Thankfully, the solution paths you're enumerating aren't the only viable solutions to the problem of representing missing values. Because Julia has value types (called immutables) and because Julia can compile functions based on their input arguments' types so that the functions don't need to do any run-time tag checking, a Nullable{Float64} value can be stored using at most 16 bytes -- 8 bytes for a Boolean flag indicating whether a value is present or not and 8 bytes for the actual 64-bit floating point value. The latter 8 bytes can store arbitrary bits if the Boolean flag is set to indicate that the Float64 value is missing.

If you're interested in the details of how Nullable{T} works, you can start with http://github.com/JuliaLang/julia/pull/8152 and then read through Julia's compiler code to understand how immutables are represented in memory and how functions that employ them are compiled to machine code.

UPDATE: Looking back at my answer, I should make clear that, when you want to work with SIMD or GPU operations, one can (and we will) use a representation of arrays of Nullable objects that places the values and the Boolean flags into two separate arrays. The values array is then identical to a normal 64-bit floating point array and allows all of the same operations. Setting the Boolean flags appropriately requires a bit more work. (How much work is strongly dependent on the exact computation being performed.)