/ParallelMatrixMultiplication

Parallel implementation of the matrix multiplication

Primary LanguageC

Parallel matrix multiplication

Given two $n$-dimensional square matrices $A, B \in\mathcal{M}_{n}(\mathbb{R})$ defined on the real field, it is know that the matrix multiplication $C=AB\in\mathcal{M}_n(\mathbb{R})$ is defined as

$$ [AB]_{i, j}=\sum_{r=1}^na_{i,r}b_{r, j} $$

and, a naive approach to implement such an operation in the programming language C (in which multidimensional arrays are stored row-major order), is the following:

for(i = 0; i < n; i++)
  for(j = 0; j < n; j++)
    for(k = 0; k < n; k++)
      C[i][j] += A[i][k] + B[k][j];

which is an example of a serial code.

Here, my goal is to implement a parallel code in C to perform such an operation given any number $m$ of computational units.

Important note: the code has been written to compile and run on the Marconi100 cluster at CINECA. To compile and run the Spectum_MPI, the cuda and the openablas modules must be loaded.

Compilation

To compile it is possible to use the command

make [version]

where [version] can be either blank, dgemm or cuda. This will produce the [version]multiplication.x executable (the name will depend on which version it has been compiled).

Execution

The executable will generate two $n\times n$ matrices with random entries and multiply them. To run, for example with 3 processes using $16\times 16$ matrices, it is possible either to use mpirun -np 3 ./[version]multiplication.x 16 or to use

make [version]run prc=3 dim=16

where [version] can be either blank, dgemm or cuda. This will produce the [version]multiplication.x executable (the name will depend on how it has been compiled) and run it.

Test

To test it is possible to pass the debug=yes flag to the Makefile

make [version] debug=yes

and then run the [version]debug_multiplication.x executable using mpirun. It is also supported the command:

make [version]run debug=yes

where [version] can be either blank, dgemm or cuda. This will compile (if necessary) and run immediately after.

To do list

These are the things done or to be done:

  1. Implement a working code using only MPI
  • Implement a working code when $n$ multiple of $m$
  • Add some testing
  • Implement a working code when $n$ generic
  • Measure performances
  1. Include a version using cblas_dgemm instead of the serial multiplication done by each MPI process
  • Make it work
  • Make some plots to compare performances with serial version
  1. Port on GPU
  • Include a version using cublasDgemm instead of the serial multiplication done by each MPI process
  • Make some plots to compare performances with serial and cblas_dgemm versions