This repository provides the code to the paper:
Speculative Parallel Reverse Cuthill-McKee Reordering on Multi- and Many-core Architectures
Daniel Mlakar, Martin Winter, Mathias Parger, Markus Steinberger
mkdir build && cd build
cmake -DCUDA_BUILD_CC=<YOUR_COMPUTE_CAPABILITY>, e.g., 70> ..
make
NOTE: Supported values for YOUR_COMPUTE_CAPABILITY are 61, 70 and 75. If none specified the project is built for all supported compute capabilities
./CuthillMcKee input {OPTIONS}
Computes the Cuthill-McKee reordering of a sparse quadratic matrix given in
the CSR format.
OPTIONS:
-h, --help Display this help menu
input quadratic matrix in CSR format to reorder
-i[implementation],
--implementation=[implementation] Select an RCM implementation. Available values are: {ALL, cuSolverRCM, CPU, CPU_BATCH, GPU, GPU_BATCH}
-r, --reverse Revert CM permutation (RCM).
-s[start], --start=[start] Manually pick start node. If not specified, a pseudo-peripheral node is selected automatically.
-b[stable], --stable=[stable] Select whether sorting should be stable.
-w, --bandwidth Select whether to compute bandwidth of reordered matrix.
-t[threads], --threads=[threads] Select the number of threads to run for CPU_BATCH.
--f=[perffile],
--perffile=[perffile] File to store the performance data.
-o[output], --output=[output] File to store the reorder matrix.
To download a few small sample matrices form the SuiteSparse Matrix Collection run
python get_sample_matrices.py
from the mats/ directory. To reorder one of them change to the build/ directory and call
./CuthillMcKee ../mats/<some_sample_matrix>.mtx -i ALL -t 24
to run all included Cuthill-McKee implementations. Parallel approaches are run with 1 to 24 threads.