Vectorized and performance-portable Quicksort (opensource.googleblog.com)

Article summary

The article discusses a new implementation of the Quicksort algorithm that is vectorized and performance-portable, achieving a 9-19x speedup compared to the standard library implementation. The implementation uses SIMD instructions to sort data in parallel, resulting in significant performance gains. The article highlights the potential benefits of using vectorized algorithms for intensive operations like sorting. The implementation is open-source and available for use.

Main themes

• vectorized algorithms
• SIMD instructions
• performance optimization
• thermal throttling
• power consumption
• database storage formats
• algorithm comparison
• hardware limitations

What commenters say

• The use of SIMD instructions can lead to significant performance gains for intensive operations like sorting, but may also cause thermal throttling and power limits.
• Comparing the new implementation to the standard library implementation may not be entirely fair, as the standard implementation is known to be slow due to constraints.
• The new implementation may not be suitable for all use cases, such as row-wise stored data in databases, but could be beneficial for columnar storage systems.
• The impact of AVX instructions on CPU performance and power consumption is a complex issue, with some arguing that it can cause significant throttling and others claiming that it can be almost free in certain scenarios.
• The lack of comparison to existing SIMD-based sorting algorithms, such as djbsort, is a notable omission in the paper.
• The use of AVX instructions can have side effects that negatively affect the performance of other operations, but this issue has been largely mitigated in recent hardware.
• Integrating the new implementation into existing databases, such as Postgres, may be challenging due to the row-wise storage format, but could be possible with columnar extensions or modifications.