EMOGI

This repository provides the required source code to reproduce merged+aligned implementation results of EMOGI.

Compilation

Makefile is provided for a comprehensive compilation. However, depending on the GPU you are using, you'll need to modify the compute_XX and arch_XX arguments in NVCCFLAGS.

Dataset

We use a custom format for the datasets. Our datasets are divided into the following categories:

<dataset_name>.bel.dst
<dataset_name>.bel.col
<dataset_name>.bel.val

All input files are in binary format. The first two 8-byte values belong to a header section. The first 8-byte shows the total number of valid elements in the file. The second 8-byte is a placeholder and it can be ignored. The rest of the file contains the data and the datatypes for *.bel.dst and *.bel.col are always 8-byte (32-bit version programs still read in 8-byte and convert to 4-byte internally). *.bel.val uses 4-byte datatype.

We use CSR format for the input graphs. *.bel.col should contain the row offsets of CSR. *.bel.dst should contain the column indices. *.bel.val should contain the edge weight values (only required by SSSP).

How to Run

$ ./bfs -h
8-byte edge BFS
        -f | input file name (must end with .bel)
        -r | BFS root (unused when i > 1)
        -t | type of BFS to run
           | BASELINE = 0, COALESCE = 1, COALESCE_CHUNK = 2
        -m | memory allocation
           | GPUMEM = 0, UVM_READONLY = 1, UVM_DIRECT = 2
        -i | number of iterations to run
        -d | GPU device id (default=0)
        -h | help message

Example:

$ ./bfs -f com-Friendster.bel -r 0 -t 2 -m 2

Citation

@article{min2020emogi,
author = {Min, Seung Won and Mailthody, Vikram Sharma and Qureshi, Zaid and Xiong, Jinjun and Ebrahimi, Eiman and Hwu, Wen-mei},
title = {EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal in GPUs},
year = {2021},
publisher = {VLDB Endowment},
volume = {14},
number = {2},
journal = {Proc. VLDB Endow.},
}

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