I try to solve the problem $Ax=B$ where $A$ is a large sparse $n\times n$ matrix, and $B$ is a dense $n\times m$ matrix (here $n=754850$ and $m=182$). The backslash operator yields correct solution (x = A\B), but most of the computational time is done on one thread (the initial step of the process is nicely executed in parallel but not the finale steps). This obviously slows down the process. What is wrong here?

I also tried an LU factorization of $A$ and then solve for each column in $B$, but lu(A) seems not to be parallelized (although I am using version R2014b). I have seen that people think lu(A) is parallelized, so is there some update I miss or something?

By executing

x = A\b;

I got the following output

sp\: bandwidth = 60795+1+60795.
sp\: is A diagonal? no.
sp\: is band density (0.00) > bandden (0.50) to try banded solver? no.
sp\: is A triangular? no.
sp\: is A morally triangular? no.
sp\: is A a candidate for Cholesky (symmetric, real positive diagonal)? no.
sp\: use Unsymmetric MultiFrontal PACKage with automatic reordering.
UMFPACK V5.4.0 (May 20, 2009), Control:
    Matrix entry defined as: double complex
    Int (generic integer) defined as: UF_long

    0: print level: 2
    1: dense row parameter:    0.2
        "dense" rows have    > max (16, (0.2)*16*sqrt(n_col) entries)
    2: dense column parameter: 0.2
        "dense" columns have > max (16, (0.2)*16*sqrt(n_row) entries)
    3: pivot tolerance: 0.1
    4: block size for dense matrix kernels: 32
    5: strategy: 0 (auto)
    6: initial allocation ratio: 0.7
    7: max iterative refinement steps: 2
    12: 2-by-2 pivot tolerance: 0.01
    13: Q fixed during numerical factorization: 0 (auto)
    14: AMD dense row/col parameter:    10
       "dense" rows/columns have > max (16, (10)*sqrt(n)) entries
        Only used if the AMD ordering is used.
    15: diagonal pivot tolerance: 0.001
        Only used if diagonal pivoting is attempted.
    16: scaling: 1 (divide each row by sum of abs. values in each row)
    17: frontal matrix allocation ratio: 0.5
    18: drop tolerance: 0
    19: AMD and COLAMD aggressive absorption: 1 (yes)

    The following options can only be changed at compile-time:
    8: BLAS library used:  Fortran BLAS.  size of BLAS integer: 8
    9: compiled for MATLAB
    10: CPU timer is POSIX times ( ) routine.
    11: compiled for normal operation (debugging disabled)
    computer/operating system: Linux
    size of int: 4 UF_long: 8 Int: 8 pointer: 8 double: 8 Entry: 16 (in bytes)

    sp\: UMFPACK's factorization was successful.
    sp\: UMFPACK's solve was successful.
    UMFPACK V5.4.0 (May 20, 2009), Info:
        matrix entry defined as:          double complex
        Int (generic integer) defined as: UF_long
        BLAS library used: Fortran BLAS.  size of BLAS integer: 8
        MATLAB:                           yes.
        CPU timer:                        POSIX times ( ) routine.
        number of rows in matrix A:       754850
        number of columns in matrix A:    754850
        entries in matrix A:              86456682
        memory usage reported in:         16-byte Units
        size of int:                      4 bytes
        size of UF_long:                  8 bytes
        size of pointer:                  8 bytes
        size of numerical entry:          16 bytes

        strategy used:                    symmetric
        ordering used:                    amd on A+A'
        modify Q during factorization:    no
        prefer diagonal pivoting:         yes
        pivots with zero Markowitz cost:               0
        submatrix S after removing zero-cost pivots:
            number of "dense" rows:                    0
            number of "dense" columns:                 0
            number of empty rows:                      0
            number of empty columns                    0
            submatrix S square and diagonal preserved
        pattern of square submatrix S:
            number rows and columns                    754850
            symmetry of nonzero pattern:               1.000000
            nz in S+S' (excl. diagonal):               85701832
            nz on diagonal of matrix S:                754850
            fraction of nz on diagonal:                1.000000
        AMD statistics, for strict diagonal pivoting:
            est. flops for LU factorization:           4.16517e+14
            est. nz in L+U (incl. diagonal):           7435413184
            est. largest front (# entries):            896703025
            est. max nz in any column of L:            29945
            number of "dense" rows/columns in S+S':    0
        symbolic factorization defragmentations:       0
        symbolic memory usage (Units):                 199337590
        symbolic memory usage (MBytes):                3041.7
        Symbolic size (Units):                         1961813
        Symbolic size (MBytes):                        30
        symbolic factorization CPU time (sec):         15.57
        symbolic factorization wallclock time(sec):    15.45

