fast functor updated #157
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fast functor updated #157
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2017 MacBook Pro 2.3 GHz Intel Core i5, 8 GB 2133 MHz LPDDR3 Node v8.9.1 List ==== map --- map: empty list mean = 1.31 μs stddev = 11.87 μs min = 799.00 ns max = 375.82 μs map: singleton list mean = 2.40 μs stddev = 11.03 μs min = 1.03 μs max = 342.18 μs map: list (1000 elems) mean = 143.41 μs stddev = 225.12 μs min = 97.16 μs max = 2.03 ms map: list (2000 elems) mean = 274.16 μs stddev = 295.84 μs min = 199.66 μs max = 2.06 ms map: list (5000 elems) mean = 531.84 μs stddev = 512.61 μs min = 229.45 μs max = 2.95 ms map: list (10000 elems) mean = 895.24 μs stddev = 777.87 μs min = 464.59 μs max = 2.94 ms map: list (100000 elems) mean = 33.45 ms stddev = 7.65 ms min = 22.07 ms max = 63.47 ms
Addresses #131 The relevant chunk sizes (5 for the initial list segment), (3 for the tail-recursive remainder) were arrived at through benchmarked experimentation, mapping a simple (_ + 1) through lists of various sizes. Relevant figures: list of 1000 elems: 142.61 μs -> 36.97 μs list of 2000 elems: 275.17 μs -> 55.33 μs list of 10000 elems: 912.73 μs -> 208.39 μs list of 100000 elems: 34.56 ms -> 1.24 ms The ~30x speed increase for long lists is probably explained by the lack of GC thrashing with this approach. Benchmarked on 2017 Macbook Pro, 2.3 GHz Intel Core i5, 8 GB RAM. macOS Sierra 10.12.6 node v8.9.1
this lower the probability of stack-size troubles
begin with reverse unrolled map
natefaubion
reviewed
Mar 10, 2019
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Cool to see this happen! |
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@matthewleon thanks again for this, and sorry it slipped under the radar for so long! |
This was referenced Mar 10, 2019
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This is @matthewleon's faster
List.map, from #135, updated for 0.12.