layed groundwork for implementing interpretation
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@ -2,16 +2,16 @@ module ExpressionProcessing
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export expr_to_postfix
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export PostfixType
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export Operator, Add, Subtract, Multiply, Divide, Power, Abs, Log, Exp, Sqrt
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export ElementType, FLOAT64, OPERATOR, INT64
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export Operator, ADD, SUBTRACT, MULTIPLY, DIVIDE, POWER, ABS, LOG, EXP, SQRT
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export ElementType, EMPTY, FLOAT64, OPERATOR, INT64
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export ExpressionElement
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@enum Operator::Int64 Add=1 Subtract=2 Multiply=3 Divide=4 Power=5 Abs=6 Log=7 Exp=8 Sqrt=9
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@enum ElementType FLOAT64=1 OPERATOR=2 INT64=3
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@enum Operator::Int64 ADD=1 SUBTRACT=2 MULTIPLY=3 DIVIDE=4 POWER=5 ABS=6 LOG=7 EXP=8 SQRT=9
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@enum ElementType EMPTY=0 FLOAT64=1 OPERATOR=2 INT64=3
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struct ExpressionElement
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Type::ElementType
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value::Int64 # Reinterpret the stored value to type "ElementType" when using it
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Value::Int64 # Reinterpret the stored value to type "ElementType" when using it
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end
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const PostfixType = Vector{ExpressionElement}
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@ -45,23 +45,23 @@ end
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function get_operator(op::Symbol)::Operator
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if op == :+
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return Add
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return ADD
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elseif op == :-
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return Subtract
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return SUBTRACT
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elseif op == :*
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return Multiply
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return MULTIPLY
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elseif op == :/
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return Divide
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return DIVIDE
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elseif op == :^
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return Power
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return POWER
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elseif op == :abs
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return Abs
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return ABS
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elseif op == :log
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return Log
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return LOG
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elseif op == :exp
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return Exp
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return EXP
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elseif op == :sqrt
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return Sqrt
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return SQRT
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end
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end
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@ -80,13 +80,13 @@ function convert_var_to_int(var::Symbol)::Int
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end
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function convert_to_ExpressionElement(element)::ExpressionElement
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value = reinterpret(Int64, element)
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Value = reinterpret(Int64, element)
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if element isa Float64
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return ExpressionElement(FLOAT64, value)
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return ExpressionElement(FLOAT64, Value)
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elseif element isa Int64
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return ExpressionElement(INT64, value)
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return ExpressionElement(INT64, Value)
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elseif element isa Operator
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return ExpressionElement(OPERATOR, value)
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return ExpressionElement(OPERATOR, Value)
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else
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error("Element was of type '$(typeof(element))', which is not supported.")
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end
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@ -100,17 +100,17 @@ end
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const SymbolTable64 = Dict{Tuple{Expr, Symbol},Float64}
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"Replaces all the variables and parameters of the given expression with their corresponding value stored in the symtable
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"Replaces all the variables and parameters of the given expression with their corresponding Value stored in the symtable
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# Arguments
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- `symtable::SymbolTable64`: Contains the values of all variables for each expression
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- `originalExpr::Expr`: Contains a deep copy of the original expression. It is used to link the expression and variables to their according value stored in the symtable
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- `originalExpr::Expr`: Contains a deep copy of the original expression. It is used to link the expression and variables to their according Value stored in the symtable
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"
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function replace_variables!(ex::Expr, symtable::SymbolTable64, originalExpr::Expr)
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for i in 1:length(ex.args)
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arg = ex.args[i]
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if typeof(arg) === Expr
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replace_variables!(arg, symtable, originalExpr)
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elseif haskey(symtable, (originalExpr,arg)) # We found a variable/parameter and can replace it with the actual value
