When you want to speed up your program, the obvious step is to recall the learnings of your data structure class and optimize the algorithmic complexi

Why You Shouldn’t Forget to Optimize the Data Layout

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2024-10-09 13:30:03

When you want to speed up your program, the obvious step is to recall the learnings of your data structure class and optimize the algorithmic complexity. Clearly, algorithms are the star of each program as swapping a hot O(n) algorithm with a lower complexity one, such as O(log n), yields almost arbitrary performance improvements. However, the way data is structured also affects performance significantly: Programs run on physical machines with physical properties such as varying data latencies to caches, disks, or RAM. After you optimized your algorithm, you need to consider these properties to achieve the best possible performance. An optimized data layout takes your algorithms and access patterns into account when deciding on how to store the bytes of your data structure on physical storage. Therefore, it can make your algorithms run several times faster. In this blog post, we will show you an example where we can achieve a 4x better read performance by just changing the data layout according to our access pattern.

Modern hardware, particularly the CPU, is designed to process data in specific ways. The arrangement of data in memory affects how efficiently your program can use the CPU’s cache, how often it suffers from cache misses, and how well it can leverage vectorized instructions (SIMD). Even with optimal algorithms, poor data layout can lead to frequent cache reloads, stalled pipelines, and excessive memory transfers, all of which reduce performance.