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OSDI-experiments-guide.md

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How to reproduce our OSDI'2022 paper results

To begin with, download the datasets and clone this repository. The first 3 graphs (Mico, Patent_citations, Youtube) are vertex-labeled graphs which are used for FSM. Put the datasets in the inputs directory.

On GPU, we compare with Pangolin and PBE. We also compare with CPU-targeted systems Peregrine and GraphZero (the OpenMP version in this repository). To run PBE and Peregrine, you can find this link for Peregrine and this link for PBE.

Next, we will build G2Miner. This requires CUDA toolkit 11.1.1 or greater. It also requires GCC 8 or greater. To install GCC 9 on Ubuntu 18.04, run:

$ sudo add-apt-repository ppa:ubuntu-toolchain-r/test
$ sudo apt update
$ sudo apt install gcc-9 g++-9
$ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-9

Now we are ready to compile G2Miner. Simply make in the root directory:

$ make

Binaries will be in the bin directory. For example, tc_omp_base is the OpenMP version of triangle counting on CPU, tc_gpu_base is the single GPU version, and tc_multigpu is the multi-GPU version. Finally, we can move on to the experiments.

Note that for motifs and diamond, as mentioned in the paper, we can apply an optimization called "counting-only pruning". This is why we have two versions for motifs: motif_gpu_base and motif_gpu_formula, with this optimization disabled and enabled respectively. To disable this optimization for Peregrine, one needs to use match instead of count, as instructed in section 2.4 in here.

Main benchmarks

All these experiments were conducted on the NVIDIA V100 GPU. For CPU, we use Intel Xeon Gold 5120 2.2GHz CPUs (56 cores in total) and 190GB RAM,

Note that the paper reports the mean runtimes of three executions for each experiment; here we only show each command once.

The total execution time can be found at the end of the log file.

We evaluate five workloads, triangle counting (TC), k-clique listing (k-CL), subgraph listing (SL), k-motif counting (k-MC), and frequent subgraph mining (FSM). For SL, we use two patterns rectangle (i.e., 4-cycle) and diamond.

Triangle Counting (Table 4)

$ bin/tc_gpu_base inputs/livej/graph      > lj-tc.log 2>&1
$ bin/tc_gpu_base inputs/orkut/graph      > ok-tc.log 2>&1
$ bin/tc_gpu_base inputs/twitter20/graph  > tw2-tc.log 2>&1
$ bin/tc_gpu_base inputs/twitter40/graph  > tw4-tc.log 2>&1
$ bin/tc_gpu_base inputs/friendster/graph > fr-tc.log 2>&1
$ bin/tc_gpu_base inputs/uk2007/graph     > uk-tc.log 2>&1

Clique Listing (Table 5)

$ # 4-cliques
$ bin/clique_gpu_base inputs/livej/graph      4 > lj-4-cliques.log 2>&1
$ bin/clique_gpu_base inputs/orkut/graph      4 > ok-4-cliques.log 2>&1
$ bin/clique_gpu_base inputs/twitter20/graph  4 > tw2-4-cliques.log 2>&1
$ bin/clique_gpu_base inputs/twitter40/graph  4 > tw4-4-cliques.log 2>&1
$ bin/clique_gpu_base inputs/friendster/graph 4 > fr-4-cliques.log 2>&1
$ # 5-cliques
$ bin/clique_gpu_base inputs/livej/graph      5 > lj-5-cliques.log 2>&1
$ bin/clique_gpu_base inputs/orkut/graph      5 > ok-5-cliques.log 2>&1
$ bin/clique_gpu_base inputs/friendster/graph 5 > fr-5-cliques.log 2>&1

Subgraph Listing (Table 6)

$ # diamond
$ bin/sgl_gpu_base inputs/livej/graph diamond        > lj-diamond.log 2>&1
$ bin/sgl_gpu_base inputs/orkut/graph diamond        > or-diamond.log 2>&1
$ bin/sgl_gpu_base inputs/twitter20/graph diamond    > tw2-diamond.log 2>&1
$ bin/sgl_gpu_base inputs/twitter40/graph diamond    > tw4-diamond.log 2>&1
$ bin/sgl_gpu_base inputs/friendster/graph diamond   > fr-diamond.log 2>&1
$ # rectangle
$ bin/sgl_gpu_base inputs/livej/graph rectangle      > lj-rectangle.log 2>&1
$ bin/sgl_gpu_base inputs/orkut/graph rectangle      > or-rectangle.log 2>&1
$ bin/sgl_gpu_base inputs/friendster/graph rectangle > fr-rectangle.log 2>&1

Motif Counting (Table 7)

