metagraphoptimization.jl/src/task_functions.jl

function compute(t::AbstractTask; data...)
   error("Need to implement compute()")
end

function compute_effort(t::AbstractTask)
   # default implementation using compute
   error("Need to implement compute_effort()")
end

function data(t::AbstractTask)
   error("Need to implement data()")
end

compute_effort(t::AbstractDataTask) = 0
compute(t::AbstractDataTask; data...) = data
data(t::AbstractDataTask) = getfield(t, :data)

data(t::AbstractComputeTask) = 0

function compute_effort(t::FusedComputeTask)
   (T1, T2) = collect(typeof(t).parameters)
   return compute_effort(T1()) + compute_effort(T2())
end

# actual compute functions for the tasks can stay undefined for now
# compute(t::ComputeTaskU, data::Any) = mycomputation(data)

function compute_intensity(t::AbstractTask)::UInt64
   if data(t) == 0
      return typemax(UInt64)
   end
   return compute_effort(t) / data(t)
end

function show(io::IO, t::FusedComputeTask)
   (T1, T2) = get_types(t)
   print(io, "ComputeFuse(", T1(), ", ", T2(), ")")
end

function ==(t1::AbstractTask, t2::AbstractTask)
   return false
end

function ==(t1::AbstractComputeTask, t2::AbstractComputeTask)
   return typeof(t1) == typeof(t2)
end

function ==(t1::AbstractDataTask, t2::AbstractDataTask)
   return data(t1) == data(t2)
end

copy(t::AbstractDataTask) = error("Need to implement copying for your data tasks!")
copy(t::AbstractComputeTask) = typeof(t)()