Instruction level parallelism (ILP)

Full source

Our program does not take advantage of the fact that modern CPUs are superscalar processors, capable of executing several independent instructions simultaneously. The problem in our v1 implementation is that each step is dependent on the previous step, caused by this part:

    let z = x + y;
    v = min(v, z);

We will solve this by using a simple idea from the reference solution: accumulate results into 4 independent, intermediate results and merge them only after processing the whole row.

Suppose we have some row of d, containing the elements x0, x1, x2, x3, ..., xn, and some column of d (i.e. row of t), containing the elements y0, y1, y2, y3, ..., yn. Then, we compute results for all rows by accumulating intermediate results into 4 variables v0, v1, v2, v3 as follows:

    // iteration 1
    v0 = min(v0, x0 + y0);
    v1 = min(v1, x1 + y1);
    v2 = min(v2, x2 + y2);
    v3 = min(v3, x3 + y3);
    // iteration 2
    v0 = min(v0, x4 + y4);
    v1 = min(v1, x5 + y5);
    v2 = min(v2, x6 + y6);
    v3 = min(v3, x7 + y7);
    // iteration 3
    v0 = min(v0, x8 + y8);
    v1 = min(v1, x9 + y9);
    v2 = min(v2, x10 + y10);
    v3 = min(v3, x11 + y11);
    // etc ...

This should allow the CPU to write results into 4 independent registers for each intermediate result.

Before we can update the step_row function, we need to make sure the amount of elements on each row is always a multiple of 4 to keep the performance-critical loop free of messy, unnecessary branching. As previously, we transpose d to allow linear reading of its columns, but have to make sure the row length of the transpose is also divisible by 4. The preprocessing looks a bit more complicated, but is essentially the same as doing the transpose in v1, except that we copy the values of d also into vd, which is padded with std::f32::INFINITY values to make its rows divisible by 4:

    const BLOCK_SIZE: usize = 4;
    let blocks_per_row = (n + BLOCK_SIZE - 1) / BLOCK_SIZE;
    let n_padded = blocks_per_row * BLOCK_SIZE;
    // d and transpose of d with extra room at the end of each row,
    // both initially filled with f32::INFINITY
    let mut vd = std::vec![std::f32::INFINITY; n_padded * n];
    let mut vt = std::vec![std::f32::INFINITY; n_padded * n];
    // Function: for one row of vd and vt,
    // copy a row at 'i' of d into vd and column at 'i' of d into vt
    let preprocess_row = |(i, (vd_row, vt_row)): (usize, (&mut [f32], &mut [f32]))| {
        for (j, (x, y)) in vd_row.iter_mut().zip(vt_row.iter_mut()).enumerate() {
            if i < n && j < n {
                *x = d[n*i + j];
                *y = d[n*j + i];
    // Partition vd and vt into rows, apply preprocessing in parallel for each row pair

Now vd contains the original d and vt contains the transpose of d, but both have been padded with extra columns to the right containing f32::INFINITYs to ensure the width of vd and vt is always divisible by 4. Then, we partition r and vd into row chunks, zip them into row chunk pairs and apply step_row in parallel for each row of vd, writing the results into its paired result row chunk. Each thread will compute results over all rows of vt.

    // Function: for some row in vd (vd_row) and all rows in vt (vt_rows),
    // compute all results for a row in r (r_row), corresponding to the row index of vd_row.
    let step_row = |(r_row, vd_row): (&mut [f32], &[f32])| {
        let vt_rows = vt.chunks_exact(n_padded);
        // Length of a zipped iterator is the length of the shorter iterator in the zip pair so this never exceeds n
        for (res, vt_row) in r_row.iter_mut().zip(vt_rows) {
            // Partition both rows into chunks of size 4
            // (x0, x1, x2, x3), (x4, x5, x6, x7), ...
            let vd_blocks = vd_row.chunks_exact(BLOCK_SIZE);
            // (y0, y1, y2, y3), (y4, y5, y6, y7), ...
            let vt_blocks = vt_row.chunks_exact(BLOCK_SIZE);
            // Using an array here is bit more convenient than 4 different variables, e.g. v0, v1, v2, v3
            let mut block = [std::f32::INFINITY; BLOCK_SIZE];
            // Accumulate all results as in v1, but 4 elements at a time
            for (vd_block, vt_block) in {
                for (b, (&x, &y)) in block.iter_mut().zip(vd_block.iter().zip(vt_block)) {
                    *b = min(*b, x + y);
            // Fold 4 intermediate values into a single minimum and assign to final result
            *res = block.iter().fold(std::f32::INFINITY, |acc, &x| min(acc, x));


We'll now compare the Rust implementation to the reference C++ version, which will be compiled with both Clang and GCC. If we run the benchmark program for a single iteration with the same parameters as previously, we get:

ImplementationCompilerTime (s)IPC
C++ v2gcc 7.4.0-1ubuntu120.82.88
C++ v2clang 6.0.0-1ubuntu244.63.23
Rust v2rustc 1.38.0-nightly17.02.43

Two interesting questions arise:

  • Why is rustc outperforming gcc?
  • What on earth is clang doing?

