Julia version 0.7 brings with it an exciting new feature: the ability to customize broadcast fusion! This recently-merged change is the culmination of a long iterative design process that involved many members of the community. We have converged on a highly extensible interface that should satisfy many use-cases. In this blog post I'll explain why this is a big deal by reviewing some of the key features and just scratch the surface of all that is possible with this new design. I'm quite certain that our enterprising community will come up with many more clever ways to exploit this new structure in the future.

Broadcasting is a core feature of Julia: it allows you to compactly and efficiently express an elementwise operation over containers and scalars by annotating operators and function calls with a . . In cases where the sizes don't match, broadcasting will virtually extend missing dimensions or "singleton" dimensions (which contain only one value) by repeating them to fill the outer shape. For example, the expression ([1, 2, 3] .+ [10 20 30 40]) ./ 10 combines a 3-element column vector, a 1x4 matrix, and a scalar to compute a 3x4 result. I imagine this as "extruding" the vector across the columns of the one-row matrix and spreading the division by 10 across the entire result:

julia> ([ 1 , 2 , 3 ] .+ [ 10 20 30 40 ]) ./ 10 3 × 4 Array { Float64 , 2 }: 1.1 2.1 3.1 4.1 1.2 2.2 3.2 4.2 1.3 2.3 3.3 4.3

Since version 0.6, Julia has executed this expression by "fusing" the two operations into a single kernel. That is, instead of first constructing an integer matrix resulting from the addition ( [11 21 31 41; 12 22 ...] ) and then subsequently using a second loop to divide each element by 10, Julia does both the addition and division for each element at the same time, making just one pass through the output array and skipping intermediate array(s) entirely. This fusion optimization happens as a syntax-level transformation so it is guaranteed to occur and easy to reason about. Version 0.7 adds an extensible API on top of this feature that allows arrays to customize precisely how this operates.

Julia now uses a first-class data structure to "lazily" represent a fused broadcast expression before executing it. If you're not a package developer this may not mean all that much to you, but you'll still reap the many rewards. In Base Julia and its standard libraries alone, this means:

BitArray s can identify cases where they can operate on 64 boolean elements at once, yielding huge performance gains — often two orders of magnitude or more! For an easy example, I'll just use random data with a simple A and not B predicate:

julia> using BenchmarkTools, Random srand( 0 ) A = bitrand( 10 ^ 6 ) B = bitrand( 10 ^ 6 ) $A .& .!$B BenchmarkTools.Trial: memory estimate: 122.23 KiB allocs estimate: 3 ---------- minimum time: 7.891 μs ( 0.00 % GC) median time: 13.152 μs ( 0.00 % GC) mean time: 17.826 μs ( 9.62 % GC) maximum time: 591.497 μs ( 95.73 % GC) ---------- samples: 10000 evals/sample: 1

Compare this to previously on 0.6:

BenchmarkTools.Trial: memory estimate: 126.45 KiB allocs estimate: 6 ---------- minimum time: 3.615 ms ( 0.00 % GC) median time: 3.741 ms ( 0.00 % GC) mean time: 3.764 ms ( 0.18 % GC) maximum time: 7.744 ms ( 50.18 % GC) ---------- samples: 1328 evals/sample: 1

That's upwards of a 450x performance gain.

Broadcasted operations over ranges can now simply re-compute a new range instead of working element-wise if it's possible. For example, the expression ((1:10000) .+ 20) .* 7 doesn't need to allocate a vector for 10,000 elements — it doesn't even need to do 10,000 computations. It can instead operate in terms of the start, stop, and step and return a new range that represents the result: 147:7:70140 . This new feature allows them to transform O(N) computations into O(1) . On version 0.6, ranges were in this strange place where (1:10000) + 20 implemented the fast O(1) computation of a new range, but all other array types had deprecated addition with numbers in favor of explicit .+ broadcasting for clearer semantics and improved performance. Thanks to this new API, ranges can now identify these cases and fully support broadcasting in an efficient manner.

