Zenong Zhang

Ibrahim Ahmed, Clemens Schaefer, Gil Tabak et al.

While Large Language Models (LLMs) have become highly influential, their enormous scale presents significant deployment challenges. Efficiently serving these models typically requires distributing them across numerous accelerator devices, which introduces substantial performance overhead from inter-device communication (collectives). While model quantization has been widely adopted to reduce the memory and compute requirements of LLM weights and activations with minimal quality impact, applying quantization directly to collectives like AllReduce is inherently difficult due to the inter-device summation involved, which can lead to numerical instability or significant error accumulation. In this work, we present a native dynamic block-wise efficient quantized AllReduce within the XLA compiler for TPUs (EQuARX). By using TPU-friendly quantization and deep pipelining of communication and compute, EQuARX with int8 precision achieves a 1.8X speedup over baseline BF16 AllReduce across various network topologies. Furthermore, EQuARX accelerates the prefill stage of Gemma 3 27B by 1.25X and Gemma 3 12B by 1.1X, respectively, with small to negligible impact on quality.

Juan Manuel Florez, Laura Moreno, Zenong Zhang et al.

Software systems are designed according to guidelines and constraints defined by business rules. Some of these constraints define the allowable or required values for data handled by the systems. These data constraints usually originate from the problem domain (e.g., regulations), and developers must write code that enforces them. Understanding how data constraints are implemented is essential for testing, debugging, and software change. Unfortunately, there are no widely-accepted guidelines or best practices on how to implement data constraints. This paper presents an empirical study that investigates how data constraints are implemented in Java. We study the implementation of 187 data constraints extracted from the documentation of eight real-world Java software systems. First, we perform a qualitative analysis of the textual description of data constraints and identify four data constraint types. Second, we manually identify the implementations of these data constraints and reveal that they can be grouped into 30 implementation patterns. The analysis of these implementation patterns indicates that developers prefer a handful of patterns when implementing data constraints and deviations from these patterns are associated with unusual implementation decisions or code smells. Third, we develop a tool-assisted protocol that allows us to identify 256 additional trace links for the data constraints implemented using the 13 most common patterns. We find that almost half of these data constraints have multiple enforcing statements, which are code clones of different types.