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HPC Best Practices Webinar Series

Algorithms and performance portability for electroni structure

Speeding up electronic structure calculations to ena

Running MD on exascale platforms

Main numerical kernels for electronic structure calculations

Developing alternative solvers based on polynomial matrices

Implementation divided into two libraries

Using OpenMP for GPU offloading

General implementation strategy

Computer Science challenges

BML: supported (shared memory) matrix formats

BML: Supporting multiple data types in a C code

BML: Fortran interface is important for targeted application codes

BML: Unit test/Continuous integration

Offloading to GPU

Offloading strategy

GPU offloading with OpenMP

Challenges in interfacing with optimized vendor libra

Using a synthetic Hamiltonian matrix for Performanc Benchmarking

rocSPARSE performance on Crusher @ OLCF

Chebyshev expansions for modest matrix sizes (metals)

Chebyshev expansion of Density Matrix

Exploiting GPU concurrency in calculating Chebysh terms

Distributing computation

Balancing computational cost and accuracy with matrix thresholding

A non-intrusive implementation

What about wavefunction-based solver? (Planewaves...)

Numerical Discretization of DFT problem

Parallel scaling/performance on Summit

Lesson learned: Efficiently using GPUs requires a lo work!

Acknowledgments

Description:

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the challenges and solutions for porting electronic structure codes to exascale platforms in this webinar from the Exascale Computing Project. Dive into the implementation strategies and algorithmic choices made in the PROGRESS and BML libraries developed by ECP's CoPA project. Learn about performance portability, strong scaling, and GPU offloading techniques using OpenMP. Discover how to speed up electronic structure calculations and run molecular dynamics simulations on exascale platforms. Examine the main numerical kernels for electronic structure calculations and the development of alternative solvers based on polynomial matrices. Gain insights into computer science challenges, matrix formats, and the importance of Fortran interfaces for targeted application codes. Explore benchmarking strategies, performance optimization on GPU-based systems, and the use of Chebyshev expansions for density matrix calculations. Understand the balance between computational cost and accuracy through matrix thresholding, and examine parallel scaling performance on Summit supercomputer. Conclude with valuable lessons learned about efficiently utilizing GPUs for electronic structure computations on exascale platforms. Read more

Facilitating Electronic Structure Computations on GPU-based Exascale Platforms

Exascale Computing Project

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#Computer Science #High Performance Computing #Parallel Computing #GPU Computing #OpenMP #Mathematics #Algebra #Linear Algebra #Matrix Operations #Science #Chemistry #Computational Chemistry #Physics #Quantum Mechanics #Quantum Chemistry #Density Functional Theory #Exascale Computing

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