MILC staggered conjugate gradient performance on Intel KNL

Li, Ruiz; DeTar, Carleton; Doerfler, Douglas W.; Gottlieb, Steven; Jha, Asish; Kalamkar, Dhiraj D.; Toussaint, D.

doi:10.48550/arxiv.1611.00728

Cited by 5 publications

(5 citation statements)

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“…Overall three USQCD codes (Chroma, MILC, CPS) askwere chosen to be Tier-1 NERSC Exascale Application Partnership (NESAP) codes, with another (QLua) becoming a Tier-2 code. As part of this partnership, these codes were further developed and optimized through hackathons and dungeon sessions with NERSC and Intel [106,107]. In the context of USQCD, Jefferson Lab deployed a KNL cluster in 2016 and enlarged it in 2018.…”

Section: Utilizing Intel Xeon Phi Tm Architecturementioning

confidence: 99%

Status and future perspectives for lattice gauge theory calculations to the exascale and beyond

et al. 2019

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In this and a set of companion whitepapers, the USQCD Collaboration lays out a program of science and computing for lattice gauge theory. These whitepapers describe how calculation using lattice QCD (and other gauge theories) can aid the interpretation of ongoing and upcoming experiments in particle and nuclear physics, as well as inspire new ones. * Editor, bjoo@jlab.org † Editor, chulwoo@bnl.gov arXiv:1904.09725v2 [hep-lat]

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Section: Utilizing Intel Xeon Phi Tm Architecturementioning

confidence: 99%

Status and future perspectives for lattice gauge theory calculations to the exascale and beyond

et al. 2019

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“…From a HPC viewpoint, a clear advantage of this operator with precomputed V µ is that its stencil is restricted to sites which are at most one hop away. Still, it is not trivial to reach an acceptable performance on a many-core architecture [4,5].…”

Section: Staggered Kernel Details and Performancementioning

confidence: 99%

Three Dirac operators on two architectures with one piece of code and no hassle

Dürr¹

2019

Proceedings of the 36th Annual International Symposium on Lattice Field Theory — PoS(LATTICE2018)

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A simple minded approach to implement three discretizations of the Dirac operator (staggered, Wilson, Brillouin) on two architectures (KNL and core i7) is presented. The idea is to use a high-level compiler along with OpenMP parallelization and SIMD pragmas, but to stay away from cache-line optimization and/or assembly-tuning. The implementation is for N v right-handsides, and this extra index is used to fill the SIMD pipeline. On one KNL node single precision performance figures for N c = 3, N v = 12 read 475 Gflop/s, 345 Gflop/s, and 790 Gflop/s for the three discretization schemes, respectively.

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“…This brief exposition of the subject cannot do justice to the effort spent by other authors to maximize performance on a specific architecture for a given Dirac operator D. Recent review talks on the interplay between algorithms and machines in lattice QCD include [19][20][21][22][23]. In addition, there is a number of HPC projects in lattice QCD with similar objectives on several architectures [24][25][26][27][28][29][30][31][32][33][34][35]. Preliminary accounts 6 of this work were given in [36,37].…”

Section: Introductionmentioning

confidence: 99%

Fast and flexible implementations of Wilson, Brillouin and Susskind fermions in lattice QCD

Dürr¹

2021

Preprint

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A modern Fortran implementation of three Dirac operators (Wilson, Brillouin, Susskind) in lattice QCD is presented, based on OpenMP shared-memory parallelization and SIMD pragmas. The main idea is to apply a Dirac operator to N v vectors simultaneously, to ease the memory bandwidth bottleneck. All index computations are left to the compiler and maximum weight is given to portability and flexibility. The lattice volume, N x N y N z N t , the number of colors, N c , and the number of right-hand sides, N v , are parameters defined at compile time. Several memory layout options are compared. The code performs well on modern many-core architectures (480 Gflop/s, 880 Gflop/s, and 780 Gflop/s with N v = 12 for the three operators in single precision on a 72-core KNL processor, a 2×24-core Skylake node yields similar results). Explicit run-time tests with CG/BiCGstab inverters confirm that the memory layout is relevant for the KNL, but less so for the Skylake architecture. The ancillary code distribution contains all routines, including the single, double, and mixed precision Krylov space solvers, to render it self-contained and ready-to-use.1 Here "frequently" means O(10 5 ) times, "large" implies a n × n matrix with n = 402 653 184 for a Wilson fermion on a 64 3 × 128 lattice, and depending on the quark mass the condition number of D † D is often in the range 10 6 . . . 10 8 . The factor 10 5 reflects the production of an ensemble of 1000 gauge configurations, separated by ten τ = 1 HMC trajectories, assuming that each of these requires O(10) inversions.

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MILC staggered conjugate gradient performance on Intel KNL

Cited by 5 publications

References 0 publications

Status and future perspectives for lattice gauge theory calculations to the exascale and beyond

Status and future perspectives for lattice gauge theory calculations to the exascale and beyond

Three Dirac operators on two architectures with one piece of code and no hassle

Fast and flexible implementations of Wilson, Brillouin and Susskind fermions in lattice QCD

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