BunchBloomer: Cost-Effective Bloom Filter Accelerator for Genomics Applications
Seongyoung Kang, Tarun Sai Ganesh Nerella, Shashank Uppoor, Sang-Woo Jun · 2022
Bloom filters are a very important tool for many applications including genomics, where they are used as a compact data structure for counting k-mers, represent de Bruijn graphs, and more. Due to their random-access nature coupled with the large size required for genomics, Bloom filters for genomics can easily become bound by the random access performance of off-chip memory. This is especially true for accelerators such as FPGAs and GPUs, which can easily remove the computation overhead of the multiple hash functions. As a result, Bloom filter accelerators have typically focused either on small filters which can fit in fast on-chip memory, or require fast off-chip memory fabric such as Hybrid Memory Cubes. In this work, we present BunchBloomer, which improves the cost-effectiveness of FPGA Bloom filter accelerators by making better use of cheaper, lower-power DDR memory. BunchBloomer uses a multi-layer radix sorter to group table updates into bursts directed to the same 8 KiB memory region, which can be efficiently cached in on-chip memory. A single BunchBloomer device outperforms a costly 12-core server by over 2×, demonstrating an order of magnitude better power efficiency. It even achieves better power efficiency compared to published FPGA Bloom filter accelerators equipped with Hybrid Memory Cubes.