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elseif haskey(symtable, (originalExpr,arg)) # We found a variable/parameter and can replace it with the actual Value
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ex.args[i] = symtable[(originalExpr,arg)]
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end
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end
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@ -12,59 +12,93 @@ export interpret
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"
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function interpret(expressions::Vector{ExpressionProcessing.PostfixType}, variables::Matrix{Float64}, parameters::Vector{Vector{Float64}})
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# TODO:
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# create CUDA array and fill it with the variables and parameters
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# create CUDA array for calculation results
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variableRows = size(variables, 1)
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cudaVars = CuArray(variables)
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paramRows = get_max_parameter_rows(parameters)
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p1aramCols = length(parameters)
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cudaParams = CuArray{Float64}(undef, p1aramCols, paramRows) # length(parameters) == number of expressions
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# TODO: Fill cudaParams
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cudaParams = create_cuda_array(parameters, NaN64)
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cudaExprs = create_cuda_array(expressions, ExpressionElement(EMPTY, 0))
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cudaStepsize = CuArray([get_max_inner_length(expressions), get_max_inner_length(parameters)]) # put into seperate cuArray, as this is static and would be inefficient to send seperatly to every kernel
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# TODO: Move CuArray(expression[i]) outside the loop for a more efficient transfer to GPU but leave kernel signature as is
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# Start kernel for each expression to ensure that no warp is working on different expressions
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for i in eachindex(expressions)
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cudaExpr = CuArray(expressions[i])
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kernel = @cuda launch=false interpret_expression(cudaExpr, cudaVars, cudaParams, i)
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kernel = @cuda launch=false interpret_expression(cudaExprs, cudaVars, cudaParams, cudaStepsize, i)
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config = launch_configuration(kernel.fun)
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threads = min(variableRows, config.threads)
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blocks = cld(variableRows, threads)
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kernel(cudaExpr, cudaVars, cudaParams, i; threads, blocks)
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kernel(cudaExprs, cudaVars, cudaParams, cudaStepsize, i; threads, blocks)
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end
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end
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function interpret_expression(expression, variables, parameters, exprIndex::Int)
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#TODO Implement interpreter
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function interpret_expression(expressions::CuDeviceArray{ExpressionElement}, variables::CuDeviceArray{Float64}, parameters::CuDeviceArray{Float64}, stepsize::CuDeviceArray{Int}, exprIndex::Int)
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firstExprIndex = (exprIndex - 1 * stepsize[1]) + 1 # Inclusive
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lastExprIndex = firstExprIndex + stepsize[1] # Exclusive
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firstParamIndex = (exprIndex - 1 * stepsize[2]) + 1 # Inclusive
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# lastParamIndex = firstParamIndex + stepsize[2] # Exclusive (probably not needed)
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for i in firstExprIndex:lastExprIndex
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# TODO Implement interpreter
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# - start at firstExprIndex and interpret until the first ExpressionElement is "Empty" or we reached lastExprIndex
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end
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return
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end
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"Retrieves the number of entries for the largest inner vector"
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function get_max_parameter_rows(params::Vector{Vector{T}})::Int where T
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function get_max_inner_length(vec::Vector{Vector{T}})::Int where T
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maxLength = 0
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for i in eachindex(params)
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if length(params) > maxLength
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maxLength = length(params)
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@inbounds for i in eachindex(vec)
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if length(vec[i]) > maxLength
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maxLength = length(vec[i])
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end
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end
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return maxLength
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end
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"Returns a CuArray filed with the data provided. The inner vectors do not have to have the same length. All missing elements will be the value ```invalidElement```"
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function create_cuda_array(data::Vector{Vector{T}}, invalidElement::T)::CuArray{T} where T
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dataCols = get_max_inner_length(data)
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dataRows = length(data)
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dataMat = convert_to_matrix(data, invalidElement)
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cudaArr = CuArray{T}(undef, dataCols, dataRows) # length(parameters) == number of expressions
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copyto!(cudaArr, dataMat)
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return cudaArr
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end
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"Converts a vector of vectors into a matrix. The inner vectors do not need to have the same length.
