Do you want to publish a course? Click here

Modified Block BiCGSTAB for Lattice QCD

399   0   0.0 ( 0 )
 Added by Yoshifumi Nakamura
 Publication date 2011
  fields
and research's language is English




Ask ChatGPT about the research

We present results for application of block BiCGSTAB algorithm modified by the QR decomposition and the SAP preconditioner to the Wilson-Dirac equation with multiple right-hand sides in lattice QCD on a $32^3 times 64$ lattice at almost physical quark masses. The QR decomposition improves convergence behaviors in the block BiCGSTAB algorithm suppressing deviation between true residual and recursive one. The SAP preconditioner applied to the domain-decomposed lattice helps us minimize communication overhead. We find remarkable cost reduction thanks to cache tuning and reduction of number of iterations.

rate research

Read More

We illustrate a technique for fitting lattice QCD correlators to sums of exponentials that is significantly faster than traditional fitting methods --- 10--40 times faster for the realistic examples we present. Our examples are drawn from a recent analysis of the Upsilon spectrum, and another recent analysis of the D -> pi semileptonic form factor. For single correlators, we show how to simplify traditional effective-mass analyses.
Pentaquark states in lattice QCD probably lie close in energy to two particle scattering states. Correctly identifying the resonant state is a challenging, yet tractable, problem given the terascale computing facilities available today. We summarize the initial round of exploratory lattice calculations and discuss what should be accomplished in the next round.
There exist two major problems in application of the conventional block BiCGSTAB method to the O(a)-improved Wilson-Dirac equation with multiple right-hand-sides: One is the deviation between the true and the recursive residuals. The other is the convergence failure observed at smaller quark masses for enlarged number of the right-hand-sides. The block BiCGGR algorithm which was recently proposed by the authors succeeds in solving the former problem. In this article we show that a preconditioning technique allows us to improve the convergence behavior for increasing number of the right-hand-sides.
109 - H.Suganuma , T.Iritani , F.Okiharu 2011
We study three subjects on quark confinement in hadrons in SU(3)$_{rm c}$ lattice QCD. From the accurate lattice calculation for more than 300 different patterns of three-quark (3Q) systems, we find that the static 3Q potential is well described by Y-Ansatz, i.e., the Coulomb plus Y-type linear potential. We also study the multi-quark (4Q, 5Q) potentials in lattice QCD, and find that they are well described by the one-gluon-exchange (OGE) Coulomb plus string-theoretical linear potential, which supports the {it infrared string picture} even for the multi-quarks. The second subject is a lattice-QCD determination of the relevant gluonic momentum component for confinement. The string tension (confining force) is found to be almost unchanged even after cutting off the high-momentum gluon component above 1.5GeV in the Landau gauge. In fact, {it quark confinement originates from the low-momentum gluon below about 1.5GeV.} Finally, we consider a possible gauge of QCD for the quark potential model, by investigating instantaneous inter-quark potential in generalized Landau gauge, which describes a continuous change from the Landau gauge to the Coulomb gauge.
A novel technique using machine learning (ML) to reduce the computational cost of evaluating lattice quantum chromodynamics (QCD) observables is presented. The ML is trained on a subset of background gauge field configurations, called the labeled set, to predict an observable $O$ from the values of correlated, but less compute-intensive, observables $mathbf{X}$ calculated on the full sample. By using a second subset, also part of the labeled set, we estimate the bias in the result predicted by the trained ML algorithm. A reduction in the computational cost by about $7%-38%$ is demonstrated for two different lattice QCD calculations using the Boosted decision tree regression ML algorithm: (1) prediction of the nucleon three-point correlation functions that yield isovector charges from the two-point correlation functions, and (2) prediction of the phase acquired by the neutron mass when a small Charge-Parity (CP) violating interaction, the quark chromoelectric dipole moment interaction, is added to QCD, again from the two-point correlation functions calculated without CP violation.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا