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# Parallelization of ground state calculations¶

## Explore the k-points/plane waves/bands parallelization¶

This tutorial discusses how to perform ground-state calculations on hundreds/thousands of computing units (CPUs) using ABINIT.

You will learn how to use some keywords related to the “KGB” parallelization scheme where “K” stands for “k-point”, “G” refers to the wavevector of a planewave, and “B” stands for a “Band”. It is possible to use ABINIT with other levels of parallelism but this is not the focus of this tutorial. You will learn how to speedup your calculations and how to improve their convergence rate.

This tutorial should take about 1.5 hour and requires access to at least a 200 CPU core parallel computer.

You are supposed to know already some basics of ABINIT. Some useful references: [Levitt2015], [Bottin2008], [Knyazev2001]

Note

Supposing you made your own install of ABINIT, the input files to run the examples are in the ~abinit/tests/ directory where ~abinit is the absolute path of the abinit top-level directory. If you have NOT made your own install, ask your system administrator where to find the package, especially the executable and test files.

To execute the tutorials, create a working directory (Work*) and copy there the input files and the files file of the lesson. This will be explicitly mentioned in the first lessons, that will tell you more about the files file (see also section 1.1). The files file ending with _x (e.g. tbase1_x.files) must be edited every time you start to use a new input file.

Most of the tutorials do not rely on parallelism (except specific tutorials on parallelism). However you can run most of the tutorial examples in parallel, see the topic on parallelism.

In case you work on your own PC or workstation, to make things easier, we suggest you define some handy environment variables by executing the following lines in the terminal:

export ABI_HOME=Replace_with_the_absolute_path_to_the_abinit_top_level_dir
export PATH=$ABI_HOME/src/98_main/:$PATH
export ABI_TESTS=$ABI_HOME/tests/ export ABI_PSPDIR=$ABI_TESTS/Psps_for_tests/  # Pseudopotentials used in examples.


Examples in this tutorial use these shell variables: copy and paste the code snippets into the terminal (remember to set ABI_HOME first!). The ‘export PATH’ line adds the directory containing the executables to your PATH so that you can invoke the code by simply typing abinit in the terminal instead of providing the absolute path.

## 1 Introduction¶

Before continuing you might work in a different subdirectory, as for the other tutorials. Why not work_paral?

All the input files can be found in the Input directory in the directory dedicated to this tutorial. You might have to adapt them to the path of the working directory. You can compare your results with reference output files located in Refs.

In the following, when “run ABINIT over nn CPUs” appears, you have to use a specific command line or submission file, according to the operating system/architecture of your computer.

When the size of the system increases up to 100 or 1000 atoms, it is usually impossible to perform ab initio calculations with a single computing core. This is because the basis sets used to solve the problem (plane waves, bands, …) increase — linearly, as the square, or even exponentially —. The computational resources are limited by 2 factors:

• The memory, i.e. the amount of data stored in RAM,
• The computing efficiency, with specific bottlenecks.

Therefore, it is mandatory to adopt a parallelization strategy:

1. Distribute the data or share them on a large number of computing nodes,
2. Parallelize the time consuming routines.

In this tutorial, we will show:

• How to improve performance by using a large number of computing units (CPU cores),
• How to decrease the computational time for a given number of CPU cores by…

1. Reducing the time needed to perform one electronic iteration (improve efficiency)
2. Reducing the number of electronic iterations (improve convergence)

The tests are performed on a 108 gold atom system; in this tutorial the plane-wave cutoff energy is strongly reduced, for practical reasons.

## 2 A simple way to begin: automatic distributed parallelism¶

The easiest way to activate the KGB parallelization in ABINIT is to add just one input variable in the input file, paral_kgb, which controls everything concerning the KGB parallelization, including the choice of the iterative eigensolver (wfoptalg = 1, 4, 14, 114) and the use of a parallel 3dim-FFT. Then you have to choose between 2 strategies:

• Activate the — not so good — flag autoparal=1 (automatic parallelization) and use the associated max_ncpus variable (maximum number of CPU cores you want),

or

• Manually define the number of processes associated to each level of parallelism: npkpt (number of processes for k points), npband (number of processes for bands), npfft (number of processes for plane-waves/FFT).

OK, let’s start! Copy the tgspw_01.in file and the related tgspw_01.files from the tutorial directory into your working directory.

Then run ABINIT on 1 CPU core (using 1 MPI process and 1 openMP thread).

