This documentation section explains some of the ideas behind the sorting strategy implemented by ChipSort. It's all mostly based on a couple of academic papers from 2008, found in References.

The overall strategy is to first use sorting networks to create small sorted arrays, and then merge them in a multi-way fashion in one or more stages, depending on the size of the input and the chip specs. The best strategy depends mostly on the size of register and cache memories.


Processing power in computers has been growing faster than memory bandwidth for years. To reach high performance, some of the main priorities for a programmer today should be:

Algorithms like Quicksort dominated sorting benchmarks for years, but they tend to result in too frequent and unordered accesses to main memory. This means merge-sort, which was suitable for linear media like punched cards and magnetic tapes, became hip again. The strategy used in ChipSort takes this and other facts into account.

More than exploiting paralelism and caching, this strategy also minimizes access to main memory, what can be especially advantageous if the objects being sorted are not just numbers, but larger structures.


To sort small arrays we employ non-branching and SIMD-friendly sorting and bitonic merge networks. This is pretty much the best approach in this case, especially if only few different input sizes must be supported. In this situation we try our best to load all the input data into the processor registers, and do as much as we can there before putting any data back into memory. In other words, we sort small sequences in the chip.

For larger arrays, ChipSort employs two stages. The first stage utilizes the same methods for small arrays to create an initial set of ordered sequences. They're made as big as it fits inside register memory before we have to start moving (too much) stuff back to the stack to carry out the calculations. A modern processor core can already offer kilobytes of register memory.

The second stage is to perform a multi-way merge of all these small sequences. They are all processed at the same time, split in small buffers which are input to the bitonic merge network. This procedure requires a binary tree that stores intermediate merged sub-sequences. This structure should fit in the cache memory.

With just two passes over the whole data in the RAM this approach can already handle thousands of entries. If the input array is so large that the merge tree is too big for the cache, then we perform more multi-way merge stages with an increasingly large block size.

The main alternative technique, not explered here at first, is based on radix sort (≈quicksort). The memory access issues can be dealt with by software managed buffers that concentrate data in the lower caches. Another technique such as sorting networks or combosort must still be employed for small arrays.

Implementation details

One interesting aspect from the ChipSort implementation is the extensive use of meta-programming, one of the greatest and most unique Julia features. Our implementation of the sorting network, bitonic merge network and matrix transpose are all based on generated functions.

This module relies on SIMD.jl whenever necessary. In special, the transpose and bitonic merge use the shufflevector function. The sorting network uses the min and max functions, which the Vec class supports.

Another notable implementation aspect is the use of non-temporal memory access, which prevents cache pollution and also improves writing throughput.


To find out more about the performance gains ChipSort can provide, check our benchmark documentation page. Note that this project is still young, and a lot of work is necessary to offer a reliable sort guaranteed to be as good as e.g. the one offered by the Julia standard library. Our first priority is to be a laboratory for implementing generic SIMD-based sorting techniques in Julia.


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