`@batch` slower than `Threads.@threads`

[ParadaCarleton]

Tried to see if replacing Threads.@threads with @batch would improve performance on smaller workloads for a function. This seems to introduce some kind of data race (which wasn't present with Threads.@threads), which results in many NaN values and incorrect results, which were caught by the tests. The relevant loop is here. Not sure what the cause is; the for loop is looping over calculations for each data point, and each one of these should be completely independent of the calculations for every other data point.

[chriselrod]

Can you provide a minimal example I can run?

[ParadaCarleton]

Can you provide a minimal example I can run?

I'll try, but I expect it to be quite difficult since the tests are based on comparing the results of a fairly complex calculation to those produced by another program in R; I'd have to build tests for much smaller subsets of the calculations. Replacing Threads.@threads with @batch per=thread in the loop I linked generates the problem, but I understand that's not really "Minimal."

[chriselrod]

Replacing Threads.@threads with @Batch per=thread in the loop I linked generates the problem, but I understand that's not really "Minimal."

While not ideal, having code to call psis would help.

[chriselrod]

What I'd do from there is store post_sample_size, r_eff, and weights and hopefully produce a minimal example from that.

    @inbounds Threads.@threads for i in eachindex(tail_length)
        tail_length[i] = def_tail_length(post_sample_size, r_eff[i])
        ξ[i] = @views ParetoSmooth.do_psis_i!(weights[i,:], tail_length[i])
    end

A trick for storing things is defining this in your REPL:

julia> const ARGS = Ref{Any}()

and then (using Revise.jl), edit your package to say

    Main.ARGS[] = copy.((post_sample_size, r_eff, weights))
    @inbounds Threads.@threads for i in eachindex(tail_length)
        tail_length[i] = def_tail_length(post_sample_size, r_eff[i])
        ξ[i] = @views ParetoSmooth.do_psis_i!(weights[i,:], tail_length[i])
    end

now, calling the function will store those into the ARGS defined in your REPL.
This is a neat trick for getting at internal data structures that may be hard to extract otherwise from deeply nested functions.

[ParadaCarleton]

Replacing Threads.@threads with @Batch per=thread in the loop I linked generates the problem, but I understand that's not really "Minimal."

While not ideal, having code to call psis would help.

The tests in the test file should do what you need, in that case; the test folder contains an example log-likelihood array used for testing, as well as the saved results of the same calculations in R.

[ParadaCarleton]

What I'd do from there is store post_sample_size, r_eff, and weights and hopefully produce a minimal example from that.

    @inbounds Threads.@threads for i in eachindex(tail_length)
        tail_length[i] = def_tail_length(post_sample_size, r_eff[i])
        ξ[i] = @views ParetoSmooth.do_psis_i!(weights[i,:], tail_length[i])
    end

A trick for storing things is defining this in your REPL:

julia> const ARGS = Ref{Any}()

and then (using Revise.jl), edit your package to say

    Main.ARGS[] = copy.((post_sample_size, r_eff, weights))
    @inbounds Threads.@threads for i in eachindex(tail_length)
        tail_length[i] = def_tail_length(post_sample_size, r_eff[i])
        ξ[i] = @views ParetoSmooth.do_psis_i!(weights[i,:], tail_length[i])
    end

now, calling the function will store those into the ARGS defined in your REPL.
This is a neat trick for getting at internal data structures that may be hard to extract otherwise from deeply nested functions.

I see; how do I get those to you after that?

[chriselrod]

I see; how do I get those to you after that?

If they're big, then there isn't really a convenient way. I'll look at the test suite.

[ParadaCarleton]

I see; how do I get those to you after that?

If they're big, then there isn't really a convenient way.

Yeah, weights is pretty big (big enough to be inconvenient, at least; 32 x 1000).

[ParadaCarleton]

Something that might be worth noting is that I tried to parallelize this with FLoops.jl as well, and it complained about "weights" being a boxed variable, which might create data races. I don't know what that means, but it might be useful to you. I figured this was just a problem for FLoops, rather than an inherent feature of the code that would produce data races, since the tests all passed when using Threads.@threads, indicating near-equality between the R outputs and Julia outputs.

[chriselrod]

I don't think that'd be a problem.

The first thing I'll look at is if Polyester/StrideArraysCore has a problem with views.
Either with the macro, or with StrideArraysCore's custom view-handling.

It'll be at least a couple hours before I can look at this.

[chriselrod]

Just as an update, I fixed a bug in StrideArraysCore so that it now gets the correct answer, but @threads is several times faster than @batch for me. I still need to look into why.

[ParadaCarleton]

Just as an update, I fixed a bug in StrideArraysCore so that it now gets the correct answer, but @threads is several times faster than @batch for me. I still need to look into why.

Cool, thanks! That's surprising -- I would have expected it to be, at worst, the same speed.

[chriselrod]

I changed the name of the issue to reflect the updated status.