Students selection period (part 2)


As for the graph bisection algorithm, I had to write a conversion script (which is very similar to the one I already wrote):

#include <algorithm>
#include <cassert>
#include <fstream>
#include <iostream>
#include <string>
#include <unordered_map>
#include <vector>

struct Dataset
    std::string name;
    size_t nb_nodes;
    size_t nb_edges;

const std::vector<Dataset> datasets = {
    {"release_to_obj", 16222788, 9907464},
    {"origin_to_snapshot", 112564374, 194970670},
    {"dir_to_rev", 35399184, 481829426},
    {"snapshot_to_obj", 170999796, 831089515},
    {"rev_to_rev", 1117498391, 1165813689},
    {"rev_to_dir", 2047888941, 1125083793}

void convert_dataset(
    std::string dataset_name, std::string graph_dir, std::string output_dir)
    auto dataset =
        std::find_if(datasets.begin(), datasets.end(),
            (const Dataset &d) -> bool { return == dataset_name; });
    if (dataset == datasets.end())
        std::cout << "Could not find dataset: " << dataset_name << "\n";

    std::unordered_map<std::string, uint32_t> node_ids;

    // Read graph nodes
        std::ifstream graph (graph_dir + dataset->name + ".nodes");
        std::string node;
        size_t node_cnt = 0;
        while (std::getline(graph, node))
            node_ids[node] = node_cnt;

        std::cout << "Read " << node_cnt << " nodes.\n";
        assert(node_cnt == dataset->nb_nodes);

    std::vector<std::vector<int>> edges(dataset->nb_nodes);

        std::ifstream graph (graph_dir + dataset->name + ".edges");
        std::string node1, node2;
        size_t edge_cnt = 0;
        while ( std::getline(graph, node1, ' ') &&
                std::getline(graph, node2))
            uint32_t node1_id = node_ids[node1];
            uint32_t node2_id = node_ids[node2];

        std::cout << "Read " << edge_cnt << " edges.\n";
        assert(edge_cnt == dataset->nb_edges);

    std::string file_path = output_dir + dataset->name + ".adj_graph";
    std::ofstream ligra_fmt (file_path, std::ios::out | std::ios::binary);

    ligra_fmt << "AdjacencyGraph\n";
    ligra_fmt << dataset->nb_nodes << '\n';
    ligra_fmt << dataset->nb_edges << '\n';

    long long sum_degree = 0;
    for (uint32_t node_id = 0; node_id < dataset->nb_nodes; node_id++)
        ligra_fmt << sum_degree << '\n';
        sum_degree += edges[node_id].size();

    for (uint32_t node_id = 0; node_id < dataset->nb_nodes; node_id++)
        for (auto edge : edges[node_id])
            ligra_fmt << edge << '\n';

int main(int argc, char *argv[])
    if (argc != 4)
        std::cout << "Usage: swh_to_ligra dataset_name graph_dir output_dir\n";
        return 0;

    std::string dataset_name = argv[1];
    std::string graph_dir = argv[2];
    if (graph_dir.back() != '/')
        graph_dir += '/';
    std::string output_dir = argv[3];
    if (output_dir.back() != '/')
        output_dir += '/';

    convert_dataset(dataset_name, graph_dir, output_dir);

    return 0;

I correctly compressed our smallest datasets (release_to_obj, origin_to_snapshot and dir_to_rev), however when trying to apply the same method to a bigger dataset (eg: snapshot_to_obj) I got a SIGSEGV.

I wrote the authors of the paper an email for two reasons:

I didn't get any reply so I moved on, and we decided to stick with WebGraph anyway.


Here is the git repo my mentor created for the compression project:

LLP does not scale on our graph, the rev_to_dir with 2B nodes and 1B edges was already quite a lot for the LLP, so we decided to use the BFS re-ordering as this should land quite similar result because of our graph topology, while taking way less time (on rev_to_dir LLP took 53h compared to 30min for BFS).

We needed to port some functions of WebGraph to support 64-bit version (since our graph have more than $2^{31}$ nodes and edges). I sent multiple patches to Sebastiano and Paolo:


On the server side, I decided to go with Java to easily work with the WebGraph framework. I was totally new to the language and ecosystem so learning the language and how to work with it was my first priority. On the client side, I chose Python since almost all of the Software Heritage infrastructure is written in Python, and they already implemented a class to deal with such API.

The first step was to enable REST API communication between the Java server and the Python client. I initially tried Spark as the web framework for Java but moved to Javalin because of a problem with HTTP 1.1 chunked transfer encoding (on the Python side). The client used the already implemented swh.core.SWHRemoteAPI, not much to do more.

Loading the compressed graph and working with WebGraph was the next logical step, before implementing actual graph operations.

Community Bonding

On May 6th, I got officially accepted to work with Software Heritage as my first Google Summer of Code! \o/

I had a call with my mentors (a co-mentor joined in) to discuss about:


Both my mentors got to meet Sebastiano IRL in Paris in a conference about his work! He also gave us access to a VM they use for experiments with 1TB of RAM and 40 CPUs. This enabled us to start compression on bigger datasets, but first we needed to create the .nodes files (which took some time):

zcat $dataset.edges.gz | tr ' ' '\n' |
sort -u --parallel $nb_threads -S100g -T tmp |
pigz -p $nb_threads -c > $dataset.nodes.gz

Weekly reports

From this point, I started to write weekly reports, here are the ones for May: