KrakenOnMem
Taha Shahroodi, Mahdi Zahedi, Abhairaj Singh, Stephan Wong, Said Hamdioui
Abstract
State-of-the-art taxonomic profilers that comprise the first step in larger-context metagenomic studies have proven to be computationally intensive, i.e., while accurate, they come at the cost of high latency and energy consumption. Table Lookup operation is a primary bottleneck of today's profilers. In this paper, we first propose TL-PIM, a hardware accelerator based on the processing-in-memory (PIM) paradigm to accelerate Table Lookup. TL-PIM leverages the in-memory compute capability of emerging memory technologies along with intelligent data mapping. Then, we integrate TL-PIM into Kraken2, a state-of-the-art metagenomic profiler, and build an HW/SW co-designed profiler, called KrakenOnMem. Results from a silicon-based prototype of our emerging memory validate the design and required operations on a smaller scale. Our large-scale calibrated simulations show that KrakenOnMem can provide an average of 61.3% speedup compared to original Kraken2 for end-to-end profiling. Additionally, our design improves the energy consumption by orders of magnitude compared to the original Kraken2 while incurring a negligible area overhead.