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The ParSHUM library

The ParSHUM library implements parallel sparse a LU factorization. The configuration and installation process for the library is explained next, followed by a description of how to use the driver that is provided by the library and the interface of the library. The last section explains how to obtain optimal performance when using this solver.

Getting sources

ParSHUM is developed at STFC in the context of the NLAFET project, and its source codes are available on GitHub. The source codes can be obtained with the following git command:

$ git clone


This library relies on cmake for the building of the library. The ParSHUM library depends only on the MKL library for the BLAS and LAPACK routines. The CFLAGS and LDFLAGS for MKL must be provided by passing as arguments −DMKL_LDFLAGS and −DMKL_CFLAGS to the cmake script. If these two argument are not given, then the cmake commands fails. These can be obtained from the MKL Link Advisor. Additionally, the SPRAL library could be provided in order to be able to read matrices stored in Rutherford Boeing format. This can be done by providing the install root directory of SPRAL to the −DSPRAL_DIR cmake’s argument.


Once the library has been downloaded into a directory, issue the following commands in that directory:

$ mkdir build &&  cd build
$ cmake -DMKL_LDFLAGS=<MKL LDFLAGS> -DMKL_CFLAGS=<MKL CFLAGS> -DSPRAL_DIR=<spral installation directory>

To build the library use:

$ make 

Using the test driver

Once the library has been built a test driver called ParSHUM_simple is created in the bin directory. This driver takes a large set of parameters:

  • --matrix (string) => specifies the path to the matrix file. Our driver is able to read classic IJV files (suffixed by ".ijv" or ".mtl"). Harwell-Boeing matrices (suffixed by ".hb" or ".rb") can be read if the library is compiled with the −DSP RAL_DIR option.
  • --marko_threshold (real) => Markowitz threshold of acceptability for a pivot. This threshold should be larger than one. The default value is 4.
  • --threshold_value (real) => threshold value of acceptability for a pivot. This threshold should be between zero and one. The default value is 10 −4 .
  • --schur_density_tolerance (real) => maximum density allowed of the Schur complement. Once the Schur complement reaches this density, we switch to dense factorization. This parameter should be between zero and one. The default value is 0.2.
  • --nb_previous_steps (integer) => number of previous steps for which we keep track of how many pivots have been found. This parameter should be at least one. The default value is 5.
  • --min_pivot_per_steps (integer) => minimum number of pivots in the last nb_previous_steps steps. If the number of pivots is less than this value, the solver switches to a dense factorization. This parameter should be at least nb_previous_steps. The default value is ten times nb_previous_steps.
  • --max_dense_schur (real) => the maximum allowed size for the dense factorization. If the Schur complement is larger than this value, then the dense factorization is not performed and an error is returned by the solver. The default value is 20,000.
  • --nb_threads (integer) => the number of threads used. This parameter should be at least 1. The default value is 1.
  • --extra_space (real) => the factor of the memory initially allocated for the solver in comparison with the size of the input matrix. The default value is 3.
  • --trace => activates a creation of a trace of the execution of the algorithm in Pajé format. By default this option is deactivated.
  • --verbosity (integer) => Controls the verbosity level. If the value is smaller and equal then 0, no otput is produced. If it is
    1 a general output is printed. If it is larger then one, extra information about each is step is given. Additionaly, plot, data and fig directories are created and a data file is writen, prefixed by the parameters of the algorithm, for each run. A script is created in the plot directory that if compiled with gnuplot will create a graph of the execution time in the f ig directory.

Using the ParSHUM interface

In this subsection we will explain how to use the interface of the ParSHUM library. The source code for the test driver presented previously is presented next:

main(int argc, char **argv)
  ParSHUM_solver solver;
  ParSHUM_vector X, B;
  /* Create the solver */
  solver = ParSHUM_solver_create();   

  /*  Parse the arguments */
  ParSHUM_solver_parse_args(solver, argc, argv);
  /*  Read the matrix */

  /*  Initialize the vectors */
  X = ParSHUM_vector_create(solver->A->n);
  B = ParSHUM_vector_create(solver->A->n);
  ParSHUM_vector_read_file(solver, X);

  /* copy the vector B in X */
  ParSHUM_vector_copy(B, X);
  /*  Initialize the solver */

  /*  Perform the factorization */

  /*  Perform the solve operation */
  ParSHUM_solver_solve(solver, B);

  /*  Compute the norms */
  ParSHUM_solver_compute_norms(solver, X, B);

  /*  Finalize the solver */

  /*  Free all the data */

  return 0;

First the solver structure is created by calling ParSHUM_solver_create in line 7. This will only allocate the basic data for the solver. Then on line 10, the arguments are processed and the solver’s parameters are set to the values provided. The matrix is then read from the file followed by the initialisation of the two vectors B and X. Once everything is in place, the function ParSHUM_solver_init is called which will allocate all the internal data that is needed for the factorization. The factorization is performed by the function ParSHUM_solver_factorize on line 25, followed by the solve operation on line 28 and 3the computation of the norms on line 31. The ParSHUM_solver_finalize needs to be called before the data is freed. This function finalises the PLASMA library, creating the trace file if demanded and prints the output of the solver. Finally, all the data that has been used is freed (lines 37-39).

Alternatively, is possible for the user to control the input matrix and parameters for the solver directly in the code and we now explain how it can be done. In order to change the input matrix and parameters, we use the ParSHUM_solver structure. The only fields that should be modified by the user are the input matrix A and the exe_parms field. The ParSHUM_solver is presented next:

typedef struct _ParSHUM_solver {
  ParSHUM_matrix   A;  /* The input matrix */

  /* The factors */
  ParSHUM_L_matrix L;   
  ParSHUM_matrix   D;
  ParSHUM_U_matrix U;

  /* The Schur complement */
  ParSHUM_schur_matrix S;

  ParSHUM_exe_parms exe_parms;
  ParSHUM_verbose verbose;
} * ParSHUM_solver; 

In the first example the input matrix was loaded by calling the function ParSHUM_solver_read_matrix. The other option is to set it as the input matrix in the ParSHUM_solver structure. We present the ParSHUM_matrix structure and a code example on how to assign a CSC matrix as the input matrix:

typedef  enum _ParSHUM_matrix_type
} ParSHUM_matrix_type;

typedef struct _ParSHUM_matrix {
  ParSHUM_matrix_type type;
  int n;

  long allocated;
  long nz;

  int *row;
  long *col_ptr;
  double *val;

  int *col;
  long *row_ptr;

  /* This is used by the SPRAL matrix driver since it is a Fortran code (see */
  void *handle;
} * ParSHUM_matrix;

   /* An example for assigning a CSC matrix as an input matrix. 
   The following code should replace the line 12 in the previous example. */

   ParSHUM_matrix matrix = calloc(1, sizeof(struct _ParSHUM_matrix);
   matrix->type = ParSHUM_CSC_matrix;
   matrix->n = n;
   matrix->allocated = nz;
   matrix->nz = nz;
   /* If the input matrix is a CSR matrix, the field type should be assigned with ParSHUM_CSR_matrix and instead of assigning arrays to the row and col_ptr fields, the col and row_ptr fields should be assigned. */
   matrix->row = row;
   matrix->col_ptr = col_ptr;
   solver->A = matrix;

In the first example, the solver is parametrized by calling the function ParSHUM_solver_parse_args. The other option is to modify directly the exe_parms field of the solver structure. The exe_parms field is of type ParSHUM_exe_parms and an example of how to use this structure to change the threshold value and Markowitz threshold. All the other parameters could be changed in the same way.

typedef struct ParSHUM_exe_parms {
  double value_tol;
  double marko_tol;
  double extra_space;
  double density_tolerance;

  char *matrix_file;
  char *RHS_file;

  int min_pivot_per_steps;
  int nb_threads;
  int nb_previous_pivots;
  int max_dense_schur;
  int trace;
} *ParSHUM_exe_parms;

   /* A code example for changing the parameters of the solver */ 
   /* The following code should replace the call the ParSHUM_solver_parse_args (line 10). */
   solver->exe_parms->value_threshold = 0.001;
   solver->exe_parms->marko_threshold = 16;

Once the matrix and the parameters are set, the solver should be initialised by calling the ParSHUM_solver_init. From this point the ParSHUM_solver structure should not be accessed or modified directly by the user.

Getting optimal performance with ParSHUM

We strongly recommend that on NUMA machines, ParSHUM should be binded to a single NUMA node. This algorithm is highly dependent on the bandwidth of the machine and not on the computational power To do so, one way is to use the numactl command. For example:

$ numactl --cpunodebind=0 --membind=0 ./bin/ParSHUM_simple <arguments>

will bind the process to the cores and the memory of NUMA node 0. Although internally in ParSHUM we bind the threads through the proc_bind option of the omp pragma, we have observed that this argument is ignored for some OpenMP imple- mentations. This could be also achieved through the OMP_PROC_BIN D environment variable. Additionally, 10% to 20% could be gained by setting the OMP_WAIT_POLICY environment variable to ACTIVE. Here is an example for an optimal run:

$ export OMP_PROC_BIND=close
$ numactl --cpunodebind=0 --membind=0 ./bin/ParSHUM_simple <arguments>


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