# Quickstep Density Matrix Linear Scaling

## Description

This is a single-point energy calculation using linear-scaling DFT.

For large systems the linear-scaling approach for solving Self-Consistent-Field
equations will be much cheaper computationally than using standard DFT and allows
scaling up to 1 million atoms for simple systems. The linear scaling cost results
from the fact that the algorithm is based on an iteration on the density matrix.
The cubically-scaling orthogonalisation step of standard Quickstep DFT using OT
is avoided and the key operation is sparse matrix-matrix multiplications, which
have a number of non-zero entries that scale linearly with system size.
These are implemented efficiently in the DBCSR library.

The problem size can be tuned by the parameter `NREP` in the input file, whereby
the number of atoms scales cubically with `NREP`.

## Files Description

- [H2O-dft-ls.inp](H2O-dft-ls.inp) (NREP=6): H20 density functional theory
  linear scaling consisting of 20'736 atoms in a 59 cubic angstrom box (6'912
  water molecules in total). An LDA functional is used with a DZVP MOLOPT basis
  set and a 300 Ry cut-off.
- [H2O-dft-ls.NREP4.inp](H2O-dft-ls.NREP4.inp): H20 density functional theory
  linear scaling consisting of 6'144 atoms in a 39 cubic angstrom box (2'048
  water molecules in total). An LDA functional is used with a DZVP MOLOPT basis
  set and a 300 Ry cut-off.
- [H2O-dft-ls.NREP2.inp](H2O-dft-ls.NREP2.inp): H20 density functional theory
  linear scaling consisting of 6'144 atoms in a 39 cubic angstrom box (2'048
  water molecules in total). An LDA functional is used with a DZVP MOLOPT basis
  set and a 300 Ry cut-off (a smaller version of the H2O-dft-ls benchmark, with
  NREP=2, meant to run on 1 node).
- [TiO2.inp](TiO2.inp)
- [amorph.inp](amorph.inp)

## Results

### NREP=4

The best configurations are shown below.
Click the links under "Detailed Results" to see more detail.

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| Machine Name | Architecture | Date       | SVN Revision | Fastest time (s) | Number of Cores | Number of Threads                  | Detailed Results |
| ------------ | ------------ | ---------- | ------------ | ---------------- | --------------- | ---------------------------------- | ---------------- |
| HECToR       | Cray XE6     | 16/1/2014  | 13196        | 98.256           | 65536           | 8 OMP threads per MPI task         | [hector-h2o-dft-ls](https://www.cp2k.org/performance:hector-h2o-dft-ls) |
| ARCHER       | Cray XC30    | 8/1/2014   | 13473        | 28.476           | 49152           | 4 OMP threads per MPI task         | [archer-h2o-dft-ls](https://www.cp2k.org/performance:archer-h2o-dft-ls) |
| Magnus       | Cray XC40    | 3/12/2014  | 14377        | 30.921           | 24576           | 2 OMP threads per MPI task         | [magnus-h2o-dft-ls](https://www.cp2k.org/performance:magnus-h2o-dft-ls) |
| Piz Daint    | Cray XC30    | 12/05/2015 | 15268        | 27.900           | 32768           | 2 OMP threads per MPI task, no GPU | [piz-daint-h2o-dft-ls](https://www.cp2k.org/performance:piz-daint-h2o-dft-ls) |
| Cirrus       | SGI ICE XA   | 24/11/2016 | 17566        | 543.032          | 2016            | 2 OMP threads per MPI task         | [cirrus-h2o-dft-ls](https://www.cp2k.org/performance:cirrus-h2o-dft-ls) |
| Noctua       | Cray CS500   | 25/09/2019 | 9f58d81      | 37.730           | 10240           | 10 OMP threads per MPI task        | [noctua-h2o-dft-ls](https://www.cp2k.org/performance:noctua-h2o-dft-ls) |
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### Weak Scaling on Piz Daint, CSCS

Following results were obtained in the following conditions:

- Date: 15th November 2019
- CP2K version: version 7.0 (Development Version, git:78cea8eeebb25e459941d8a28d987c9990d92676)
- DBCSR version: v2.0.0-rc9 (git:15fdaba855385f12db7599a6e69b51a7a4ce8a9a)
- CP2K flags: omp libint fftw3 libxc elpa parallel mpi3 scalapack acc pw_cuda
  xsmm dbcsr_acc max_contr=4
- Machine: Piz Daint (GPU partition), CSCS
- Slurm configuration: 2 MPI ranks per node, 12 OpenMP threads per MPI rank
- The cell contents specify the runtime (`grep 'CP2K    ' output.out`) in seconds,
  while the cells marked with an `X` crashed with out-of-memory errors, and the
  cells left empty weren't measured.

|  nodes / NREP | NREP=1 | NREP=2 | NREP=3 | NREP=4 | NREP=6 | NREP=8 | NREP=9 |
| ------------- | -----  | -----  | -----  | -----  | -----  | -----  | -----  |
|  1 node       |   7.4  |  60.3  |   X    |        |        |        |        |
|  2 nodes      |   7.4  |  35.0  | 269.4  |   X    |        |        |        |
|  4 nodes      |   9.9  |  22.7  | 149.8  |   X    |        |        |        |
|  6 nodes      |  12.1  |  19.7  | 113.0  |   X    |        |        |        |
|  8 nodes      |  11.4  |  16.4  |  90.2  | 253.4  |   X    |        |        |
| 12 nodes      |  15.5  |  21.7  |  71.5  | 193.8  |   X    |        |        |
| 16 nodes      |  15.5  |  20.8  |  61.5  | 159.2  |   X    |        |        |
| 24 nodes      |  22.0  |  24.7  |  51.8  | 130.2  |   X    |        |        |
| 32 nodes      |  15.9  |  20.4  |  42.8  | 101.8  | 352.9  |   X    |        |
| 36 nodes      |  21.9  |  25.6  |  44.0  |  99.8  | 333.0  |   X    |        |
| 48 nodes      |  24.5  |  34.1  |  42.0  |  84.1  | 277.9  |   X    |        |
| 64 nodes      |  24.9  |  29.0  |  40.4  |  79.7  | 257.5  |   X    |        |
| 128 nodes     |  26.3  |  32.8  |  36.6  |  62.5  | 181.9  | 400.6  |   X    |

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|  nodes / NREP | NREP=6 | NREP=8 | NREP=9 | NREP=10 | NREP=11 | NREP=12 | NREP=13 | NREP=14 | NREP=16 | NREP=18 | NREP=19 | NREP=20 |
| ------------- | -----  | -----  | -----  | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- |
|  256 nodes    |  132.6 |  262.3 |  359.2 |   498.8 |   647.1 |    X    |         |         |         |         |         |         |
|  512 nodes    |  106.0 |  212.5 |  290.2 |   409.2 |   534.0 |   732.3 |   875.2 |  1030.1 |    X    |         |         |         |
| 1024 nodes    |   98.1 |  168.9 |        |   284.7 |         |   510.8 |         |   786.5 |  1161.1 |  1607.3 |  1872.8 |    X    |
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