# Some experiments with SuiteSparse

**URL:** <https://discourse.julialang.org/t/some-experiments-with-suitesparse/9380>\
**Category:** Internals & Design\
**Created:** [February 27, 2018, 11:01pm UTC](https://discourse.julialang.org/t/some-experiments-with-suitesparse/9380 "2018-02-27T23:01:20Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![CodeLenz](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/codelenz/32/3419_2.png) [@CodeLenz](https://discourse.julialang.org/u/CodeLenz)\
**Post date:** [February 27, 2018, 11:01pm UTC](https://discourse.julialang.org/t/some-experiments-with-suitesparse/9380/1 "2018-02-27T23:01:20Z")

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Hi.

I would like to share some experiences regarding some modifications on SuiteSparse and its Julia interface. As a disclaimer, I state that I only tested the modifications in a linux machine, with fedora 27, gcc and intel hardware. All the modifications where performed over the current master (02/27/2018)

After some experiments, I played with the following:

1. I changed cholfact function to allow the user to set a specific ordering method, via

> unsafe\_store!(common\_nmethods, ordering)

where ordering in [0,2,…9] is a new information provided by the user, with a default value of 0 (this is the actual behaviour). The other options are:

1. AMD with default parameters.
2. METIS with default parameters.
3. NESDIS with default parameters: stopping the partitioning when the graph is of size nd small  
= 200 or less, remove nodes with more than max (16, prune dense \* sqrt (n)) nodes  
where prune dense = 10, and follow partitioning with constrained minimum degree ordering  
(CAMD for the symmetric case, CCOLAMD for the unsymmetric case).
4. natural ordering (with weighted postorder).
5. NESDIS, nd small = 20000, prune dense = 10.
6. NESDIS, nd small = 4, prune dense = 10, no constrained minimum degree.
7. NESDIS, nd small = 200, prune dense = 0.
8. COLAMD for A\*A’ or AMD for A

This modification is very easy and provides some fine tunning to the well written Julia interface.

1. I bumped the SuiteSparse version to 5.1.2. This version has two very interesting advantages over the current version used in Julia master: it includes Metis (Option 3 above) and has a good support for CUDA (when compared to the actual version). So far I played with METIS only.

Regarding METIS, its inclusion in Julia is also quite easy. First, I added a new entry to Make.inc: SUITE\_SPARSE\_METIS =0/1. If 0 (METIS disabled) one has to add -DNPARTITION to CHOLMOD\_CONFIG. Its done.

If METIS is enabled, than, in deps/suitesparse.mk:

a) set LDLIBS:=-fopenmp  
b) copy libmetis.so from SuiteSparse/lib to usr/lib (under julia source tree)  
c) include -lmetis when converting the cholmod library from static to shared. Also, add $(LDLIBS) to include openmp  
d) add -lmetis in SUITESPARSE\_LIB

> SUITESPARSE\_LIB := -lumfpack -lcholmod -lamd -lcamd -lcolamd -lspqr -lmetis

With those modifications, I have been able to use METIS and also to properly choose the partition method.

In a first test, I was realy disapointed with the performance of the default scheme (ordering=0). Actually, for a very large sparse matrix, CHOLMOD first try AMD and then uses METIS. Thus, the time increases when compared to a system without METIS. On the other hand, subsequent uses of the factorization are faster:

> C = cholfact(A) → time increases if METIS is needed  
> cholfact!(C,A) → faster

Sincerely yours,  
Eduardo.