The structured matrices in the LinearAlgebra standard library no longer return sparse arrays as the result of a broadcasted operation. They'll now either maintain an appropriate structure or return a dense array. For example:

julia> using LinearAlgebra d = Diagonal ( 1 : 3 ) 3 × 3 Diagonal { Int64 , UnitRange { Int64 }}: 1 ⋅ ⋅ ⋅ 2 ⋅ ⋅ ⋅ 3 julia> d ./ 10 3 × 3 Diagonal { Float64 , Array { Float64 , 1 }}: 0.1 ⋅ ⋅ ⋅ 0.2 ⋅ ⋅ ⋅ 0.3 julia> t = d .+ LowerTriangular (rand( 3 , 3 )) 3 × 3 LowerTriangular { Float64 , Array { Float64 , 2 }}: 1.5446 ⋅ ⋅ 0.529211 2.26095 ⋅ 0.533674 0.373702 3.88408 julia> t .+ 100 3 × 3 Array { Float64 , 2 }: 101.327 100.0 100.0 100.85 102.138 100.0 100.575 100.129 103.839

Previously, Julia 0.6 would have returned a SparseMatrixCSC for d ./ 10 and an Array for d .+ LowerTriangular(rand(3,3)) .

Finally, broadcasting at the global scope is now pre-compilable, and you can use dot-broadcast inside generated functions. This wasn't a huge limitation in the past, but it did surprise folks timing an in-place broadcast like @time y .*= 2 and seeing thousands of allocations:

julia> y = rand( 1000 ); julia> y .*= 2 ; 0.032775 seconds ( 17.43 k allocations: 947.671 KiB) julia> y .*= 2 ; 0.020790 seconds ( 4.27 k allocations: 233.457 KiB) julia> y = rand( 1000 ); julia> y .*= 2 ; 0.060494 seconds ( 90.41 k allocations: 5.529 MiB) julia> y .*= 2 ; 0.000020 seconds ( 6 allocations: 208 bytes)

I'll now dive deeper into exactly how this new API works.

You can see precisely how a fused broadcast is represented with Meta.@lower , but in simpler terms the expression ([1, 2, 3] .+ [10 20 30 40]) ./ 10 is effectively a syntax transformation for:

julia> using .Broadcast: materialize, broadcasted bc = broadcasted(/, broadcasted(+, [ 1 , 2 , 3 ], [ 10 20 30 40 ]), 10 ) materialize(bc) 3 × 4 Array { Float64 , 2 }: 1.1 2.1 3.1 4.1 1.2 2.2 3.2 4.2 1.3 2.3 3.3 4.3

In this case, that bc object is an instance of a Broadcasted struct. It just holds onto the function and its arguments — and its arguments may include other nested Broadcasted structs. The materialize function does a bit of pre-processing and then calls copy(bc) , which allocates the result and then finally loops over the result and executes the functions.

Each step along the way is extensible, leveraging the power of Julia's multiple dispatch, inlining and argument specialization for near-zero overhead. With this basic framework in mind, you can begin to see how the built-in and standard library arrays are able to implement all those new features mentioned above:

When broadcasting into a BitArray , it can first introspect the functions and their arguments in the expression tree to see if it can operate at the level of the packed 64-bit chunks as UInt64 s instead of working bit-by-bit. It can even convert boolean-only operators like ! to their equivalent bitwise operators like ~ .

Ranges are able to "opt-out" of fusion by defining specialized Broadcast.broadcasted methods that immediately return those re-computed ranges. This means that they do not fuse multiple operations at all, but in exchange they get an O(1) algorithm.

When LinearAlgebra 's structured matrices are asked to allocate the result, their specialized broadcast_similar methods can walk through the Broadcasted expression tree and identify if any structure will remain.

Fused broadcast expressions no longer construct an anonymous function behind your back; they just construct new instances of a Julia data structure that simply call functions that are already defined. This is what allows them to be precompiled and work in generated functions.

Of course, this is all documented and available to packages. There's no secret sauce that we're hoarding for ourselves. It'll be exciting to see how the many creative minds in the package ecosystem manage to take advantage of all this new functionality. Some of the places where I see this yielding a significant benefit include machine learning applications and computation on GPUs.

Historically, some of the machine learning libraries have put in place hacks to allow their arrays to opt-out of fusion. The new broadcasting API instead allows them to opt-out of fusion with a first-class and fully supported mechanism that is much simpler to implement. Further, many deep learning techniques are heavily dependent upon differentiation; in some cases introspecting a broadcasted kernel will allow them to use an exact analytical result instead of computing differentials.