Merge branch 'main' into HLL_WavespeedDefault_Davis

.github/workflows/Downgrade.yml

@@ -72,7 +72,7 @@ jobs:
       - uses: julia-actions/cache@v1
       - uses: julia-actions/julia-downgrade-compat@v1
         with:
-          skip: LinearAlgebra,Printf,SparseArrays,DiffEqBase
+          skip: LinearAlgebra,Printf,SparseArrays,UUIDs,DiffEqBase
           projects: ., test
       - uses: julia-actions/julia-buildpkg@v1
         env:

Project.toml

@@ -25,6 +25,7 @@ OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 P4est = "7d669430-f675-4ae7-b43e-fab78ec5a902"
 Polyester = "f517fe37-dbe3-4b94-8317-1923a5111588"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
+Preferences = "21216c6a-2e73-6563-6e65-726566657250"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -46,6 +47,7 @@ Triangulate = "f7e6ffb2-c36d-4f8f-a77e-16e897189344"
 TriplotBase = "981d1d27-644d-49a2-9326-4793e63143c3"
 TriplotRecipes = "808ab39a-a642-4abf-81ff-4cb34ebbffa3"
 TrixiBase = "9a0f1c46-06d5-4909-a5a3-ce25d3fa3284"
+UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [weakdeps]
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
@@ -76,6 +78,7 @@ OffsetArrays = "1.12"
 P4est = "0.4.9"
 Polyester = "0.7.5"
 PrecompileTools = "1.1"
+Preferences = "1.3"
 Printf = "1"
 RecipesBase = "1.1"
 Reexport = "1.0"
@@ -97,6 +100,7 @@ Triangulate = "2.2"
 TriplotBase = "0.1"
 TriplotRecipes = "0.1"
 TrixiBase = "0.1.1"
+UUIDs = "1.6"
 julia = "1.8"
 
 [extras]

src/Trixi.jl

@@ -76,6 +76,12 @@ using TrixiBase: TrixiBase
 using SimpleUnPack: @pack!
 using DataStructures: BinaryHeap, FasterForward, extract_all!
 
+using UUIDs: UUID
+using Preferences: @load_preference, set_preferences!
+
+const _PREFERENCE_SQRT = @load_preference("sqrt", "sqrt_Trixi_NaN")
+const _PREFERENCE_LOG = @load_preference("log", "log_Trixi_NaN")
+
 # finite difference SBP operators
 using SummationByPartsOperators: AbstractDerivativeOperator,
                                  AbstractNonperiodicDerivativeOperator, DerivativeOperator,