ABINIT should stop without starting a calculation (don’t pay attention to the error message). At the end of the log file *log, you will see:

 Computing all possible proc distributions for this input with #CPUs<=108:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|       npkpt|       npfft|      npband|      bandpp|  #MPI(proc)|    WEIGHT|
|    1<<    1|    1<<   22|    1<<  108|    1<<  648|    1<<  108|  <=   108|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|           1|           6|          18|          12|         108|    74.762|
|           1|           9|          12|          18|         108|    74.530|
|           1|          12|           9|          24|         108|    73.687|
|           1|          18|           6|          36|         108|    73.560|
|           1|           4|          27|           8|         108|    73.037|
|           1|           3|          36|           6|         108|    70.188|
|           1|          12|           9|          18|         108|    70.065|
|           1|           4|          27|           6|         108|    69.448|
|           1|           8|          12|          18|          96|    67.032|
|           1|          16|           6|          36|          96|    65.931|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Only the best possible choices for nproc are printed...
......................


A weight is assigned to each distribution of processors. As indicated, you are advised to select a processor distribution with a high “weight”“. If we just focus on npband and npfft, we see that, for 108 processes, the recommended distribution is (18x6).

In a second step you can launch ABINIT in parallel on 108 processors by changing your input file as follows:

- paral_kgb 1 autoparal 1 max_ncpus 108
+ paral_kgb 1 npband 18 npfft 6


You can now perform your calculations using the KGB parallelization in ABINIT. But somehow, you did it without understanding how you got the result…

## 3 A more sophisticated method¶

In this part we will try to recover the previous process distribution, but with a better understanding. As shown above, the pair (npband x npfft) of input variables can have various values: (108x1), (54x2), (36x3), (27x4), (18x6), (12x9) or (9x12). In order to perform these 7 calculations you can use the tgspw_02.in and tgspw_02.files files.

Change the line corresponding to the processor distribution. A first calculation with:

+ npband 108 npfft 1


A second one with:

- npband 108 npfft 1
+ npband  54 npfft 2


And so on, using (36x3), (27x4), (18x6), (12x9) and (9x12).

Alternatively, this can be performed using a shell script including:

cp tgspw_02.in tmp.file
echo "npband 108 npfft 1" >> tgspw_02.in
mpirun -n 108 abinit < tgspw_02.files > log
cp tgspw_02.out tgspw_02.108-01.out
cp tmp.file tgspw_02.in
echo "npband 54 npfft 2" >> tgspw_02.in
...


Store all the output files by renaming them as follows: tgspw_02.108-01.out, tgspw_02.054-02.out, tgspw_02.036-03.out, tgspw_02.027-04.out, tgspw_02.018-06.out, tgspw_02.012-09.out and tgspw_02.009-12.out. The timing of each calculation can be retrieved using the shell command:

grep Proc *02*out


tgspw_02.009-12.out:- Proc.   0 individual time (sec): cpu=         66.1  wall=         67.8
tgspw_02.012-09.out:- Proc.   0 individual time (sec): cpu=         63.9  wall=         65.6
tgspw_02.018-06.out:- Proc.   0 individual time (sec): cpu=         61.1  wall=         62.6
tgspw_02.027-04.out:- Proc.   0 individual time (sec): cpu=         59.0  wall=         60.8
tgspw_02.036-03.out:- Proc.   0 individual time (sec): cpu=         60.6  wall=         62.3
tgspw_02.054-02.out:- Proc.   0 individual time (sec): cpu=         63.1  wall=         64.8
tgspw_02.108-01.out:- Proc.   0 individual time (sec): cpu=         74.4  wall=         76.0


As far as the timing is concerned, the best distributions are then the ones proposed in section 2; (27x4) seems to be the best one. The prediction using autoparal=1 was pretty good.

Up to now, we have not learned more than before. We have so far only considered the timing of 10 electronic steps. However the “Locally Optimal Block Preconditioned Conjugate Gradient” algorithm (LOBPCG) — used in ABINIT by default — operates a diagonalization by block of eigenvectors. Each block of eigenvectors is concurrently diagonalized by the npband processes, one block after the other. When the npband value is modified, the size of the block changes accordingly (it is exactly equal to npband), and the solutions of the eigensolver are modified. One calculation can be the quickest if we look at the time needed by one iteration but the slowest at the end because many more steps are performed. In order to see this, we can have a look at the convergence rate at the end of the calculations. The last iterations of the SCF loops are:

grep "ETOT 10" *02*out
tgspw_02.009-12.out: ETOT 10  -4191.7097103563     3.739E-04 1.171E-05 6.693E+00
tgspw_02.012-09.out: ETOT 10  -4191.7096733078     2.111E-04 1.702E-05 6.999E+00
tgspw_02.018-06.out: ETOT 10  -4191.7102597852     1.384E-04 2.298E-05 4.033E+00
tgspw_02.027-04.out: ETOT 10  -4191.7107673046    -6.372E-06 1.772E-05 1.947E+00
tgspw_02.036-03.out: ETOT 10  -4191.7111202227    -1.791E-04 1.875E-05 1.097E+00
tgspw_02.054-02.out: ETOT 10  -4191.7113973688    -2.561E-04 1.181E-05 3.156E-01
tgspw_02.108-01.out: ETOT 10  -4191.7114913729    -4.742E-05 1.646E-05 3.480E-02


The last column indicates the convergence of the potential residual. You can see that this quantity is the smallest when npband is the highest. This result is expected: the convergence is better when the size of the block is the largest. But this (best) convergence is obtained for the (108x1) distribution… when the worst timing is measured.

So, you face a dilemma. The calculation with the smallest number of iterations (the best convergence) is not the best concerning the timing of one iteration (the best efficiency), and vice versa… The best choice is a compromise.

In the following we will choose the (27x4) pair, because it definitively offers more guarantees concerning the convergence and the timing.

Note: You could check that the convergence is not changed when the npfft value is modified.

## 4 Even more sophisticated: BANDs Per Process (bandpp)¶

We have seen in the previous section that the best convergence is obtained when the size of the block is the largest. This size was exactly equal to the npband value. It was only possible to increase the block size by increasing the number of MPI processes.

Is it possible to do better? The answer is yes! The input variable named bandpp (BANDs Per Process) enables an increasing of the block size without changing the number of processes dedicated to bands.

How does this work? As previoulsy, each block of bands is diagonalized by npband MPI processes in parallel. But, if bandpp is activated, each process handles bandpp bands (sequentially). The block size — exactly equal to the number of bands handled by the band processes — is now equal to npband x bandpp. Accordingly the block size can be modified (usually increased) by playing with the value of bandpp, without changing the number of MPI processes. Note also that scalar products and FFTs are done by the npfft MPI processes.

In the following we use the same settings as previously, just changing:

+ nstep 20
+ paral_kgb 1 npband 27 npfft 4 bandpp 1


Copy the input files tgspw_03.files and tgspw_03.in then run ABINIT over 108 CPUs, setting bandpp=1 and then bandpp=2. The output files will be named tgspw_03.bandpp1.out and tgspw_03.bandpp2.out, respectively. A comparison of these two files shows that the convergence is better in the second case. Conclusion: for a given number of processors, it is possible to improve the convergence by increasing bandpp.

We can also compare the (27x4)+bandpp=2 distribution with the (54x2)+bandpp=1 one. Use the same input file and change it according to:

- paral_kgb 1 npband 27 npfft 4 bandpp 2
+ paral_kgb 1 npband 54 npfft 2 bandpp 1


Then run ABINIT over 108 CPUs and name the output file tgspw_03.054-02.out. Perform a diff between the two output files tgspw_03.bandpp1.out and tgspw_03.054-02.out. As you can see, the two calculations give exactly the same convergence rate. This was expected since, in both cases, the block sizes are equal (to 54) and the number of FFT processors npfft does not affect the convergence.

Tip

It is possible to adjust the distribution of processes, without changing the convergence, by reducing npband and increasing bandpp proportionally.

However, as you can see in the previous calculations, the CPU time per iteration increases when bandpp increases (note that the 2nd run performed less iterations than the first one):

grep Proc tgspw_03.bandpp1.out tgspw_03.bandpp2.out
tgspw_03.bandpp1.out:- Proc.   0 individual time (sec): cpu=         97.4  wall=         99.3
tgspw_03.bandpp2.out:- Proc.   0 individual time (sec): cpu=         96.3  wall=         98.2


Where does this CPU time consumption come from? As previously explained, each MPI processes handles bandpp bands sequentially. Thus the sequential part of the code increases when bandpp increases. So, bandpp>1 is usually mandatory but do not increase it too much; do it only if you want to improve the convergence rate whatever the cost in total timing.

We will see in the next section how the use of hybrid parallelism can improve this…

Important

Using only MPI parallelism, the timing of a single electronic step increases when bandpp increases but the convergence rate is better.

