AdaptiveOrder Output

=== Adaptive Reordering Selection ===
Comm    Nodes   Edges   Density Selected
131     1662    16151   0.0117  LeidenCSR
272     103     149     0.0284  Original
...

=== Algorithm Selection Summary ===
Original: 846 communities
LeidenCSR: 3 communities
HUBCLUSTERDBG: 2 communities

Common Command Patterns

Quick Test

./bench/bin/pr -f test/graphs/4.el -s -n 1

Compare Algorithms

for algo in 0 7 14 15 17; do
    echo "=== Algorithm $algo ==="
    ./bench/bin/pr -f graph.el -s -o $algo -n 3
done

Run All Benchmarks

for bench in pr bfs cc sssp bc tc; do
    echo "=== $bench ==="
    ./bench/bin/$bench -f graph.el -s -o 7 -n 3
done

Batch Processing

for graph in graphs/*.el; do
    echo "=== $graph ==="
    ./bench/bin/pr -f "$graph" -s -o 7 -n 3 | tail -2
done

Exit Codes

Code	Meaning
0	Success
1	General error
-1	Argument parsing failed
Other	System error

Python Script Options (graphbrew_experiment.py)

The unified experiment script provides comprehensive options for training and benchmarking.

Dependency Management

Option	Description
`--check-deps`	Check system dependencies (g++, boost, numa, etc.)
`--install-deps`	Install missing system dependencies (requires sudo)
`--install-boost`	Download, compile, and install Boost 1.58.0 to /opt/boost_1_58_0

Pipeline Options

Option	Description
`--full`	Run complete pipeline (download → build → experiment → weights)
`--download-only`	Only download graphs
`--download-size SIZE`	SMALL (16), MEDIUM (28), LARGE (37), XLARGE (6), ALL (87 graphs)
`--phase PHASE`	Run specific phase: all, reorder, benchmark, cache, weights, adaptive

Memory Management

Option	Description
`--max-memory GB`	Maximum RAM (GB) for graph processing. Graphs exceeding this are skipped.
`--auto-memory`	Automatically detect available RAM and skip graphs that won't fit (uses 80% of total)

Memory estimation: (edges × 24 bytes + nodes × 8 bytes) × 1.5

Disk Space Management

Option	Description
`--max-disk GB`	Maximum disk space (GB) for downloads. Downloads stop when limit is reached.
`--auto-disk`	Automatically limit downloads to available disk space (uses 80% of free space)

Training Options

Option	Description
`--train-adaptive`	Run iterative training feedback loop
`--train-large`	Large-scale training with batching and multi-benchmark support
`--target-accuracy N`	Target accuracy % for training (default: 80)
`--max-iterations N`	Maximum training iterations (default: 10)
`--learning-rate N`	Weight adjustment rate (default: 0.1)
`--fill-weights`	Fill ALL weight fields: runs cache sim, graph features, benchmark analysis
`--init-weights`	Initialize/upgrade weights file with enhanced features

Weight Run Management

Option	Description
`--list-runs`	List all saved weight runs in `scripts/weights/runs/`
`--merge-runs [TIMESTAMP ...]`	Merge specific runs (or all if no args) into `merged/`
`--use-run TIMESTAMP`	Use weights from a specific run (copy to `active/`)
`--use-merged`	Use merged weights (copy `merged/` to `active/`)
`--no-merge`	Don't auto-merge weights after `--fill-weights` (keep run isolated)

Label Map Options

Option	Description
`--generate-maps`	Pre-generate .lo mapping files for consistent reordering
`--use-maps`	Use pre-generated label maps instead of regenerating

Examples

# Auto-detect RAM and disk limits, run on all fitting graphs
python3 scripts/graphbrew_experiment.py --full --download-size ALL --auto-memory --auto-disk

# Set explicit 32GB memory and 100GB disk limits
python3 scripts/graphbrew_experiment.py --full --download-size ALL --max-memory 32 --max-disk 100

# Fill all weight fields comprehensively
python3 scripts/graphbrew_experiment.py --fill-weights --auto-memory --auto-disk --download-size ALL

# Iterative training to 85% accuracy
python3 scripts/graphbrew_experiment.py --train-adaptive --target-accuracy 85 --graphs small

# Download and run full pipeline
python3 scripts/graphbrew_experiment.py --full --download-size SMALL

See Python-Scripts for complete documentation.

Tips

Always use -s for undirected graphs
Use -n 5 or more for reliable timing
Start with -o 0 as baseline
Use -o 14 (AdaptiveOrder) for unknown graphs
Verify first run with -v to check correctness

← Back to Home | Configuration Files →

Command Line Reference

Command-Line Reference

Benchmark Binaries

Universal Options

Input/Output

File Format Detection

General Flags

Reordering Algorithm IDs

Benchmark-Specific Options

PageRank (pr)

BFS (bfs)

Connected Components (cc)

SSSP (sssp)

Betweenness Centrality (bc)

Triangle Counting (tc)

Converter

Environment Variables

Thread Control

Perceptron Weights

NUMA Binding

Output Format

Standard Output

With Verification

BFS Output (includes throughput)

AdaptiveOrder Output

Common Command Patterns

Quick Test

Compare Algorithms

Run All Benchmarks

Batch Processing

Exit Codes

Python Script Options (graphbrew_experiment.py)

Dependency Management

Pipeline Options

Memory Management

Disk Space Management

Training Options

Weight Run Management

Label Map Options

Examples

Tips

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