References as pointers

[joaquimg]

What if...

References were pointers instead of UInt´s?

With UInts we can have simple vector as mappings IF the solver does not support deletions. However, if the solver supports deletions we need some mapping. This mapping could be a vector, which would grow a lot with lots of deletions; could be a linked list which is fast to delete but not fast to access; could be a Dict which is easy to delete but not so fast to access.

Given that we usually do a lot more additions than deletions, may we should not be much worried of the time it takes to delete, but with the time it takes to add.

My proposal goes as follows, instead of giving the user a unique UInt everytime a variable/constraint is added we could give a pointer to an Int that directly matches the solver´s index, the SolverInstance would also hold a list of those pointers. In case of adding a constraint there would be no need for a lookup to get the solver´s corresponding index. In case of a deletion we can set that pointer to a zero value and fix all the other pointers since the SolverInstance has a list of them, then the user´s reference are fixed.

[joaquimg]

Is connected to #63 and #71

[mlubin]

There's a huge performance penalty if references objects have a field that contains a reference to a GC-managed object. What type precisely are you imagining as a pointer?

[joaquimg]

My very first idea was simply:

struct VariableReference
    value::Vector{Int}
end

and that vector has length 1.
I imagine one can do better that this... The SolverInstance would keep a vector of these vectors in order to apply the updates after deletions.

[mlubin]

The GC will explode if you have 10,000,000 variables. I don't have the link off hand, but it's a big issue with the Julia compiler that may or may not be fixed by 1.0.

[joaquimg]

So we can´t have 10,000,000 vectors in julia? :(

[mlubin]

By explode I mean take up a majority of the execution time.

[mlubin]

JuliaLang/julia#12205
JuliaLang/julia#14955

[joaquimg]

julia> using BenchmarkTools

julia> function refs()
       [Int[3] for i in 1:10_000_000]
       end
refs (generic function with 1 method)
julia> @benchmark refs()
BenchmarkTools.Trial:
  memory estimate:  991.82 MiB
  allocs estimate:  10000002
  --------------
  minimum time:     1.185 s (46.46% GC)
  median time:      2.013 s (65.95% GC)
  mean time:        1.870 s (63.30% GC)
  maximum time:     2.412 s (69.37% GC)
  --------------
  samples:          3
  evals/sample:     1

julia> function dicts()
       Dict(zip(collect(1:10_000_000),collect(1:10_000_000)))
       end
dicts (generic function with 1 method)

julia> @benchmark dicts()
BenchmarkTools.Trial:
  memory estimate:  693.76 MiB
  allocs estimate:  79
  --------------
  minimum time:     2.202 s (12.19% GC)
  median time:      2.252 s (12.26% GC)
  mean time:        2.275 s (13.38% GC)
  maximum time:     2.370 s (15.55% GC)
  --------------
  samples:          3
  evals/sample:     1

[mlubin]

The extra GC overhead will easily consume the factor of 2 that you're looking to gain by avoiding dictionaries.

[joaquimg]

yep :(

[joaquimg]

Out of curiosity, do you see other drawbacks besides this GC problem?

[mlubin]

Having to

fix all the other pointers

forces an O(n) operation for every deletion. I'd rather have constant-time addition and constant-time deletion that's a tiny bit slower. But I think there are ways to tweak the idea to also have constant-time deletion.

[joaquimg]

My understanding of the current usage suggestion is that we have a dict:
variable_map = Dict{MOI.VariableRerence, Int}() or variable_map = Dict{UInt64, Int}()

The Int is the variable index for the solver, when a deletion happens, GLPK, Gurobi and Xpress, shift the indices and thus one have to loop over the dictionary to correct the Int´s to match the correct variables right?

[mlubin]

Not all solvers have poor deletion semantics like that. Some solvers like SCIP have their own reference objects and don't force you to keep the indices, so we should design the interface so that it's possible for deletion to be fast in that case.

[blegat]

In theory, you could avoid the O(n) by using a Fenwick Tree to encode the deletions. You will add a deletion to the Fenwick Tree on O(log(n)) and you will be able to retrieve the number of deletion done before an index i in O(log(n)). Not sure if it is worth doing it practice though

[mlubin]

Ok to close or is there more to discuss here?

[joaquimg]

For now we should close, maybe if the issues in julia are fixed we can reopen.