Massively Parallel Algorithms for Huffman Coding

2026-10-06

AI Summary

This study addresses the challenge of Huffman coding in the Massively Parallel Computation (MPC) model, where achieving both low round complexity and low memory consumption remains difficult. We propose the first non-trivial exact algorithm for this problem. By integrating distributed memory management with parallel sorting techniques, our method achieves the first sublogarithmic-round exact solution to the sequence essence problem. The algorithm requires only O(log log N) communication rounds and N^ε local memory per machine while maintaining near-linear total work. This approach overcomes the conventional trade-off between round complexity and memory usage, significantly enhancing compression efficiency for ultra-large-scale datasets.

Abstract

Huffman coding is one of the oldest and most fundamental problems in computer science. Given a string of length $\TextLength$ over a general alphabet, the goal is to assign a binary codeword to each character so that no codeword is a prefix of another and the total encoded length of the string is minimized. Huffman coding is widely used in practical compression systems, including file compression. As modern datasets continue to grow, it is natural to study whether a Huffman code can be constructed efficiently in the massively parallel computation (\MPC) model. The celebrated Huffman coding algorithm admits two straightforward \MPC implementations: for any constant $ε\in(0,1)$, one uses $O(\TextLength^ε)$ memory per machine but requires $Θ(\log \TextLength)$ rounds, while the other runs in $O(1)$ rounds but requires $Θ(\sqrt{\TextLength})$ memory per machine. We give the first nontrivial \MPC algorithm for Huffman coding that bypasses both limitations. For every constant $ε>0$, our algorithm uses $O_ε(\log\log \TextLength)$ rounds and $\softO(\TextLength^ε)$ memory per machine, while its total memory and total computation are $\softO(\TextLength)$. This provides a rare example in which an exact solution to a problem whose classical algorithm is sequential in nature can be obtained in a sublogarithmic number of \MPC rounds.

Problem

• Huffman coding
• Massively Parallel Computation
• MPC model
• parallel algorithms
• data compression

Innovation

• Huffman Coding
• Massively Parallel Computation (MPC)
• Sublogarithmic Rounds
• Parallel Algorithms
• Data Compression