Don't leave easy performance wins on the table.

One of the most exciting feature of Java 16 is vector API (JEP 338) that makes it possible to take advantage of available SIMD instructions and by doing so significantly improve performance.

When reading an example from JEP documentation I was somewhat shocked to see that a simple scalar computation

void scalarComputation(float[] a, float[] b, float[] c) {
  for (int i = 0; i < a.length; i++) {
    c[i] = (a[i] * a[i] + b[i] * b[i]) * -1.0f;
  }
}

has to be rewritten as a hardly readable

static final VectorSpecies<Float> SPECIES = FloatVector.SPECIES_PREFERRED;

void vectorComputation(float[] a, float[] b, float[] c,
        VectorSpecies<Float> species) {
    int i = 0;
    int upperBound = species.loopBound(a.length);
    for (; i < upperBound; i += species.length()) {
        //FloatVector va, vb, vc;
        var va = FloatVector.fromArray(species, a, i);
        var vb = FloatVector.fromArray(species, b, i);
        var vc = va.mul(va).
                    add(vb.mul(vb)).
                    neg();
        vc.intoArray(c, i);
    }

    for (; i < a.length; i++) {
        c[i] = (a[i] * a[i] + b[i] * b[i]) * -1.0f;
    }
}

vectorComputation(a, b, c, SPECIES);

to get the desired vectorized assembly.

  0.43%  / │  0x0000000113d43890: vmovdqu 0x10(%r8,%rbx,4),%ymm0
  7.38%  │ │  0x0000000113d43897: vmovdqu 0x10(%r10,%rbx,4),%ymm1
  8.70%  │ │  0x0000000113d4389e: vmulps %ymm0,%ymm0,%ymm0
  5.60%  │ │  0x0000000113d438a2: vmulps %ymm1,%ymm1,%ymm1
 13.16%  │ │  0x0000000113d438a6: vaddps %ymm0,%ymm1,%ymm0
 21.86%  │ │  0x0000000113d438aa: vxorps -0x7ad76b2(%rip),%ymm0,%ymm0
  7.66%  │ │  0x0000000113d438b2: vmovdqu %ymm0,0x10(%r9,%rbx,4)
 26.20%  │ │  0x0000000113d438b9: add    $0x8,%ebx
  6.44%  │ │  0x0000000113d438bc: cmp    %r11d,%ebx
         \ │  0x0000000113d438bf: jl     0x0000000113d43890

"Phew, great that I don't use Java", thought I and went on to see what would Go do in such case. To my big disappointment, Go does not seem to support SIMD intrinsics and generates non-vectorized assembly :(

Convinced that Clang would not disappoint me I checked the assembly using compiler explorer with highest level of optimization and noticed that even though it does a lot of useful optimizations, including loop unrolling, it's still using only 128bit XMM registers:

...
.LBB0_6: # =>This Inner Loop Header: Depth=1
movups xmm1, xmmword ptr [rsi + 4*rax]
movups xmm2, xmmword ptr [rsi + 4*rax + 16]
mulps xmm1, xmm1
mulps xmm2, xmm2
movups xmm3, xmmword ptr [rdx + 4*rax]
movups xmm4, xmmword ptr [rdx + 4*rax + 16]
mulps xmm3, xmm3
addps xmm3, xmm1
mulps xmm4, xmm4
addps xmm4, xmm2
xorps xmm3, xmm0
xorps xmm4, xmm0
movups xmmword ptr [rcx + 4*rax], xmm3
movups xmmword ptr [rcx + 4*rax + 16], xmm4
add rax, 8
cmp rdi, rax
jne .LBB0_6
cmp rdi, r8
je .LBB0_13
...

but easily switches to 512bit ZMM registers when foundation AVX 512 support is requested via -mavx512f flag:

...
.LBB0_8: # =>This Inner Loop Header: Depth=1
vmovups zmm1, zmmword ptr [rsi + 4*rdi]
vmovups zmm2, zmmword ptr [rsi + 4*rdi + 64]
vmovups zmm3, zmmword ptr [rsi + 4*rdi + 128]
vmovups zmm4, zmmword ptr [rsi + 4*rdi + 192]
vmulps zmm1, zmm1, zmm1
vmulps zmm2, zmm2, zmm2
vmulps zmm3, zmm3, zmm3
vmulps zmm4, zmm4, zmm4
vmovups zmm5, zmmword ptr [rdx + 4*rdi]
vmovups zmm6, zmmword ptr [rdx + 4*rdi + 64]
vmovups zmm7, zmmword ptr [rdx + 4*rdi + 128]
vmovups zmm8, zmmword ptr [rdx + 4*rdi + 192]
vmulps zmm5, zmm5, zmm5
vaddps zmm1, zmm1, zmm5
vmulps zmm5, zmm6, zmm6
vaddps zmm2, zmm2, zmm5
vmulps zmm5, zmm7, zmm7
vaddps zmm3, zmm3, zmm5
vmulps zmm5, zmm8, zmm8
vaddps zmm4, zmm4, zmm5
vpxord zmm1, zmm1, zmm0
vpxord zmm2, zmm2, zmm0
vpxord zmm3, zmm3, zmm0
vpxord zmm4, zmm4, zmm0
vmovdqu64 zmmword ptr [rcx + 4*rdi], zmm1
vmovdqu64 zmmword ptr [rcx + 4*rdi + 64], zmm2
vmovdqu64 zmmword ptr [rcx + 4*rdi + 128], zmm3
vmovdqu64 zmmword ptr [rcx + 4*rdi + 192], zmm4
add rdi, 64
cmp rax, rdi
jne .LBB0_8
cmp rax, r8
je .LBB0_19
test r8b, 56
je .LBB0_14
...

AVX 512 support was added by Intel with the Haswell processor, which shipped in 2013, so it's very likely that in 2021 your servers have it.

Moral of the story?

Don't leave performance on the table - know your hardware and how to take full advantage of it.