# In-place Parallel Super Scalar Samplesort (IPS$^4$o)

## TL;DR

The paper introduces IPS$^4$o, an in-place, parallel, cache-efficient sorting algorithm that outperforms existing methods in speed and scalability on multi-core systems, combining theoretical innovations with practical performance.

## Contribution

It presents novel in-place distribution-based sorting techniques that eliminate recursion stacks and improve parallel and cache-efficient performance.

## Key findings

- IPS$^4$o scales well on multi-core machines.
- Outperforms in-place competitors by up to 3x.
- Faster than BlockQuicksort by up to 1.5x sequentially.

## Abstract

We present a sorting algorithm that works in-place, executes in parallel, is cache-efficient, avoids branch-mispredictions, and performs work O(n log n) for arbitrary inputs with high probability. The main algorithmic contributions are new ways to make distribution-based algorithms in-place: On the practical side, by using coarse-grained block-based permutations, and on the theoretical side, we show how to eliminate the recursion stack. Extensive experiments show that our algorithm IPS$^4$o scales well on a variety of multi-core machines. We outperform our closest in-place competitor by a factor of up to 3. Even as a sequential algorithm, we are up to 1.5 times faster than the closest sequential competitor, BlockQuicksort.