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4. Distributed Diagonaliser and Inverter
(Work Package 2)

4.1 Introduction

The first stage of the project included the distribution over the band-group of the calculation of the band-overlap matrices required for both orthonormalisation of the wavefunction and the subspace Hamiltonian. These matrices still need to be diagonalised (or inverted) and Castep 4.2 does this in serial. These operations scale as $N_b^3$ so as larger and larger systems are studied they will start to dominate. For this reason the second stage of the project involves distributing these operations over the nodes.

4.2 Programming

Because the aim of this stage of the project was to implement a parallel matrix diagonaliser and inverter, which is independent of most of Castep, we created a testbed program to streamline the development. The subspace diagonalisation subroutine in Castep uses the LAPACK subroutine ZHEEV, so we concentrated on that operation first. The testbed program creates a random Hermitian matrix, and uses the parallel LAPACK variant ScaLAPACK to diagonalise it.

4.3 ScaLAPACK Performance

The performance of the distributed diagonaliser (PZHEEV) was compared to that of the LAPACK routine ZHEEV for a range of matrix sizes.

Table 4.1: Hermitian matrix diagonalisation times for the ScaLapack subroutine PZHEEV.
  time for various matrix sizes
cores 1200 1600 2000 2400 2800 3200
1 19.5s 46.5s 91.6s 162.7s    
2 28.3s 65.9s 134.6s      
4 15.8s 38.2s 54.7s 90.1s    
8 7.9s 19.0s 37.6s 63.9s 81.6s  
16 4.3s 10.5s 20.3s 32.5s 76.2s  
32 2.7s 6.0s 11.6s 19.2s 43.1s  

An improved parallel matrix diagonalisation subroutine, PZHEEVR (The `R' is because it uses the Multiple Relatively Robust Representations (MRRR) method) , was made available to us by Christof Vömel (Zurich) and Edward Smyth (NAG). This subroutine consistently out-performed PZHEEV, as can be seen from figure 4.1.

Figure 4.1: A graph showing the scaling of the parallel matrix diagonalisers PZHEEV (solid lines with squares) and PZHEEVR (dashed lines with diamonds) with matrix size, for various numbers of cores (colour-coded)

The ScaLAPACK subroutines are based on a block-cyclic distribution, which allows the data to be distributed in a general way rather than just by row or column. The timings for different data-distributions for the PZHEEVR subroutine are given in table 4.2.

Table 4.2: PZHEEVR matrix diagonalisation times for a 2200x2200 Hermitian matrix distributed in various ways over 64 cores of HECToR.
Cores used for distribution of  
Rows Columns Time
1 64 6.48s
2 32 6.45s
4 16 5.80s
8 8 5.92s

The computational time $t$ for diagonalisation of a $N\times N$ matrix scales as $O(N^3)$, so we fitted a cubic of the form

t(N) = a + bN + cN^2 + dN^3
\end{displaymath} (4.1)

to these data for the 8-core runs. The results are shown in table 4.3. This cubic fit reinforces the empirical evidence that the PZHEEVR subroutines have superior performance and scaling with matrix size, since the cubic coefficient for PZHEEVR is around 20% smaller than that of the usual PZHEEV subroutine.

Table 4.3: The best-fit cubic polynomials for the PZHEEV and PZHEEVR matrix diagonalisation times for Hermitian matrices from $1000\times 1000$ to $3600\times 3600$ distributed over 8 cores of HECToR.
a -1.43547 -0.492901
b 0.00137909 0.00107718
c 9.0013e-08 -7.22616e-07
d 4.31679e-09 3.53573e-09

4.4 Castep Performance

With the new distributed inversion and diagonalisation subroutines the performance and scaling of Castep was improved noticeably. As expected, this improvement was more significant when using larger number of cores. Figure 4.2 shows the improved performance of Castep due to the distribution of the matrix inversion and diagonalisation in this work package.

Figure 4.2: Graph showing the performance and scaling improvement achieved by the distributed inversion and diagonalisation work in Work Package 2, compared to the straight band-parallel work from Work Package 1. Each calculation is using 8-way band-parallelism, and running the standard al3x3 benchmark.

The distributed diagonalisation, on top of the basic band-parallelism, enables Castep calculations to scale effectively to between two and four times more cores compared to Castep 4.2 (see figure 4.3). The standard al3x3 benchmark can now be run on 1024 cores with almost 50% efficiency, which equates to over three cores per atom, and it is expected that larger calculations will scale better. A large demonstration calculation is being performed that should illustrate the new Castep performance even better.

Figure 4.3: Comparison of Castep scaling for Work Packages 1 and 2 and the original Castep 4.2, for 10 SCF cycles of the al3x3 benchmark. Parallel efficiencies were measured relative to the 16 core calculation with Castep 4.2.
[num_proc_in_smp : 1]

[num_proc_in_smp : 2]

next up previous contents
Next: 5. Independent Band Optimisation Up: castep_performance_xt Previous: 3. Band-Parallelism (Work Package   Contents
Sarfraz A Nadeem 2008-09-03