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Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System

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arxiv 1709.08785 v2 pith:OE7B46EJ submitted 2017-09-26 physics.comp-ph hep-lat

Taisuke Boku , Ken-Ichi Ishikawa , Yoshinobu Kuramashi , Lawrence Meadows This is my paper · ORCID
classification physics.comp-phhep-lat
keywords latticesolverquantumquarkssystemchromodynamicsdiscretizedequation
verification ladder T0 review T1 audit T2 compute T3 formal

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abstract

Lattice Quantum Chromodynamics (Lattice QCD) is a quantum field theory on a finite discretized space-time box so as to numerically compute the dynamics of quarks and gluons to explore the nature of subatomic world. Solving the equation of motion of quarks (quark solver) is the most compute-intensive part of the lattice QCD simulations and is one of the legacy HPC applications. We have developed a mixed-precision quark solver for a large Intel Xeon Phi (KNL) system named "Oakforest-PACS", employing the $O(a)$-improved Wilson quarks as the discretized equation of motion. The nested-BiCGSTab algorithm for the solver was implemented and optimized using mixed-precision, communication-computation overlapping with MPI-offloading, SIMD vectorization, and thread stealing techniques. The solver achieved 2.6 PFLOPS in the single-precision part on a $400^3\times 800$ lattice using 16000 MPI processes on 8000 nodes on the system.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine Learning-Based Estimation of Cumulants of Chiral Condensate via Multi-Ensemble Reweighting with Deborah.jl

    hep-lat 2026-02 conditional novelty 5.0 of 10

    Using Tr M^-1 as both an input and a feature, a bias-corrected ML model predicts Tr M^-2..-4 and reproduces chiral-condensate cumulants with ~1% labeled data at ~26% of the original cost.

  2. Machine Learning Estimation on the Trace of Inverse Dirac Operator using the Gradient Boosting Decision Tree Regression

    hep-lat 2024-11 conditional novelty 4.0 of 10

    Gradient boosting with bias correction estimates Tr M^{-n} in lattice QCD from cheaper observables in favorable ensembles, but needs a substantial labeled fraction and fails for light-quark Tr M^{-3,4}.

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