        matrix scaled: yes (divided each row by sum of abs values in each row)
        minimum sum (abs (rows of A)):              3.25924e-02
        maximum sum (abs (rows of A)):              6.95838e+01

        symbolic/numeric factorization:      upper bound               actual      %
        variable-sized part of Numeric object:
            initial size (Units)               226394213            225639362   100%
            peak size (Units)               101780904057           8946021598     9%
            final size (Units)               98119112401           7543694062     8%
        Numeric final size (Units)           98124018962           7548223198     8%
        Numeric final size (MBytes)            1497253.7             115176.7     8%
        peak memory usage (Units)           101792135841           8957253382     9%
        peak memory usage (MBytes)             1553224.7             136676.8     9%
        numeric factorization flops          2.95433e+16          4.20989e+14     1%
        nz in L (incl diagonal)              36831301536           3820083550    10%
        nz in U (incl diagonal)              58996420369           3718115473     6%
        nz in L+U (incl diagonal)            95826967055           7537444173     8%
        largest front (# entries)            13417023050            896703025     7%
        largest # rows in front                    93703                29945    32%
        largest # columns in front                144673                29945    21%

        initial allocation ratio used:                 0.0942
        # of forced updates due to frontal growth:     0
        number of off-diagonal pivots:                 38
        nz in L (incl diagonal), if none dropped       3820083550
        nz in U (incl diagonal), if none dropped       3718115473
        number of small entries dropped                0
        nonzeros on diagonal of U:                     754850
        min abs. value on diagonal of U:               8.14e-09
        max abs. value on diagonal of U:               1.42e+02
        estimate of reciprocal of condition number:    5.75e-11
        indices in compressed pattern:                 13860039
        numerical values stored in Numeric object:     7537500101
        numeric factorization defragmentations:        3
        numeric factorization reallocations:           0
        costly numeric factorization reallocations:    0
        numeric factorization CPU time (sec):          106790.54
        numeric factorization wallclock time (sec):    7368.85
        numeric factorization mflops (CPU time):       3942.19
        numeric factorization mflops (wallclock):      57130.86
        symbolic + numeric CPU time (sec):             106806.11
        symbolic + numeric mflops (CPU time):          3941.62
        symbolic + numeric wall clock time (sec):      7384.30
        symbolic + numeric mflops (wall clock):        57011.33

        solve flops:                                   1.86839e+11
        iterative refinement steps taken:              1
        iterative refinement steps attempted:          2
        sparse backward error omega1:                  4.93e-16
        sparse backward error omega2:                  0.00e+00
        solve CPU time (sec):                          248.79
        solve wall clock time (sec):                   246.02
        solve mflops (CPU time):                       750.99
        solve mflops (wall clock time):                759.45

        total symbolic + numeric + solve flops:        4.21176e+14
        total symbolic + numeric + solve CPU time:     107054.90
        total symbolic + numeric + solve mflops (CPU): 3934.20
        total symbolic+numeric+solve wall clock time:  7633.09
        total symbolic+numeric+solve mflops(wallclock) 55177.60

