wheatman / byo

A Unified Framework for Benchmarking Large-Scale Graph Containers

BYO: A Unified Framework for Benchmarking Large-Scale Graph Containers

Organization

This repository contains code for our paper "BYO: A Unified Framework for Benchmarking Large-Scale Graph Containers" (VLDB'24).

It is designed to make it as easy as possible to implement and benchmark new graph container data structures.

The containers already implemented can be found in benchmarks/run_structures and include:

The following set based structures:

• vector of absl::btree code
  • run_absl_btree_set
  • run_absl_btree_set_inplace
• vector of absl::flat_hash_map code
  • run_absl_flat_hash_set
  • run_absl_flat_hash_set_inplace
• vector of std::set
  • run_std_set
  • run_std_set_inplace
• vector of std::unordered_set
  • run_std_unordered_set
  • run_std_unordered_set
• vector of aspen trees code paper
  • run_vector_aspen
  • run_vector_compressed_aspen (as described in the paper)
  • run_vector_aspen_inplace
  • run_vector_compressed_aspen_inplace
• vector of cpam trees code paper
  • run_vector_cpam
  • run_vector_compressed_cpam (as described in the paper)
  • run_vector_cpam_inplace
  • run_vector_compressed_cpam_inplace
• vector of PMAs code
  • run_vector_pma
  • run_vector_spma
  • run_vector_cpma
  • run_vector_scpma
• vector of tinysets code paper
  • run_vector_tinyset
• vector of vectors
  • run_vector_vector

The following full graph based structures:

• csr
  • run_csr
  • run_csr_shuffled
• gbbs paper
  • run_gbbs (as described in the paper)
• dhb code paper
  • run_dhb (as described in the paper)
• pcsr original code paper
  • run_pcsr_orig (can only run small graphs)
• pcsr code
  • run_pcsr (as described in the paper)
• ppcsr code paper
  • run_ppcsr (as described in the paper)
• F-Graph code paper
  • run_single_pma
  • run_single_spma
  • run_single_cpma
  • run_single_scpma (as described in the paper)
• SSTGraph code paper
  • run_sstgraph (as described in the paper)
• Terrace code paper
  • run_terrace (as described in the paper)

For many of the data structures we implement multiple different versions, we note above which version was described and benchmarked in the papers.

The raw data for all systems we tested can be found at here

Compilation

Compiler:

• g++ >= 11 with pthread support (Homemade Scheduler)
• clang++ >= 14 with support for OpenCilk

Build system:

• Bazel 2.1.0

The default compilation uses a lightweight scheduler developed at CMU (Homemade) for parallelism.

To compile codes for graphs with more than 2^32 edges, the LONG command-line parameter should be set. If the graph has more than 2^32 vertices, the EDGELONG command-line parameter should be set. Note that the codes have not been tested with more than 2^32 vertices, so if any issues arise please contact make an issue on github.

To compile with the OpenCilk scheduler instead of the Homegrown scheduler, use the Bazel configuration --config=cilk. To compile using OpenMP instead, use the Bazel configuration --config=openmp. To compile serially instead, use the Bazel configuration --config=serial.

To build:

# Load external libraries as submodules. (This only needs to be run once.)
git submodule update --init

# Note that the default compilation mode in bazel is to build optimized binaries
# (stripped of debug symbols). You can compile debug binaries by supplying `-c
# dbg` to the bazel build command.

#To build all of the different structures use the command 
`bazel build benchmarks/run_structures:all`

#similarly individual systems can be built with 

bazel build benchmarks/run_structures:run_csr

# The following commands cleans the directory:

$ bazel clean  # removes all executables

Adding a new graph Container

To add a new set container we recommend following the example given in run_vector.

Similarly to add a new Graph container we recommend following the example in run_single_pma.

You will also need to add the new container to the build file in benchmarks/run_structures/BUILD so that it can be built.

Running code

The applications take the input graph as input as well as an optional flag "-s" to indicate a symmetric graph. Symmetric graphs should be called with the "-s" flag for better performance. For example:

# For Bazel:
$ ./bazel-bin/benchmarks/run_structures/run_csr -s -src 10 ~/gbbs/inputs/rMatGraph_J_5_100

Note that the codes that compute single-source shortest paths (or centrality) take an extra -src flag. The benchmark is run four times by default, and can be changed by passing the -rounds flag followed by an integer indicating the number of runs.

