Accelerating RNA-Seq Quantification on a Real Processing-in-Memory System

Abstract

Recently, with the growth of the required data size for emerging applications (e.g., graph processing and machine learning), the von Neumann bottleneck has become a main problem for restricting the throughput of the applications. To address the problem, an acceleration technique called Processing in Memory (PIM) has garnered attention due to its potential to reduce off-chip data movement between the processing unit (e.g., CPU) and memory device (e.g., DRAM). In 2019, UPMEM introduced the commercially available processing-in-memory product, the DRAM Processing Unit (DPU) [8], showing a new chance for accelerating data-intensive applications. Among data-intensive applications, RNA sequence (RNA-seq) quantification is used to measure the abundance of RNA sequences, and it also plays a critical role in the field of bioinformatics. We aim to leverage UPMEM DPU to accelerate RNA-seq Quantification. However, due to the DPU usage limitations caused by DPU hardware, there are some challenges to realizing RNA-seq Quantification on the DPU system. To overcome these challenges, we propose UpPipe, which consists of the DPU-friendly transcriptome allocation, the DPU-aware pipeline management, and the WRAM prefetching scheme. The UpPipe considers the hardware limitations of DPUs, enabling efficient sequence alignment even within the resource-constrained DPUs. The experimental results demonstrate the feasibility and efficiency of our proposed design. We also provide an evaluation study on the impact of data granularity selection on pipeline management and the optimal size for the WRAM prefetching scheme.