    SParse MONItor output level 2.
    mmd: threshold = 1.1 * mindegree + 1,
         using approximate degrees in A'*A,
         supernode amalgamation every 3 stages,
         row reduction every 3 stages,
         withhold rows at least 50% dense in colmmd.
    Minimum degree orderings used with v4 chol, lu, and qr in \ and /.
    Approximate minimum degree orderings used with CHOLMOD and UMFPACK in \ and /.
    Pivot tolerance of 0.1 used by UMFPACK in \ and /.
    Backslash uses band solver if band density is > 0.5
    UMFPACK used for lu in \ and /.
    Symmetric pivot tolerance of 0.001 used by UMFPACK in \ and /.
    Pivot tolerance of 0.01 used by MA57 in \ and /.
  • 1
    $\begingroup$ By "initial step" do you mean factorization of the matrix A? And by final step do you mean using the factors to solve for the solution? Normally the factorization step takes much more time than the triangular solution steps unless you have a large number of right hand sides. How large is your m compared with n? $\endgroup$ – Bill Greene Apr 21 '15 at 21:06
  • $\begingroup$ "Initial step" was a vague term I used as I do not have complete knowlage at what stage the matlab functions started using only one thread. To put things in perspective. I am currently running a simulation which used the first hour on several threads, after 12 hours, it has still not yielded result (only working on one thread). n ~1e6 while m = 360 $\endgroup$ – user253249 Apr 21 '15 at 21:11
  • 4
    $\begingroup$ 12 hours, wow! I can see why you want to speed that up. If you add this line: spparms('spumoni',2); before you invoke the \ operator, you will get a lot more information about what is happening. If you post that output, we might be able to make some suggestions. $\endgroup$ – Bill Greene Apr 21 '15 at 21:27
  • 1
    $\begingroup$ If you want MATLAB to thread your solves, you'll probably need to point it at a threaded BLAS and LAPACK. $\endgroup$ – Bill Barth Apr 22 '15 at 2:15
  • 1
    $\begingroup$ The matrix here is sparse, so LAPACK isn't being used to do the LU factorization. I believe MATLAB uses UMFPACK. Parallel sparse LU factorization isn't really a well solved problem, so this doesn't really surprise me. $\endgroup$ – Brian Borchers Apr 22 '15 at 3:34

The MATLAB \ operator uses UMFPACK when the input matrix is sparse, square, and unsymmetric. UMFPACK is not a parallel sparse solver. However, the UMFPACK factorization step calls BLAS level-3 routines in its computational kernel. Most modern implementations of the BLAS (e.g. Intel MKL in MATLAB) support multicore architectures so UMFPACK can obtain fairly significant speedup in the factorization step. Since there isn't any high-level parallelism in UMFPACK and the parallelized BLAS doesn't help much in the solve step, there isn't any significant speedup there on multicore machines.

The MATLAB lu function calls an older LU implementation (https://www.mathworks.com/help/pdf_doc/otherdocs/simax.pdf) when the following form is used:

[L,U,P] = lu(A);

and UMFPACK for this form:

[L,U,P,Q] = lu(A);

The older implementation doesn't support BLAS level-3 calls so there is no speedup on multicore machines. The UMFPACK version of lu supports the parallel BLAS in the same way as backslash.

An interesting discussion of the MATLAB backslash operator and the lu function is included in this paper by Tim Davis: http://www.cise.ufl.edu/research/sparse/techreports/factorize.pdf

  • $\begingroup$ So the lu(A) command is not parallelized, but the backslashoperator works as if it uses LU factorization in parallel. Is it possible for me to access this routine such that I can solve for each collumn in $B$? Or is this problem simply a dead end (unless I create my own LU solver)? $\endgroup$ – user253249 Apr 23 '15 at 21:55
  • 1
    $\begingroup$ If you use the UMFPACK version of lu() it will use the parallel BLAS. Then you can solve using the L, U, P, Q matrices. The Davis paper explains in detail how to do this. There are also several non-Mathworks parallel LU sparse solvers available that interface with MATLAB. But they require some extra work to build and test them. $\endgroup$ – Bill Greene Apr 24 '15 at 0:22
  • $\begingroup$ The lu(A) seems to be parallelized, but when the backwards substitution x = Q_A*(U_A\(L_A\(P_A*B(:,i)))) seems not to be parallelized. And I can not create a parfor loop over all indices i as this would create a massive amount of momory consumption (each loop must copy the memoryexpencive matrices). So I guess there is not solution to this problem using the built in functions of Matlab $\endgroup$ – user253249 Apr 25 '15 at 17:40

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