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The added benchmark is a basis for a performance investigation. We compare the performance of the same operation run in Java vs Enso to see what is the overhead and try to get the Enso operations closer to the pure-Java performance.
53 lines
2.5 KiB
Markdown
53 lines
2.5 KiB
Markdown
# Exploring Table operation performance
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These benchmarks are used to compare various approaches to computing operations
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on Table columns, to find out what best practices should we use for these and
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find venues for optimization of the language and Table implementation.
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These benchmarks are not meant to be used for tracking performance of the
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current implementation itself. That is supposed to be done by another project -
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`Table_Benchmarks`.
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## Structure
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Currently, the benchmarks are split into a few files, each exploring some
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separate topic, like mapping a single column, combining two columns with some
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operation, or computing an aggregate operation over a column. In each file,
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there may be a few Enso types, each representing a separate benchmark. Usually,
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we have two benchmarks for each operation type - one dealing with a primitive
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value type like integers (`long` in the Java side) and another dealing with a
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reference type like `String` or `Date`. We expect the performance
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characteristics between these may differ, e.g. because Java allows to use `long`
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without boxing, so we compare them separately.
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Each Enso type for a given benchmark contains multiple methods which represent
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various 'approaches' to computing the same operation.
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Each benchmark run has a name that consists of the type it defines it, a dot and
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the method representing the particular approach, e.g.
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`Boxed_Map_Test.enso_map_as_vector`.
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## Running
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The runner is very simple. If any options are to be customized, the Enso file
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itself needs to be modified. One can run the whole project to run all the
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benchmarks, or run only a specific file.
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## Analysis
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The output of the benchmarks should be saved to a file. Then that file can be
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loaded using the Enso workflow in `tools/performance/benchmark-analysis`.
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The workflow is tuned to analysing these comparative benchmarks.
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At the top, one can select which file is to be analyzed. Below there is a
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dropdown allowing to select one particular benchmark (represented by the type,
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e.g. `Boxed_Map_Test`). With that selected, one can display a scatter plot
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visualization comparing various approaches of that one given benchmark. On the
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plot we can see runtimes of subsequent iterations. Later, we drop the first 40
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iterations (the number can easily be customized in the workflow) to ensure
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sufficient warm-up for each benchmark. Then a table is displayed computing the
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average runtime of each approach and how they compare relative to each other - a
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dropdown allows to select one benchmark that will be used as a reference point
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(100%) for the average runtime comparison.
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