src/auxiliary/math.jl

@@ -5,6 +5,103 @@
 @muladd begin
 #! format: noindent
 
+const TRIXI_UUID = UUID("a7f1ee26-1774-49b1-8366-f1abc58fbfcb")
+
+"""
+    Trixi.set_sqrt_type(type; force = true)
+
+Set the `type` of the square root function to be used in Trixi.jl.
+The default is `"sqrt_Trixi_NaN"` which returns `NaN` for negative arguments
+instead of throwing an error.
+Alternatively, you can set `type` to `"sqrt_Base"` to use the Julia built-in `sqrt` function 
+which provides a stack-trace of the error which might come in handy when debugging code.
+"""
+function set_sqrt_type(type; force = true)
+    @assert type == "sqrt_Trixi_NaN"||type == "sqrt_Base" "Only allowed `sqrt` function types are `\"sqrt_Trixi_NaN\"` and `\"sqrt_Base\"`"
+    set_preferences!(TRIXI_UUID, "sqrt" => type, force = force)
+    @info "Please restart Julia and reload Trixi.jl for the `sqrt` computation change to take effect"
+end
+
+@static if _PREFERENCE_SQRT == "sqrt_Trixi_NaN"
+    """
+        Trixi.sqrt(x::Real)
+
+    Custom square root function which returns `NaN` for negative arguments instead of throwing an error.
+    This is required to ensure [correct results for multithreaded computations](https://github.com/trixi-framework/Trixi.jl/issues/1766) 
+    when using the [`Polyester` package](https://github.com/JuliaSIMD/Polyester.jl), 
+    i.e., using the `@batch` macro instead of the Julia built-in `@threads` macro, see [`@threaded`](@ref).
+
+    We dispatch this function for `Float64, Float32, Float16` to the LLVM intrinsics 
+    `llvm.sqrt.f64`, `llvm.sqrt.f32`, `llvm.sqrt.f16` as for these the LLVM functions can be used out-of the box, 
+    i.e., they return `NaN` for negative arguments.
+    In principle, one could also use the `sqrt_llvm` call, but for transparency and consistency with [`log`](@ref) we 
+    spell out the datatype-dependent functions here. 
+    For other types, such as integers or dual numbers required for algorithmic differentiation, we
+    fall back to the Julia built-in `sqrt` function after a check for negative arguments.
+    Since these cases are not performance critical, the check for negativity does not hurt here 
+    and can (as of now) even be optimized away by the compiler due to the implementation of `sqrt` in Julia.
+
+    When debugging code, it might be useful to change the implementation of this function to redirect to 
+    the Julia built-in `sqrt` function, as this reports the exact place in code where the domain is violated 
+    in the stacktrace.
+
+    See also [`Trixi.set_sqrt_type`](@ref).
+    """
+    @inline sqrt(x::Real) = x < zero(x) ? oftype(x, NaN) : Base.sqrt(x)
+
+    # For `sqrt` we could use the `sqrt_llvm` call, ...
+    #@inline sqrt(x::Union{Float64, Float32, Float16}) = Base.sqrt_llvm(x)
+
+    # ... but for transparency and consistency we use the direct LLVM calls here.
+    @inline sqrt(x::Float64) = ccall("llvm.sqrt.f64", llvmcall, Float64, (Float64,), x)
+    @inline sqrt(x::Float32) = ccall("llvm.sqrt.f32", llvmcall, Float32, (Float32,), x)
+    @inline sqrt(x::Float16) = ccall("llvm.sqrt.f16", llvmcall, Float16, (Float16,), x)
+end
+
+"""
+    Trixi.set_log_type(type; force = true)
+
+Set the `type` of the (natural) `log` function to be used in Trixi.jl.
+The default is `"sqrt_Trixi_NaN"` which returns `NaN` for negative arguments
+instead of throwing an error.
+Alternatively, you can set `type` to `"sqrt_Base"` to use the Julia built-in `sqrt` function 
+which provides a stack-trace of the error which might come in handy when debugging code.
+"""
+function set_log_type(type; force = true)
+    @assert type == "log_Trixi_NaN"||type == "log_Base" "Only allowed log function types are `\"log_Trixi_NaN\"` and `\"log_Base\"`."
+    set_preferences!(TRIXI_UUID, "log" => type, force = force)
+    @info "Please restart Julia and reload Trixi.jl for the `log` computation change to take effect"
+end
+
+@static if _PREFERENCE_LOG == "log_Trixi_NaN"
+    """
+        Trixi.log(x::Real)
+
+    Custom natural logarithm function which returns `NaN` for negative arguments instead of throwing an error.
+    This is required to ensure [correct results for multithreaded computations](https://github.com/trixi-framework/Trixi.jl/issues/1766) 
+    when using the [`Polyester` package](https://github.com/JuliaSIMD/Polyester.jl), 
+    i.e., using the `@batch` macro instead of the Julia built-in `@threads` macro, see [`@threaded`](@ref).
+
+    We dispatch this function for `Float64, Float32, Float16` to the respective LLVM intrinsics 
+    `llvm.log.f64`, `llvm.log.f32`, `llvm.log.f16` as for this the LLVM functions can be used out-of the box, i.e., 
+    they return `NaN` for negative arguments.
+    For other types, such as integers or dual numbers required for algorithmic differentiation, we
+    fall back to the Julia built-in `log` function after a check for negative arguments.
+    Since these cases are not performance critical, the check for negativity does not hurt here.
+
+    When debugging code, it might be useful to change the implementation of this function to redirect to 
+    the Julia built-in `log` function, as this reports the exact place in code where the domain is violated 
+    in the stacktrace.
+
+    See also [`Trixi.set_log_type`](@ref).
+    """
+    @inline log(x::Real) = x < zero(x) ? oftype(x, NaN) : Base.log(x)
+
+    @inline log(x::Float64) = ccall("llvm.log.f64", llvmcall, Float64, (Float64,), x)
+    @inline log(x::Float32) = ccall("llvm.log.f32", llvmcall, Float32, (Float32,), x)
+    @inline log(x::Float16) = ccall("llvm.log.f16", llvmcall, Float16, (Float16,), x)
+end
+
 """
     ln_mean(x, y)
 

test/test_parabolic_1d.jl

@@ -195,14 +195,14 @@ end
                                                                                 Prandtl = prandtl_number(),
                                                                                 gradient_variables = GradientVariablesEntropy()),
                         l2=[
-                            2.459359632523962e-5,
-                            2.3928390718460263e-5,
-                            0.00011252414117082376,
+                            2.4593501090944024e-5,
+                            2.3928163240907908e-5,
+                            0.00011252309905552921,
                         ],
                         linf=[
-                            0.0001185052018830568,
-                            0.00018987717854305393,
-                            0.0009597503607920999,
+                            0.0001185048754512863,
+                            0.0001898766501935486,
+                            0.0009597450028770993,
                         ])
     # Ensure that we do not have excessive memory allocations
     # (e.g., from type instabilities)

test/test_unstructured_2d.jl

@@ -701,7 +701,7 @@ end
                             1.8290174930157832e-11,
                             4.61017890529547e-11],
                         tspan=(0.0, 0.1),
-                        atol=1.0e-11)
+                        atol=1.0e-10)
     # Ensure that we do not have excessive memory allocations
     # (e.g., from type instabilities)
     let