$ # 3-motifs
$ bin/motif_gpu_base inputs/livej/graph      3 > lj-3-motifs.log 2>&1
$ bin/motif_gpu_base inputs/orkut/graph      3 > ok-3-motifs.log 2>&1
$ bin/motif_gpu_base inputs/twitter20/graph  3 > tw2-3-motifs.log 2>&1
$ bin/motif_gpu_base inputs/twitter40/graph  3 > tw4-3-motifs.log 2>&1
$ bin/motif_gpu_base inputs/friendster/graph 3 > fr-3-motifs.log 2>&1
$ # 4-motifs
$ bin/motif_gpu_base inputs/livej/graph      4 > lj-4-motifs.log 2>&1
$ bin/motif_gpu_base inputs/orkut/graph      4 > ok-4-motifs.log 2>&1
$ bin/motif_gpu_base inputs/friendster/graph 4 > fr-4-motifs.log 2>&1

Frequent Subgraph Mining (Table 8)

Notice that FSM takes two more arguments: max_num_edges an minimum_support. For 3-FSM, max_num_edges should be set to 2.

$ # Mico
$./bin/fsm_gpu_base inputs/mico/graph 2 300  > mi-fsm-3-300.log 2>&1
$./bin/fsm_gpu_base inputs/mico/graph 2 500  > mi-fsm-3-500.log 2>&1
$./bin/fsm_gpu_base inputs/mico/graph 2 1000 > mi-fsm-3-1000.log 2>&1
$./bin/fsm_gpu_base inputs/mico/graph 2 5000 > mi-fsm-3-5000.log 2>&1
$ # Patent
$./bin/fsm_gpu_base inputs/patent_citations/graph 2 300  > pa-fsm-3-300.log 2>&1
$./bin/fsm_gpu_base inputs/patent_citations/graph 2 500  > pa-fsm-3-500.log 2>&1
$./bin/fsm_gpu_base inputs/patent_citations/graph 2 1000 > pa-fsm-3-1000.log 2>&1
$./bin/fsm_gpu_base inputs/patent_citations/graph 2 5000 > pa-fsm-3-5000.log 2>&1
$ # Youtube
$./bin/fsm_gpu_base inputs/youtube/graph 2 300  > yo-fsm-3-300.log 2>&1
$./bin/fsm_gpu_base inputs/youtube/graph 2 500  > yo-fsm-3-500.log 2>&1
$./bin/fsm_gpu_base inputs/youtube/graph 2 1000 > yo-fsm-3-1000.log 2>&1
$./bin/fsm_gpu_base inputs/youtube/graph 2 5000 > yo-fsm-3-5000.log 2>&1

Multi-GPU (Figure 9 & 10)

For multi-GPU execution, run the executable xxx_multigpu instead of xxx_gpu_base, and add the number of GPUs at the end of the argument list.

$ bin/tc_multigpu inputs/twitter40/graph 8 > tw4-tc-8gpu.log 2>&1
$ bin/sgl_multigpu inputs/friendster/graph rectangle 4 > fr-rectangle-4gpu.log 2>&1
$ bin/motif_multigpu inputs/twitter20/graph 3 6 > tw2-3-motifs-6gpu.log 2>&1

Large Cliques (Figure 11)

$ # 6-cliques
$ bin/clique_gpu_base inputs/orkut/graph      6 > ok-6-cliques.log 2>&1
$ bin/clique_gpu_base inputs/friendster/graph 6 > fr-6-cliques.log 2>&1
$ # 7-cliques
$ bin/clique_gpu_base inputs/orkut/graph      7 > ok-7-cliques.log 2>&1
$ bin/clique_gpu_base inputs/friendster/graph 7 > fr-7-cliques.log 2>&1
$ # 8-cliques
$ bin/clique_gpu_base inputs/orkut/graph      8 > ok-8-cliques.log 2>&1
$ bin/clique_gpu_base inputs/friendster/graph 8 > fr-8-cliques.log 2>&1

Warp Efficiency (Figure 12)

$ nvprof -m "warp_execution_efficiency" bin/tc_gpu_base inputs/livej/graph > lj-tc-warp-eff.log 2>&1
$ nvprof -m "warp_execution_efficiency" bin/tc_gpu_base inputs/orkut/graph > or-tc-warp-eff.log 2>&1
$ nvprof -m "warp_execution_efficiency" bin/tc_gpu_base inputs/twitter2/graph > tw2-tc-warp-eff.log 2>&1
$ nvprof -m "warp_execution_efficiency" bin/sgl_gpu_base inputs/livej/graph rectangle > lj-rectangle-warp-eff.log 2>&1
$ nvprof -m "warp_execution_efficiency" bin/sgl_gpu_base inputs/orkut/graph rectangle > or-rectangle-warp-eff.log 2>&1
$ nvprof -m "warp_execution_efficiency" bin/motif_gpu_base inputs/livej/graph 3 > lj-3-motifs-warp-eff.log 2>&1
$ nvprof -m "warp_execution_efficiency" bin/motif_gpu_base inputs/orkut/graph 3 > ok-3-motifs-warp-eff.log 2>&1