zSort: Stable Distribution Sort using Z-Score Partitioning
Hriday Jain, Ketan Sabale, Aditya Shastri, Hiren Kumar Thakkar, Ashutosh Londhe

TL;DR
zSort is a new adaptive distribution sorting algorithm that guarantees stability, improves performance over traditional stable sorts, and narrows the gap with unstable algorithms, suitable for diverse data distributions.
Contribution
Introduces zSort, a stable, distribution-based sorting algorithm that avoids pass complexity scaling with key-width and outperforms existing stable sorts in speed.
Findings
zSort achieves lower bad-speculation overhead (19.7%) than stable baselines.
Demonstrates 3x-4.5x speedup over comparison-based stable sorts.
Maintains high throughput comparable to unstable algorithms like Skasort.
Abstract
Sorting is a foundational primitive in modern data processing, influencing the execution speed of high-performance data pipelines. However, the algorithmic landscape is currently bifurcated by a pervasive "Stability Tax": practitioners must sacrifice either order preservation for high throughput or execution speed for stability. To address these limitations, this paper introduces, zSort, an adaptive z-score based distribution sorting algorithm that guarantees stability while avoiding pass complexity that scales with key-width. The performance of the proposed technique is evaluated using Microarchitectural analysis and experimental results. Microarchitectural analysis shows that zSort achieves a lower bad-speculation overhead (19.7%) than both stable baselines and several high-performance unstable algorithms and sustains a competitive IPC of 1.44. Empirical evaluation across diverse…
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