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17 changed files with 339 additions and 20 deletions
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@ -10,7 +10,11 @@ makedocs(sitename="LatticeGPU", modules=[LatticeGPU], doctest=true,
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"Space-time" => "space.md",
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"Groups and algebras" => "groups.md",
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"Fields" => "fields.md",
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"Yang-Mills" => "ym.md",
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"Gradient flow" => "flow.md",
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"Schrödinger Functional" => "sf.md",
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"Dirac" => "dirac.md",
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"Solvers" => "solvers.md"
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],
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"Solvers" => "solvers.md",
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"Input Output" => "io.md"
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],
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repo = "https://igit.ific.uv.es/alramos/latticegpu.jl")
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@ -10,10 +10,11 @@ gauge fields `SU3`, for scalar fields `Float64`). We have:
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direction.
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- `N` scalar fields: `N` elemental types at each spacetime point.
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For all these fields the spacetime point are ordered in memory
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according to the point-in-block and block indices (see
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[`SpaceParm`](@ref)). An execption is the [`scalar_field_point`](@ref)
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fields.
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Fields can have **naturaL indexing**, where the memory layout follows
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the point-in-block and block indices (see
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[`SpaceParm`](@ref)). Fields can also have **lexicographic indexing**,
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where points are labelled by a D-dimensional index (see [`scalar_field_point`](@ref)).
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## Initialization
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47
docs/src/flow.md
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docs/src/flow.md
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@ -0,0 +1,47 @@
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# Gradient flow
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The gradient flow equations can be integrated in two different ways:
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1. Using a fixed step-size integrator. In this approach one fixes the
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step size $\epsilon$ and the links are evolved from
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$V_\mu(t)$ to $V_\mu(t +\epsilon)$ using some integration
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scheme.
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1. Using an adaptive step-size integrator. In this approach one fixes
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the tolerance $h$ and the links are evolved for a time $t_{\rm
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end}$ (i.e. from $V_\mu(t)$ to $V_\mu(t +t_{\rm end})$)
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with the condition that the maximum error while advancing is not
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larger than $h$.
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In general adaptive step size integrators are much more efficient, but
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one loses the possibility to measure flow quantities at the
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intermediate times $\epsilon, 2\epsilon, 3\epsilon,...$. Adaptive
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step size integrators are ideal for finite size scaling studies, while
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a mix of both integrators is the most efficient approach in scale
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setting applications.
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## Integration schemes
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```@docs
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FlowIntr
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wfl_euler
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zfl_euler
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wfl_rk2
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zfl_rk2
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wfl_rk3
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zfl_rk3
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```
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## Integrating the flow equations
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```@docs
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flw
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flw_adapt
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```
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## Observables
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```@docs
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Eoft_plaq
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Eoft_clover
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Qtop
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```
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@ -153,6 +153,10 @@ projalg
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## Generic `Algebra` methods
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```@docs
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dot
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norm
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norm2
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normalize
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exp
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expm
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alg2mat
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14
docs/src/io.md
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14
docs/src/io.md
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@ -0,0 +1,14 @@
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# Input/Output
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## Configurations
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Routines to read/write and import gauge configurations.
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```@docs
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read_cnfg
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save_cnfg
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import_bsfqcd
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import_lex64
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import_cern64
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```
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9
docs/src/sf.md
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9
docs/src/sf.md
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@ -0,0 +1,9 @@
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# Schödinger Functional
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Specific SF observables and routines
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```@docs
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setbndfield
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sfcoupling
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```
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@ -3,7 +3,10 @@
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D-dimensional lattice points are labeled by two ordered integer
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numbers: the point-in-block index ($$b$$ in the figure below) and the
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block index ($$r$$ in the figure below). The routines [`up`](@ref) and
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block index ($$r$$ in the figure below). This is called **natural
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indexing**, in contrast with the **lexicographic indexing** where points on
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the lattice are represented by a D-dimensional `CartesianIndex`.
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The routines [`up`](@ref) and
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[`dw`](@ref) allow you to displace to the neighboring points of the
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lattice.
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![D dimensional lattice points are labeled by its
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59
docs/src/ym.md
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59
docs/src/ym.md
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# Simulating Yang-Mills on the lattice
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```@docs
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GaugeParm
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YMworkspace
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ztwist
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```
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## Gauge actions and forces
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Routines to compute the gauge action.
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```@docs
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gauge_action
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```
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Routines to compute the force derived from gauge actions.
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```@docs
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force_gauge
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```
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### Force field refresh
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Algebra fields with **natural indexing** can be randomized.
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```@docs
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randomize!
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```
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## Basic observables
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Some basic observable.
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```@docs
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plaquette
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```
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## HMC simulations
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### Integrating the EOM
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```@docs
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IntrScheme
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leapfrog
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omf2
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omf4
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MD!
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```
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### HMC algorithm
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```@docs
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hamiltonian
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HMC!
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```
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