An Asynchronous Distributed-Memory Parallel Algorithm for k-mer Counting
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909603470508032 |
|---|---|
| author | Hati, Souvadra Hayashi, Akihiro Vuduc, Richard |
| author_facet | Hati, Souvadra Hayashi, Akihiro Vuduc, Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04431 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Asynchronous Distributed-Memory Parallel Algorithm for k-mer Counting Hati, Souvadra Hayashi, Akihiro Vuduc, Richard Distributed, Parallel, and Cluster Computing Genomics 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. |
| title | An Asynchronous Distributed-Memory Parallel Algorithm for k-mer Counting |
| topic | Distributed, Parallel, and Cluster Computing Genomics |
| url | https://arxiv.org/abs/2505.04431 |