Tip

The only exception is when istwfk = 2, i.e. when the wavefunctions are real. This occurs when only the Γ point is used in the Brillouin Zone. For even values of bandpp, the real wavefunctions are associated in pairs in the complex FFTs, leading to a reduction by a factor of their cost. When calculations are performed at Γ point you are strongly encouraged to use bandpp=2, 4,... (even).

## 5 Hybrid parallelism: MPI+openMP¶

In modern supercomputers, the computing units (CPU cores) are no more equally distributed. They are grouped by nodes in which they share the same memory access. In so-called many-core architecture CPU cores can be numerous on the same node. You could continue to use them as if they were not sharing the memory (using MPI only) but this is not the most efficient way to take benefit from the computer. The best practice is to used hybrid parallelism, mixing distributed memory parallelism (MPI, between nodes) and shared memory parallelism ( openMP, inside a node). As you will see, this will also have consequences on the performance of the iterative diagonalization algorithm (LOBPCG). Let’s try!

We are going to run ABINIT using 2 threads. Copy the tgspw_04.files and tgspw_04.in files and run ABINIT using 54 MPI processes and 2 openMP threads (by setting OMP_NUM_THREADS=2 in your environment). Note: 54MPI x 2threads = 108 CPU cores.

Important note: When using threads, we have to impose npfft=1. The best is to suppress it from the input file.

Let’s have a look at the timings and compare them to the same run without openMP threads:

grep Proc tgspw_03.bandpp2.out tgspw_04.bandpp2.2threads.out
tgspw_03.bandpp2.out:         - Proc.   0 individual time (sec): cpu=         96.3  wall=         98.2
tgspw_04.bandpp2.2threads.out:- Proc.   0 individual time (sec): cpu=        147.9  wall=        148.8


As you can wee, the new output file show a larger computing time for process 0: disappointing? Not really: you have to keep in mind that this timing is for one MPI process, adding the timings of all the openMP tasks for this process. In the pure MPI case, we thus have 96 sec. per task; but in the hybrid case, we have 148/2=74 sec. per task. For the total 108 CPUS, the time used by ABINIT is 96x108=10368 sec. in the MPI case, 74*108=7992 sec. in the hybrid case. This is better! The best way to confirm that is to look at the Wall Time (cumulated on all tasks) at the end of the output file:

grep Overall tgspw_03.bandpp2.out tgspw_04.bandpp2.2threads.out
tgspw_03.bandpp2.out:         +Overall time at end (sec) : cpu=      10429.3  wall=      10607.3
tgspw_04.bandpp2.2threads.out:+Overall time at end (sec) : cpu=       8003.9  wall=       8033.4


How does ABINIT distribute the workload? Each block of bands is diagonalized by npband MPI processes in parallel. As previously, each process handles bandpp bands but now using the openMP tasks. This means that bandpp x npband bands are computed in parallel using nthreads x npband tasks (bandpp has thus to be a multiple of nthreads). This is in principle more efficient than in the pure MPI case. Scalar products and FFTs are done in parallel by the openMP tasks (npfft not used). So, note that there are subtle differences with the pure MPI case.

Important note: When using threads, bandpp has to be a multiple of the number of threads.

How do we choose the number of threads? It strongly depends on the computer architecture!

A computer is made of nodes. On each node, there are sockets containing a given number of CPU cores. All the cores of the node can access the RAM of all the sockets but this access is faster on their own socket. This is the origin of the famous Non Uniform Memory Access effect (NUMA). The number of threads has thus to be a divisor of the total number of CPU cores in the node, but it is better to choose a divisor of the number of cores in a socket. Indeed ABINIT performance is very sensitive to NUMA effect.

In the following, let’s learn how to use ABINIT on 6 openMP threads… First we change the input file as follows:

- paral_kgb 1 npband 54 bandpp 2
+ paral_kgb 1 npband 18 bandpp 6


Then we run ABINIT using 18 MPI processes and 6 threads (still 108 CPUs). And we compare the timing of this “6 threads” case with the “2 threads” case:

grep Overall tgspw_04.bandpp2.2threads.out tgspw_04.bandpp6.6threads.out
tgspw_04.bandpp2.2threads.out:+Overall time at end (sec) : cpu=       8003.9  wall=       8033.4
tgspw_04.bandpp6.6threads.out:+Overall time at end (sec) : cpu=       5452.4  wall=       5442.8


We again have improved ABINIT performances!

