Initial benchmarks for intersection types + a bit of speedup #11924
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| package org.enso.interpreter.bench.benchmarks.semantic; | ||
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| import java.util.concurrent.TimeUnit; | ||
| import org.enso.common.MethodNames; | ||
| import org.enso.compiler.benchmarks.Utils; | ||
| import org.graalvm.polyglot.Value; | ||
| import org.openjdk.jmh.annotations.Benchmark; | ||
| import org.openjdk.jmh.annotations.BenchmarkMode; | ||
| import org.openjdk.jmh.annotations.Fork; | ||
| import org.openjdk.jmh.annotations.Measurement; | ||
| import org.openjdk.jmh.annotations.Mode; | ||
| import org.openjdk.jmh.annotations.OutputTimeUnit; | ||
| import org.openjdk.jmh.annotations.Scope; | ||
| import org.openjdk.jmh.annotations.Setup; | ||
| import org.openjdk.jmh.annotations.State; | ||
| import org.openjdk.jmh.annotations.Warmup; | ||
| import org.openjdk.jmh.infra.BenchmarkParams; | ||
| import org.openjdk.jmh.infra.Blackhole; | ||
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| /** | ||
| * These benchmarks compare performance of {@link EnsoMultiValue}. They create a vector in a certain | ||
| * configuration representing numbers and then they perform {@code sum} operation on it. | ||
| */ | ||
| @BenchmarkMode(Mode.AverageTime) | ||
| @Fork(1) | ||
| @Warmup(iterations = 3, time = 5, timeUnit = TimeUnit.SECONDS) | ||
| @Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS) | ||
| @OutputTimeUnit(TimeUnit.MILLISECONDS) | ||
| @State(Scope.Benchmark) | ||
| public class MultiValueBenchmarks { | ||
| private Value arrayOfNumbers; | ||
| private Value sum; | ||
| private Value self; | ||
| private final long length = 100000; | ||
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| @Setup | ||
| public void initializeBenchmark(BenchmarkParams params) throws Exception { | ||
| var ctx = Utils.createDefaultContextBuilder().build(); | ||
| var code = | ||
| """ | ||
| from Standard.Base import Vector, Float, Number, Integer | ||
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| type Complex | ||
| private Number re:Float im:Float | ||
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| Complex.from (that:Number) = Complex.Number that 0 | ||
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| sum arr = | ||
| go acc i = if i >= arr.length then acc else | ||
| v = arr.at i | ||
| sum = acc + v | ||
| @Tail_Call go sum i+1 | ||
| go 0 0 | ||
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| sum_re arr = | ||
| go acc i = if i >= arr.length then acc else | ||
| v = arr.at i . re | ||
| sum = acc + v | ||
| @Tail_Call go sum i+1 | ||
| go 0 0 | ||
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| make_vector typ n = | ||
| Vector.new n i-> | ||
| r = 3 + 5*i | ||
| case typ of | ||
| 0 -> r:Complex | ||
| 1 -> r:Integer | ||
| 2 -> r:Float | ||
| 3 -> | ||
| c = r:Complex&Float | ||
| c:Float | ||
| 4 -> | ||
| c = r:Float&Complex | ||
| c:Float | ||
| 5 -> r:Complex&Float | ||
| 6 -> r:Float&Complex | ||
| """; | ||
| var benchmarkName = SrcUtil.findName(params); | ||
| var src = SrcUtil.source(benchmarkName, code); | ||
| int type = Integer.parseInt(benchmarkName.substring(benchmarkName.length() - 1)); | ||
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| var module = ctx.eval(src); | ||
| this.self = module.invokeMember(MethodNames.Module.GET_ASSOCIATED_TYPE); | ||
| var makeVector = module.invokeMember("get_method", self, "make_vector"); | ||
| this.arrayOfNumbers = makeVector.execute(self, type, length); | ||
| this.sum = | ||
| module.invokeMember(MethodNames.Module.EVAL_EXPRESSION, type == 0 ? "sum_re" : "sum"); | ||
| } | ||
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| /** | ||
| * The <b>base benchmark</b> for this suite. Measures how much it takes to access an Atom in a | ||
| * Vector, read {@code re:Float} field out of it and sum all of them together. | ||
| */ | ||
| @Benchmark | ||
|
There was a problem hiding this comment. This is now the base benchmark. The results after 4dacf53 are: still 60 times slower than it should be. There was a problem hiding this comment. The biggest slowdown is currently in
It seems to always allocate new arrays. Rather it should treat Attempting to make things better in a follow up PR: |
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| public void sumOverComplexBaseBenchmark0(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| /** | ||
| * Working with {@code Integer} should be the fastest. The plus operation on integer are faster | ||
| * than those on {@code Float} and moreover the {@code Vector} has a special representation when | ||
| * full of {@code long} values. | ||
| */ | ||
| @Benchmark | ||
| public void sumOverInteger1(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| /** | ||
| * Working with {@code Float} should be also fast. The {@code Vector} has a special representation | ||
| * when full of {@code double} values that increases cache locality. | ||
| */ | ||
| @Benchmark | ||
| public void sumOverFloat2(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| // | ||
| // Following benchmarks shall catch up with the base benchmark | ||
| // | ||
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| @Benchmark | ||
| public void sumOverComplexFloatRecastedToFloat3(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| @Benchmark | ||
| public void sumOverFloatComplexRecastedToFloat4(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| @Benchmark | ||
| public void sumOverComplexAndFloat5(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| @Benchmark | ||
| public void sumOverFloatAndComplex6(Blackhole matter) { | ||
| performBenchmark(matter); | ||
| } | ||
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| private void performBenchmark(Blackhole matter) throws AssertionError { | ||
| var resultValue = sum.execute(arrayOfNumbers); | ||
| if (!resultValue.fitsInLong()) { | ||
| throw new AssertionError("Shall be a long: " + resultValue); | ||
| } | ||
| long result = resultValue.asLong(); | ||
| long expectedResult = length * 3L + (5L * (length * (length - 1L) / 2L)); | ||
| boolean isResultCorrect = result == expectedResult; | ||
| if (!isResultCorrect) { | ||
| throw new AssertionError("Expecting " + expectedResult + " but was " + result); | ||
| } | ||
| matter.consume(result); | ||
| } | ||
| } | ||
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@Akirathan, @hubertp, @4e6 (and also @radeusgd):
Vectoris created bymake_vector typmethodtypes includeInteger,Float,Complexas non-intersection type benchmarks to compare totypes mixingFloatandComplextogether into different intersection typesIdeally the intersection types benchmark results should be close to base benchmark
sumOverComplexBaseBenchmark0with some "reasonable overhead".Are you OK with these benchmarks?