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Preliminary CPMD Benchmarks

Preliminary CPMD Benchmarks. On Ranger, Pople , and Abe TG AUS Materials Science Project Matt McKenzie LONI. What is CPMD ?. Car Parrinello Molecular Dynamics www.cpmd.org Parallelized plane wave / pseudopotential implementation of Density Functional Theory

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Preliminary CPMD Benchmarks

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  1. Preliminary CPMD Benchmarks On Ranger, Pople, and Abe TG AUS Materials Science Project Matt McKenzie LONI

  2. What is CPMD ? • Car Parrinello Molecular Dynamics • www.cpmd.org • Parallelized plane wave / pseudopotential implementation of Density Functional Theory • Common chemical systems: liquids, solids, interfaces, gas clusters, reactions • Large systems ~500atoms • Scales w/ # electrons NOT atoms

  3. Key Points in Optimizing CPMD • Developers have done a lot of work here • The Intel compiler is used in this study • BLAS/LAPACK • BLAS levels 1 (vector ops) and 3 (matrix-matrix ops) • Some level 2 (vector-matrix) • Integrated optimized FFT Library • Compiler flag: -DFFT_DEFAULT

  4. Benchmarking CPMD is difficult because… • Nature of the modeled chemical system • Solids, liquids, interfaces • Require different parameters stressing the memory along the way • Volume and # electrons • Choice of the pseudopotential (psp) • Norm-conserving, ‘soft’, non-linear core correction (++memory) • Type of simulation conducted • CPMD, BOMD, Path Integral, Simulated Annealing, etc… • CPMD is a robust code • Very chemical system specific • Any one CPMD sim. cannot be easily compared to another • However, THERE ARE TRENDS • FOCUS: simple wave function optimization timing • This is a common ab initio calculation

  5. Probing Memory Limitations • For any ab initio calculation: • Accuracy is proportional to # basis sets used • Stored in matrices, requiring increased RAM • Energy cutoff determines the size of the Plane wave basis set, NPW = (1/2π2)ΩEcut3/2

  6. Model Accuracy & Memory Overview Image obtained from the CPMD user’s manual Pseudopotential’s convergence behavior w.r.t. basis set size (cutoff) NOTE: Choice of psp is important i.e. ‘softer’ psp = lower cutoff = loss of transferability VASP specializes in softpsp’s ; CPMD works with any psp’s

  7. Memory ComparisonΨoptimization, 63 Si atoms, SGS psp Ecut= 50 Ryd Ecut = 70 Ryd • NPW≈ 134,000 • Memory = 1.0 GB • NPW ≈ 222,000 • Memory = 1.8 GB Well known CPMD benchmarking model: www.cpmd.org Results can be shown either by: Wall time = (n steps x iteration time/step) + network overhead Typical Results / Interpretations, nothing new here Iteration time = fundamental unit, used throughout any given CPMD calculation It neglects the network, yet results are comparable Note, CPMD runs well on a few nodes connected with gigabyte ethernet Two important factor which affects CPMD performance MEMORY BANDWIDTH FLOATING-POINT

  8. Pople, Abe, Ranger CPMD Benchmarks

  9. Results I • All calculations ran no longer than 2 hours • Ranger is not the preferred machine for CPMD • Scales well between 8 and 96 cores • This is a common CPMD trend • CPMD is known to super-linearity scale above ~1000 processors • Will look into this • Chemical system would have to change as this smaller simulation is likely not to scale in this manner

  10. Results II • Pople and Abe gave the best performance • IF a system requires more than 96 procs, Abe would be a slightly better choice • Knowing the difficulties in benchmarking CPMD, ( psp, volume, system phase, sim. protocol ) this benchmark is not a good representation of all the possible uses of CPMD. • Only explored one part of the code • How each system performs when taxed with additional memory requirements is a better indicator of CPMD’s performance • To increase system accuracy, increase Ecut

  11. Percent Difference between 70 and 50 Ryd%Diff = [(t70-t50) / t50]*100

  12. Conclusions RANGER • Re-ran Ranger calculations • Lower performance maybe linked to Intel compiler on AMD chips • PGI compiler could show an improvement • Nothing over 5% is expected: still be the slowest • Wanted to use the same compiler/math libraries ABE • Possible super-linear scaling, tAbe, 256procs < tothers, 256procs • Memory size effects hinders performance below 96 procs POPLE • Is the best system for wave function optimization • Shows a (relatively) stable, modest speed decrease as the memory requirement is increased, it is the recommended system

  13. Future Work • Half-node benchmarking • Profiling Tools • Test the MD part of CPMD • Force calculations involving the non-local parts of the psp will increase memory • Extensive level 3 BLAS & some level 2 • Many FFT all-to-all calls, Now the network plays a role • Memory > 2 GB • A new variable ! Monitor the fictitious electron mass • Changing the model • Metallic system (lots of electrons, change of psp; Ecut) • Check super-linear scaling

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