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All entries that cannot be filled have ```invalidElement``` as their value
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"
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function convert_to_matrix(vec::Vector{Vector{T}}, invalidElement::T)::Matrix{T} where T
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vecCols = get_max_inner_length(vec)
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vecRows = length(vec)
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vecMat = fill(invalidElement, vecCols, vecRows)
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for i in eachindex(vec)
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vecMat[:,i] = copyto!(vecMat[:,i], vec[i])
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end
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return vecMat
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end
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@deprecate InterpretExplicit!(op::Operator, x, y) interpret_expression(expression, variables, parameters, exprIndex::Int)
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# @deprecate InterpretExplicit!(op::Operator, x, y) interpret_expression(expression, variables, parameters, exprIndex::Int)
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# Kernel
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function InterpretExplicit!(op::Operator, x, y)
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index = (blockIdx().x - 1) * blockDim().x + threadIdx().x
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stride = gridDim().x * blockDim().x
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if op == Add
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if op == ADD
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# @cuprintln("Performing Addition") # Will only be displayed when the GPU is synchronized
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for i = index:stride:length(y)
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@inbounds y[i] += x[i]
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end
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return
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elseif op == Subtract
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elseif op == SUBTRACT
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# @cuprintln("Performing Subtraction") # Will only be displayed when the GPU is synchronized
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for i = index:stride:length(y)
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@inbounds y[i] -= x[i]
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@ -14,18 +14,18 @@ parameters[1][1] = 5
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@testset "Test conversion expression element" begin
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reference1 = ExpressionElement(FLOAT64, reinterpret(Int64, 1.0))
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reference2 = ExpressionElement(INT64, reinterpret(Int64, 1))
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reference3 = ExpressionElement(OPERATOR, reinterpret(Int64, Add))
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reference3 = ExpressionElement(OPERATOR, reinterpret(Int64, ADD))
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@test isequal(reference1, ExpressionProcessing.convert_to_ExpressionElement(1.0))
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@test isequal(reference2, ExpressionProcessing.convert_to_ExpressionElement(1))
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@test isequal(reference3, ExpressionProcessing.convert_to_ExpressionElement(Add))
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@test isequal(reference3, ExpressionProcessing.convert_to_ExpressionElement(ADD))
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end
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@testset "Test conversion to postfix" begin
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reference = PostfixType()
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append!(reference, [ExpressionProcessing.convert_to_ExpressionElement(1), ExpressionProcessing.convert_to_ExpressionElement(1.0), ExpressionProcessing.convert_to_ExpressionElement(2), ExpressionProcessing.convert_to_ExpressionElement(Multiply),
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ExpressionProcessing.convert_to_ExpressionElement(Add), ExpressionProcessing.convert_to_ExpressionElement(-1), ExpressionProcessing.convert_to_ExpressionElement(Add)])
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append!(reference, [ExpressionProcessing.convert_to_ExpressionElement(1), ExpressionProcessing.convert_to_ExpressionElement(1.0), ExpressionProcessing.convert_to_ExpressionElement(2), ExpressionProcessing.convert_to_ExpressionElement(MULTIPLY),
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ExpressionProcessing.convert_to_ExpressionElement(ADD), ExpressionProcessing.convert_to_ExpressionElement(-1), ExpressionProcessing.convert_to_ExpressionElement(ADD)])
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postfix = expr_to_postfix(expressions[1])
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@test isequal(reference, postfix)
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@ -1,21 +1,42 @@
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using CUDA
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using .ExpressionProcessing
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using .Interpreter
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expressions = Vector{Expr}(undef, 1)
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variables = Matrix{Float64}(undef, 1,2)
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parameters = Vector{Vector{Float64}}(undef, 1)
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expressions = Vector{Expr}(undef, 2)
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variables = Matrix{Float64}(undef, 2,2)
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parameters = Vector{Vector{Float64}}(undef, 2)
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# Resulting value should be 10
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expressions[1] = :(x1 + 1 * x2 + p1)
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variables[1,1] = 2
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variables[1,2] = 3
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expressions[2] = :(5 + x1 + 1 * x2 + p1 + p2)
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variables[1,1] = 2.0
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variables[1,2] = 3.0
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variables[2,1] = 2.0
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variables[2,2] = 3.0
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parameters[1] = Vector{Float64}(undef, 1)
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parameters[1][1] = 5
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parameters[2] = Vector{Float64}(undef, 2)
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parameters[1][1] = 5.0
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parameters[2][1] = 5.0
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parameters[2][2] = 0.0
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@testset "Test interpretation" begin
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postfixExpr = expr_to_postfix(expressions[1])
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postfixExprs = Vector{PostfixType}(undef, 1)
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postfixExprs[1] = postfixExpr
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postfixExprs = Vector([postfixExpr])
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push!(postfixExprs, expr_to_postfix(expressions[2]))
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Interpret(postfixExprs, variables, parameters)
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CUDA.@sync interpret(postfixExprs, variables, parameters)
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end
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@testset "Test conversion to matrix" begin
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reference = Matrix{Float64}(undef, 2, 2)
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reference[1,1] = 5.0
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reference[2,1] = NaN64
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reference[1,2] = 5.0
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reference[2,2] = 0.0
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# fill!(reference, [[], [5.0, 0.0]])
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# reference = Matrix([5.0, NaN],
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# [5.0, 0.0])
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result = Interpreter.convert_to_matrix(parameters, NaN64)
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@test isequal(result, reference)
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end
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