Introduction

Without fast gather and scatter, AVX2 code performs best when you reshape the problem so loads and stores become more contiguous. AVX2 does have gather instructions, but they are often much slower than normal aligned or nearly contiguous loads, and it does not provide the kind of general fast scatter support people often want.

The Real Strategy: Change the Access Pattern

The first question should not be “How do I emulate gather and scatter exactly?” It should be “Can I reorganize the data or the loop so I no longer need them often?”

The most effective strategies are:

• change array-of-structs into struct-of-arrays
• process data in blocks that are contiguous
• precompute permutations or compact index groups
• fall back to scalar access only at the edges

Data layout changes often beat heroic instruction tricks.

Prefer Contiguous Loads and Shuffles

If the needed values live in a small contiguous window, load the window and then rearrange inside registers. That is usually much faster than a true gather on scattered addresses.

Conceptually:

// Instead of reading scattered values one by one into a vector,
// load a contiguous block and shuffle the elements you need.

With intrinsics, you often end up doing a sequence like:

• '_mm256_loadu_si256'
• lane extraction or shuffles
• blends or permutes

The exact shuffle depends on the pattern, but the important optimization is that memory access stays friendly.

Restructure Data into SoA

A classic example is converting:

• array of structs

into:

• one array per field

That turns irregular field gathers into ordinary vector loads.

For example, this layout is harder for SIMD:

1typedef struct {
2    float x;
3    float y;
4    float z;
5} Point;

This layout is often easier:

1typedef struct {
2    float *x;
3    float *y;
4    float *z;
5} PointsSoA;

Now a vectorized loop can load eight x values or eight y values contiguously with AVX2 instead of trying to gather them from strided struct fields.

Scatter Usually Becomes Scalar Stores or Reordered Work

Without general fast scatter, one practical pattern is:

1. compute vector results
2. extract lanes
3. store them individually

That sounds disappointing, but it is often fine if the expensive part was the arithmetic and the stores are not the bottleneck.

Another common trick is to reorder the algorithm so outputs are produced in contiguous chunks instead of random destinations. If you can sort, bin, or buffer intermediate results first, the final stores can often become linear.

Use Scalar Code for Truly Irregular Tails

Not every loop deserves full vectorization. If most of the work is regular but a small fraction is irregular, a hybrid design is often best:

• vectorized main loop for regular blocks
• scalar cleanup for irregular indices

This keeps the hot path fast without overcomplicating the whole implementation.

Measure Gather Before Assuming It Is Worth It

AVX2 gather exists, but performance depends heavily on access locality. If the addresses are close together and cache-friendly, gather may be acceptable. If the addresses are far apart or cache-cold, it can be slow enough that a different algorithm wins.

That is why benchmark-driven design matters here. “Use gather” or “never use gather” are both too simplistic. The real answer depends on:

• cache locality
• index regularity
• arithmetic-to-memory ratio
• how much of the loop is truly irregular

Common Pitfalls

The most common mistake is trying to emulate arbitrary gather and scatter patterns exactly without first asking whether the data layout can be changed. Many SIMD problems become much easier once the memory layout is fixed.

Another pitfall is focusing only on instruction count. Memory behavior often dominates, so a solution with a few extra shuffles can still beat a more “direct” gather-heavy design.

It is also easy to over-vectorize irregular tails. If only a small part of the loop is messy, scalar cleanup is often the simpler and faster engineering choice.

Finally, do not judge AVX2 gather in isolation from cache behavior. The same instruction can look acceptable in one benchmark and terrible in another depending on how the addresses map to memory.

Summary

• The best AVX2 workaround is usually to reduce the need for gather and scatter, not to emulate them blindly.
• Prefer contiguous loads plus shuffles whenever the access pattern allows it.
• Struct-of-arrays layouts are often much more SIMD-friendly than array-of-structs.
• Scalar stores or scalar cleanup are often acceptable for the irregular parts.
• Benchmark the whole memory-access pattern, not just the instruction sequence.