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Hydra

Hydra provides a SPMD programming model for Julia, intended for auto batching of machine learning models. Hydra is an early proof-of-concept; it ready for simple alpha testing and testers should open issues liberally.

julia> @spmd 4 println("Hello, World!")
Hello, World!
Hello, World!
Hello, World!
Hello, World!

@spmd N is analagous to for i = 1:N ..., with an important difference; each "lane" (iteration) runs in lockstep, one instruction from each lane at a time.

julia> @spmd 4 begin
         println("Hello from lane ", lane())
         println("Goodbye from lane ", lane())
       end
Hello from lane 1
Hello from lane 2
Hello from lane 3
Hello from lane 4
Goodbye from lane 1
Goodbye from lane 2
Goodbye from lane 3
Goodbye from lane 4

This allows computations across lanes to be run batched. For example, the set of return values from lane() can be stored as a tuple and use SIMD operations; thus 4 lanes of SPMD code can potentially be just as fast as a single lane of normal scalar code. Hydra also handles batches of more complex objects such as arrays, allowing it to express the kinds of batching used in machine learning.

julia> @spmd 4 lanesum(lane()*2)
20

Crucially, Hydra does this while taking care of control flow, so we hope to bring great performance to almost any Julia program.

julia> @spmd 4 begin
         iseven(lane()) && println("Hello from lane ", lane())
       end
Hello from lane 2
Hello from lane 4

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