Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.16470 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915167650971648 |
|---|---|
| author | Lim, Se-Min Kang, Seongyoung Jun, Sang-Woo |
| author_facet | Lim, Se-Min Kang, Seongyoung Jun, Sang-Woo |
| contents | This paper presents Bancroft, a computational genomics acceleration platform that provides the illusion of practically infinite on-device memory capacity by compressing genomic data movement over PCIe. Bancroft introduces novel optimizations for efficient accelerator implementation to reference-based genome compression, including fixed-stride matching using cuckoo hashes and grouped header encoding, incorporated into a familiar interface supporting random accesses. We evaluate a prototype implementation of Bancroft on an affordable Alveo U50 FPGA equipped with 8 GB of HBM. Thanks to the orders of magnitude improvements in performance and resource efficiency of genomic compression, our prototype provides access to TBs of host-side genomic data at memory-class performance, measuring speeds over 30% of the on-device HBM bandwidth, an order of magnitude higher than conventional PCIe-limited architectures. Using a real-world pre-alignment filtering application, Bancroft demonstrates over 6x improvement over the conventional PCIe-attached architecture, achieving 30% of peak internal throughput of an accelerator with HBM, and 90% of the one with DDR4. Bancroft supports memory-class performance to practically infinite data capacity, using a small, fixed amount of HBM, making it an attractive solution to continued future scalability of computational genomics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bancroft: Genomics Acceleration Beyond On-Device Memory Lim, Se-Min Kang, Seongyoung Jun, Sang-Woo Hardware Architecture This paper presents Bancroft, a computational genomics acceleration platform that provides the illusion of practically infinite on-device memory capacity by compressing genomic data movement over PCIe. Bancroft introduces novel optimizations for efficient accelerator implementation to reference-based genome compression, including fixed-stride matching using cuckoo hashes and grouped header encoding, incorporated into a familiar interface supporting random accesses. We evaluate a prototype implementation of Bancroft on an affordable Alveo U50 FPGA equipped with 8 GB of HBM. Thanks to the orders of magnitude improvements in performance and resource efficiency of genomic compression, our prototype provides access to TBs of host-side genomic data at memory-class performance, measuring speeds over 30% of the on-device HBM bandwidth, an order of magnitude higher than conventional PCIe-limited architectures. Using a real-world pre-alignment filtering application, Bancroft demonstrates over 6x improvement over the conventional PCIe-attached architecture, achieving 30% of peak internal throughput of an accelerator with HBM, and 90% of the one with DDR4. Bancroft supports memory-class performance to practically infinite data capacity, using a small, fixed amount of HBM, making it an attractive solution to continued future scalability of computational genomics. |
| title | Bancroft: Genomics Acceleration Beyond On-Device Memory |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2502.16470 |