9. Parallelization

9.1 OpenMP

9.1.1 - Parallelization with OpenMP

An easy way to parallelize our for loops is using

#pragma omp parallel for

example:

#pragma omp parallel for
for (unsigned short l_st = 0; l_st < 2; l_st++){
...

9.1.2 - Parallelization speedup

We have used following batch script for ara:

#!/bin/bash
#SBATCH --job-name=tohoku_1000
#SBATCH --output=tohoku_1000.out
#SBATCH --error=tohoku_1000.err
#SBATCH --partition=s_hadoop
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=10:00:00
#SBATCH --cpus-per-task=72

./build/tsunami_lab configs/tohoku1000.json

And got following results:

Without parallelization

Note: max 10520 steps will be computed.
entering time loop
finished time loop

Calculation time: 1.94101e+06ms
= 1941.01 seconds
= 32.3501 minutes

With parallelization on 72 cores with 72 threads

Note: max 10520 steps will be computed.
entering time loop
finished time loop

Calculation time: 75507.5ms
= 75.5075 seconds
= 1.25846 minutes

Note

We compiled in benchmark mode (no IO).

Speedup: \(\frac{1941}{75.5} = 25.7\)

With parallelization on 72 cores with 144 threads

Note: max 10520 steps will be computed.
entering time loop
finished time loop

Calculation time: 215822ms
= 215.822 seconds
= 3.59704 minutes

Speedup: \(\frac{1941}{215} = 8.99\)

We can see that having twice the amount of threads resulted in a much slower computation. We conclude that using more threads than cores results in a slowed down performance.

9.1.3 - 2D for loop parallelization

The results from above used parallelization in the outer loop. The parallelized inner loops leads to following time:

Calculation time: 791389ms
= 791.389 seconds
= 13.1898 minutes

It is clear, that parallelizing the outer loop is more effficient.

9.1.4 - Pinning and Scheduling

Scheduling

The upper implementation used the basic scheduling(static).

For scheduling(dynamic) we get:

Calculation time: 1.57024e+06ms
    = 1570.24 seconds
    = 26.1706 minutes

For scheduling(guided) we get:

Calculation time: 95218ms
    = 95.218 seconds
    = 1.58697 minutes

For scheduling(auto) we get:

Calculation time: 84546.7ms
    = 84.5467 seconds
    = 1.40911 minutes

Pinning

Using OMP_PLACES={0}:36:1 we get:

Calculation time: 1.39672e+06ms
    = 1396.72 seconds
    = 23.2786 minutes

Using OMP_PLACES={0,36}:18:1 we get:

Calculation time: 1.43714e+06ms
= 1437.14 seconds
= 23.9523 minutes

It shows that using the first strategy is more efficient for computation.