Let's compare the disassembly of all 3 versions.


I omitted a portion of code above LOOP, up until label 1f0 since perf-record placed most CPU cycles between LOOP and the jb instruction that jumps to LOOP.

It looks like the compiler outsmarted us by ignoring our attempt of writing code that utilizes ILP and instead auto-vectorized our loop, which now does all the work with two 128-bit SIMD registers:

    mov       rbp,r14
    add       rbp,rbx
    je        1f0 ; about 20 lines above LOOP
    inc       rcx
    vmovups   xmm3,XMMWORD PTR [r14+rbx*1]
    vaddps    xmm3,xmm3,XMMWORD PTR [r10+rbx*1]
    vpermilps xmm3,xmm3,0x1b
    vminps    xmm2,xmm2,xmm3
    add       rbx,0x10
    cmp       rcx,rax
    jb        LOOP

We'll be rewriting most of our code with 256-bit vector types and instructions in v3, but let's take a look at what the compiler managed to generate here.

We load 4 consecutive f32 values from vd_row into a 128-bit vector register xmm3:

    vmovups   xmm3,XMMWORD PTR [r14+rbx*1]

Then we load 4 consecutive f32 values from vt_row, add those to the 4 values in xmm3 using a single SIMD add-instruction, and store the result in xmm3:

    vaddps    xmm3,xmm3,XMMWORD PTR [r10+rbx*1]

Using vpermilps with shuffle control 0x1b = 0b00_01_10_11 will reverse the order of 4 elements in xmm3, but I don't know why the compiler wants to use this here, especially inside the loop. However, we are going to use these kind of SIMD register permutations ourselves later in v5 to significantly lower the total amount of memory accesses.

    vpermilps xmm3,xmm3,0x1b

We use a single SIMD min-instruction for 4 f32 result values in xmm2 and 4 sums in xmm3 we got from the previous step and store the result in xmm2:

    vminps    xmm2,xmm2,xmm3

We increment the loop variable by 16, which will jump over 4 f32s in the next loop, and start over:

    add       rbx,0x10
    cmp       rcx,rax
    jb        LOOP


I did not try to figure out what happens here, but it looks like a failed auto-vectorization attempt:

    ; other half with similar lines omitted
    lea       edx,[rax+r14*1+0x2]
    movsxd    rdx,edx
    lea       esi,[r15+r14*1+0x2]
    movsxd    rsi,esi
    lea       edi,[rax+r14*1+0x3]
    movsxd    rdi,edi
    lea       ebx,[r15+r14*1+0x3]
    movsxd    rbx,ebx
    vmovss    xmm0,DWORD PTR [r8+rdi*4]
    vinsertps xmm0,xmm0,DWORD PTR [r8+rdx*4],0x10
    vmovss    xmm3,DWORD PTR [rbp+rbx*4+0x0]
    vinsertps xmm3,xmm3,DWORD PTR [rbp+rsi*4+0x0],0x10
    vaddps    xmm0,xmm0,xmm3
    vpmovzxdq xmm3,xmm0
    vcmpltps  xmm0,xmm0,xmm4
    vunpcklps xmm0,xmm2,xmm0
    vblendvpd xmm6,xmm6,xmm3,xmm0
    vpermilps xmm7,xmm5,0xe8
    vpermilps xmm4,xmm6,0xe8
    add       r14d,0x4
    add       rcx,0xffffffffffffffff
    jne       LOOP


GCC did not auto-vectorize anything but produced a good example of ILP:

    lea    rcx,[r10+rcx*4]
    lea    r8,[r8+r9*1+0x10]
    nop    WORD PTR cs:[rax+rax*1+0x0]
    vmovss xmm0,DWORD PTR [rcx]
    vaddss xmm0,xmm0,DWORD PTR [rax]
    add    rax,0x10
    add    rcx,0x10
    vminss xmm1,xmm0,xmm1
    vmovss xmm0,DWORD PTR [rcx-0xc]
    vaddss xmm0,xmm0,DWORD PTR [rax-0xc]
    vminss xmm4,xmm0,xmm4
    vmovss xmm0,DWORD PTR [rcx-0x8]
    vaddss xmm0,xmm0,DWORD PTR [rax-0x8]
    vminss xmm3,xmm0,xmm3
    vmovss xmm0,DWORD PTR [rcx-0x4]
    vaddss xmm0,xmm0,DWORD PTR [rax-0x4]
    vminss xmm2,xmm0,xmm2
    cmp    r8,rax
    jne    LOOP

This is what we were trying to achieve, to have 4 independent registers for updating the minimums. You can read more about it here.

We are not going to twist our Rust code so we can get a good ILP example out of it, the auto-vectorization already produced code that was more efficient than the gcc ILP example above. However, this was just an example, and we'll be needing ILP extensively later in v4. First, let's rewrite our code using SIMD instructions.