JuliaArrays / JuliaArrays/ArraysOfArrays.jl
GPU: segmented broadcast/map for scalar-result element ops over device-resident VectorOfArrays
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Description
Follow-up to #51.
v1.0 added GPU support for both nested-array types: the flat-data / depth API (innersum, innermapreduce, innerreduce, mapat, bcastat, flatview, vecflattened) runs vectorized on device-resident data (with a segmented reduction kernel for VectorOfArrays), and map / broadcast over the element arrays work under allowscalar(false) when a VectorOfArrays' shape information (elem_ptr / kernel_size) is host-resident.
Remaining gap: scalar-returning per-element operations — map(argmin, V), f.(V) where f returns a scalar — when the shape information is also on the device (adapt(CuArray, V)). These fall back to host-side element iteration and hit allowscalar errors.
Suggested fix: a broadcast style / map specialization that lowers such ops to a segmented device kernel (one work item per part, over the flat data), along the lines of StructArrays' always_struct_broadcast(::AbstractGPUArrayStyle) and the existing GPU segmented reduction used by innersum.
ArrayOfSimilarArrays is unaffected — its equal-size elements broadcast and map on device without this.
Filed by an AI assistant (Claude), on request, to re-scope #51.
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start with the existing GPU segmented reduction used by innersum, the scope in #51, and StructArrays' always_struct_broadcast(::AbstractGPUArrayStyle) example. Trace how scalar-returning map and broadcast behave when elem_ptr or kernel_size are device-resident. Done means these operations use a segmented device kernel without host iteration or allowscalar errors.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- data, performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100