Can we do better? In principle, yes. As previously explained, we have to increase the block size for the LOBPCG diagonalization algorithm. Let’s try it, just changing the bandpp value in input file:

- paral_kgb 1 npband 54 bandpp 2
+ paral_kgb 1 npband 18 bandpp 12


We don’t change here the number of threads (keeping 6). And we obtain the following timings:

grep Overall tgspw_04.bandpp6.6threads.out tgspw_04.bandpp12.6threads.out
tgspw_04.bandpp6.6threads.out: +Overall time at end (sec) : cpu=       5452.4  wall=       5442.8
tgspw_04.bandpp12.6threads.out:+Overall time at end (sec) : cpu=       6426.2  wall=       6414.9


The new settings do not give a better result…

To help you in choosing the distribution of processes/tasks, you can launch ABINIT with the autoparal=1 and max_ncpus keywords. max_ncpus should be equal the total number of targeted CPU cores, i.e. nthreads x nMPI and you should launch ABINIT on 1 MPI process with OMP_NUM_THREADS=nn. You can try this with max_ncpus=108 and OMP_NUM_THREADS=6

Tip

The rules to distribute the workload are:

• npband x bandpp (size of a block) should be maximalized. It has to divide the number of bands (nband)
• bandpp has to be a multiple of the number of openMP tasks
• nband has to be a multiple of npband x bandpp. Using autoparal, the code gives you some good values for nband.
• In any case, the ideal distribution is system dependent!

## 6 The KGB parallelization¶

Up to now, we only performed a “GB” parallelization, using 2 levels (bands and/or FFT). If the system has more than 1 k-point, one can add a third level of parallelization and perform a full “KBG” parallelization. There is no difficulty in adding processes to this level.

To test the full parallelism, we restart with the same input file as in section 3 and add a denser k-point grid. In this case, the system has 4 k-points in the irreducible Brillouin zone (IBZ) so the calculation can be parallelized over (at most) 4 k-points MPI processes. This is done using the npkpt input variable:

- paral_kgb 1 npband 27 npfft 4 bandpp 1
+ paral_kgb 1 npkpt 4 npband 27 npfft 4 bandpp 1


We need 4 times more processes than before, so run ABINIT over 432 CPU cores (only MPI) with the tgspw_05.in and tgspw_05.files files. The timing obtained in the output file tgspw_05.out

grep Proc tgspw_05.out
- Proc.   0 individual time (sec): cpu=         69.8  wall=         71.9


is quasi-identical to the one obtained for 1 k-point (see tgspw_02.027-04.out). This means that the scalability of ABINIT is quasi-linear on the k-point level.

Important

When you want to parallelize a calculation, begin by the k-point level, then follow with the band level; then activate the FFT level or openMP threads.

Here, the timing obtained for the output tgspw_05.out leads to a hypothetical speedup of 346, which is good, but not 432 as expected if the scaling was linear. Indeed, the time needed here is slightly longer (10 sec. more) than the one obtained in tgspw_02.027-04.out. To go further, let’s compare the time spent in all the routines. A first clue comes from the timing done outside the “vtowfk level”, which contains 99% of sequential processor time:

grep "vtowfk   " tgspw_05.out tgspw_02.027-04.out
tgspw_05.out:       - vtowfk   21028.382  68.3  21042.797  67.9  4320
tgspw_05.out:       - vtowfk   21028.382  68.3  21042.797  67.9  4320
tgspw_02.027-04.out:- vtowfk    5154.681  80.8   5155.008  78.6  1080
tgspw_02.027-04.out:- vtowfk    5154.681  80.8   5155.008  78.6  1080


We see that the KGB parallelization performs really well, since the wall time spent within vtowfk is approximatively equal: $21028/432\approx 5154/108$. So, the speedup is quasi-linear in vtowfk. The problem comes from parts outside vtowfk which are not parallelized and are responsible for the negligible (1-99.xyz)% of time spent in sequential. These parts are no longer negligible when you parallelize over hundreds of processors. The time spent in vtowfk corresponds to 84.8% of the overall time when you don’t parallelize over k-points, and only 70.7% when you parallelize.

This behaviour is related to the Amdhal’s law:

The speedup of a program using multiple processes in parallel computing is limited by the time needed for the sequential fraction of the program.

For example, if a program needs 20 hours using a single processor core, and a particular portion of 1 hour cannot be parallelized, while the remaining portion of 19 hours (95%) can be parallelized, then regardless of how many processor cores we have, the minimum execution time cannot be less than that critical 1 hour. Hence the speedup is limited to 20.

In our case, the part above the loop over k-points in not parallelized by the KGB parallelization. Even if this part is very small — less than 1% — it determines an upper bound for the speedup.

To do in the future: discuss convergence, wfoptalg, nline