111. Benchmarking the Vector API

Benchmarking the Vector API can be accomplished via JMH. Let’s consider three Java arrays (x, y, z) each of 50,000,000 integers, and the following computation:

z[i] = x[i] + y[i];
w[i] = x[i] * z[i] * y[i];
k[i] = z[i] + w[i] * y[i];

So, the final result is stored in a Java array named k. And, let’s consider the following benchmark containing four different implementations of this computation (using a mask, no mask, unrolled, and plain scalar Java with arrays):

@OutputTimeUnit(TimeUnit.MILLISECONDS)
@BenchmarkMode({Mode.AverageTime, Mode.Throughput})
@Warmup(iterations = 3, time = 1)
@Measurement(iterations = 5, time = 1)
@State(Scope.Benchmark)
@Fork(value = 1, warmups = 0, 
    jvmArgsPrepend = {"--add-modules=jdk.incubator.vector"})
public class Main {
  private static final VectorSpecies<Integer> VS 
    = IntVector.SPECIES_PREFERRED;
  ...
  @Benchmark
  public void computeWithMask(Blackhole blackhole) {…}
  @Benchmark
  public void computeNoMask(Blackhole blackhole) {…}
  @Benchmark
  public void computeUnrolled(Blackhole blackhole) {…}
  @Benchmark
  public void computeArrays(Blackhole blackhole) {…}
}

Running this benchmark on an Intel(R) Core(TM) i7-3612QM CPU @ 2.10GHz machine running Windows 10 produced the following results:

Figure 5.9: Benchmark results

Overall, executing the computation using data-parallel capabilities gives the best performance, highest throughput, and best average time.