NMP-PaK：可扩展从头基因组组装的近内存处理加速
NMP-PaK: Near-Memory Processing Acceleration of Scalable De Novo Genome Assembly
AI总结：
本文提出软硬件协同设计 NMP-PaK，利用通道级近内存处理、定制处理单元、批处理和 CPU-NMP 混合执行加速可扩展从头基因组组装，显著降低内存占用并提升性能与吞吐量。
摘要：
从头（de novo）组装使研究未知基因组成为可能，为个性化医疗和疾病管理铺平了道路。然而，由于数据量过大和算法复杂，它面临巨大的计算挑战。尽管最先进的从头组装器利用分布式系统进行超大规模基因组组装，但它们需要大量计算和内存资源。它们也未能解决从头组装的内在挑战，包括巨大的内存占用、受内存限制的行为，以及由复杂、相互依赖的数据结构产生的不规则数据模式。鉴于这些挑战，从头组装值得采用定制硬件方案，但现有方法尚未完全解决这些局限。我们提出 NMP-PaK，这是一种通过近内存处理（NMP）加速可扩展从头基因组组装的软硬件协同设计。我们的通道级 NMP 架构在解决内存瓶颈的同时，为处理单元提供充足的暂存空间。定制处理单元最大化并行性，同时高效处理既动态又相互依赖的大型数据结构。软件优化包括用于降低内存占用的定制批处理，以及用于解决不规则数据模式导致的硬件利用率不足问题的 CPU-NMP 混合处理。NMP-PaK 执行相同的基因组组装，同时与最先进的从头组装相比内存占用减小 14 倍。此外，NMP-PaK 相比 CPU 基线实现 16 倍性能提升，内存操作减少 2.4 倍。因此，在相同资源约束下，NMP-PaK 比最先进的从头组装实现 8.3 倍更高的吞吐量，展示了其卓越的计算效率。
英文摘要：
De novo assembly enables investigations of unknown genomes, paving the way for personalized medicine and disease management. However, it faces immense computational challenges arising from the excessive data volumes and algorithmic complexity. While state-of-the-art de novo assemblers utilize distributed systems for extreme-scale genome assembly, they demand substantial computational and memory resources. They also fail to address the inherent challenges of de novo assembly, including a large memory footprint, memory-bound behavior, and irregular data patterns stemming from complex, interdependent data structures. Given these challenges, de novo assembly merits a custom hardware solution, though existing approaches have not fully addressed the limitations. We propose NMP-PaK, a hardware-software co-design that accelerates scalable de novo genome assembly through near-memory processing (NMP). Our channel-level NMP architecture addresses memory bottlenecks while providing sufficient scratchpad space for processing elements. Customized processing elements maximize parallelism while efficiently handling large data structures that are both dynamic and interdependent. Software optimizations include customized batch processing to reduce the memory footprint and hybrid CPU-NMP processing to address hardware underutilization caused by irregular data patterns. NMP-PaK conducts the same genome assembly while incurring a 14X smaller memory footprint compared to the state-of-the-art de novo assembly. Moreover, NMP-PaK delivers a 16X performance improvement over the CPU baseline, with a 2.4X reduction in memory operations. Consequently, NMP-PaK achieves 8.3X greater throughput than state-of-the-art de novo assembly under the same resource constraints, showcasing its superior computational efficiency.
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