On NUMA machines, adding the command "numactl -i all " when running the program may improve performance for large graphs. For example:

$ numactl -i all ./bazel-bin/benchmarks/run_structures/run_csr [...]

Running code on compressed graphs

The CSR graphs can also take in a compressed graph. The dynamic systems take in an uncompressed graph and compress it on the fly

We make use of the bytePDA format in our benchmark, which is similar to the parallelByte format of Ligra+, extended with additional functionality. We have provided a converter utility which takes as input an uncompressed graph and outputs a bytePDA graph. The converter can be used as follows:

# For Bazel:
bazel run //utils:compressor -- -s -o ~/gbbs/inputs/rMatGraph_J_5_100.bytepda ~/gbbs/inputs/rMatGraph_J_5_100
bazel run //utils:compressor -- -s -w -o ~/gbbs/inputs/rMatGraph_WJ_5_100.bytepda ~/gbbs/inputs/rMatGraph_WJ_5_100

After an uncompressed graph has been converted to the bytepda format, applications can be run on it by passing in the usual command-line flags, with an additional -c flag.

# For Bazel:
$ bazel run //benchmarks/BFS/NonDeterministicBFS:BFS_main -- -s -c -src 10 ~/gbbs/inputs/rMatGraph_J_5_100.bytepda

When processing large compressed graphs, using the -m command-line flag can help if the file is already in the page cache, since the compressed graph data can be mmap'd. Application performance will be affected if the file is not already in the page-cache. We have found that using -m when the compressed graph is backed by SSD results in a slow first-run, followed by fast subsequent runs.

Running code on binary-encoded graphs

We make use of a binary-graph format in our benchmark. The binary representation stores the representation we use for in-memory processing (compressed sparse row) directly on disk, which enables applications to avoid string-conversion overheads associated with the adjacency graph format described below. We have provided a converter utility which takes as input an uncompressed graph (e.g., in adjacency graph format) and outputs this graph in the binary format. The converter can be used as follows:

# For Bazel:
bazel run //utils:compressor -- -s -o ~/gbbs/inputs/rMatGraph_J_5_100.binary ~/gbbs/inputs/rMatGraph_J_5_100

After an uncompressed graph has been converted to the binary format, applications can be run on it by passing in the usual command-line flags, with an additional -b flag. Note that the application will always load the binary file using mmap.

# For Bazel:
$ bazel run //benchmarks/BFS/NonDeterministicBFS:BFS_main -- -s -b -src 10 ~/gbbs/inputs/rMatGraph_J_5_100.binary

Note that application performance will be affected if the file is not already in the page-cache. We have found that using -m when the binary graph is backed by SSD or disk results in a slow first-run, followed by fast subsequent runs.

Input Formats

We support the adjacency graph format used by the Problem Based Benchmark suite and Ligra.

The adjacency graph format starts with a sequence of offsets one for each vertex, followed by a sequence of directed edges ordered by their source vertex. The offset for a vertex i refers to the location of the start of a contiguous block of out edges for vertex i in the sequence of edges. The block continues until the offset of the next vertex, or the end if i is the last vertex. All vertices and offsets are 0 based and represented in decimal. The specific format is as follows:

AdjacencyGraph
<n>
<m>
<o0>
<o1>
...
<o(n-1)>
<e0>
<e1>
...
<e(m-1)>

This file is represented as plain text.

Weighted graphs are represented in the weighted adjacency graph format. The file should start with the string "WeightedAdjacencyGraph". The m edge weights should be stored after all of the edge targets in the .adj file.

Using SNAP graphs

Graphs from the SNAP dataset collection are commonly used for graph algorithm benchmarks. We provide a tool that converts the most common SNAP graph format to the adjacency graph format that GBBS accepts. Usage example:

# Download a graph from the SNAP collection.
wget https://snap.stanford.edu/data/wiki-Vote.txt.gz
gzip --decompress ${PWD}/wiki-Vote.txt.gz
# Run the SNAP-to-adjacency-graph converter.
# Run with Bazel:
bazel run //utils:snap_converter -- -s -i ${PWD}/wiki-Vote.txt -o <output file>
# Or run with Make:
#   cd utils
#   make snap_converter
#   ./snap_converter -s -i <input file> -o <output file>