An Asynchronous Distributed-Memory Parallel Algorithm for k-mer Counting

TL;DR

This paper introduces an asynchronous distributed-memory algorithm for k-mer counting in DNA sequences, significantly improving speed and scalability by reducing communication overhead in large-scale computational biology workloads.

Contribution

The paper presents a novel asynchronous algorithm (DAKC) that reduces global communication and enhances performance for k-mer counting on large distributed systems.

Findings

Performs strong scaling up to 256 nodes

Counts k-mers up to 9x faster than existing algorithms

Achieves up to 100x speedup over shared-memory methods

Abstract

This paper describes a new asynchronous algorithm and implementation for the problem of k-mer counting (KC), which concerns quantifying the frequency of length k substrings in a DNA sequence. This operation is common to many computational biology workloads and can take up to 77% of the total runtime of de novo genome assembly. The performance and scalability of the current state-of-the-art distributed-memory KC algorithm are hampered by multiple rounds of Many-To-Many collectives. Therefore, we develop an asynchronous algorithm (DAKC) that uses fine-grained, asynchronous messages to obviate most of this global communication while utilizing network bandwidth efficiently via custom message aggregation protocols. DAKC can perform strong scaling up to 256 nodes (512 sockets / 6K cores) and can count k-mers up to 9x faster than the state-of-the-art distributed-memory algorithm, and up to 100x faster than the shared-memory alternative. We also provide an analytical model to understand the hardware resource utilization of our asynchronous KC algorithm and provide insights on the performance.