GenPIP：通过碱基识别与读段比对的紧密集成实现基因组分析的内存内加速
GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping
AI总结：
针对纳米孔测序中碱基识别与读段比对分离执行导致的高数据移动和冗余计算问题，提出紧密集成二者的内存内加速器GenPIP，通过协同并行执行与提前剔除无效读段，显著提升基因组分析性能并降低能耗。
摘要：
纳米孔测序是一种被广泛应用的高通量基因组测序技术，能够以低成本将基因组的长片段测序为原始电信号。为实现准确的下游基因组分析，纳米孔测序需要两个计算成本高昂的处理步骤。第一步是碱基识别（basecalling），将原始电信号转换为核苷酸碱基（即A、C、G、T）。第二步是读段比对（read mapping），找到一条读段在参考基因组中的正确位置。在现有的基因组分析流程中，碱基识别和读段比对是分开执行的。我们在本研究中观察到，这两个最耗时步骤的分离执行本质上会导致（1）大量的数据移动，以及（2）数据上的冗余计算，从而拖慢基因组分析流程。本文提出了GenPIP，这是一种紧密集成碱基识别与读段比对的内存内基因组分析加速器。GenPIP通过两种关键机制提升基因组分析流程的性能：（1）主要基因组分析步骤在内存内的细粒度协同并行执行；（2）一种用于提前剔除低质量和无法比对的读段的新技术，可及时终止这类读段的基因组分析执行，减少无效计算。我们的实验表明，在执行基因组分析流程时，与在最先进的CPU（GPU）上运行的最先进软件基因组分析工具相比，GenPIP实现了41.6倍（8.4倍）的速度提升和32.8倍（20.8倍）的能耗节省，且精度损失可忽略不计。与结合了最先进的内存内碱基识别和读段比对加速器的设计相比，GenPIP实现了1.39倍的速度提升和1.37倍的能耗节省。
英文摘要：
Nanopore sequencing is a widely-used high-throughput genome sequencing technology that can sequence long fragments of a genome into raw electrical signals at low cost. Nanopore sequencing requires two computationally-costly processing steps for accurate downstream genome analysis. The first step, basecalling, translates the raw electrical signals into nucleotide bases (i.e., A, C, G, T). The second step, read mapping, finds the correct location of a read in a reference genome. In existing genome analysis pipelines, basecalling and read mapping are executed separately. We observe in this work that such separate execution of the two most time-consuming steps inherently leads to (1) significant data movement and (2) redundant computations on the data, slowing down the genome analysis pipeline. This paper proposes GenPIP, an in-memory genome analysis accelerator that tightly integrates basecalling and read mapping. GenPIP improves the performance of the genome analysis pipeline with two key mechanisms: (1) in-memory fine-grained collaborative execution of the major genome analysis steps in parallel; (2) a new technique for early-rejection of low-quality and unmapped reads to timely stop the execution of genome analysis for such reads, reducing inefficient computation. Our experiments show that, for the execution of the genome analysis pipeline, GenPIP provides 41.6X (8.4X) speedup and 32.8X (20.8X) energy savings with negligible accuracy loss compared to the state-of-the-art software genome analysis tools executed on a state-of-the-art CPU (GPU). Compared to a design that combines state-of-the-art in-memory basecalling and read mapping accelerators, GenPIP provides 1.39X speedup and 1.37X energy savings.
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