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引用次数: 20
摘要
在本文中,我们首次提出了一种基于优化和硬件友好的基因组组装算法的高通量和节能的processing - In - ram加速基因组组装器,称为PIM-Assembler。PIM-Assembler可以从全对重叠中组装大规模的DNA序列数据集。我们首先开发了PIM-Assembler平台,该平台利用DRAM作为计算存储器,并将其转换为基因组组装的基本处理单元。PIM-Assembler可以在DRAM内部执行高效的基于X(N) or的操作,在商品DRAM设计的基础上产生低成本(约占芯片面积的5%)。然后,PIM-Assembler通过相关的数据划分和映射方法进行优化,该方法允许DNA短读段的本地存储和处理,以充分利用基因组组装算法级别的并行性。仿真结果表明,PIM-Assembler在执行基于大容量位xnor的比较操作时,平均比CPU和最近的dram处理平台分别提高8.4倍和2.3倍的吞吐量。对于比较/添加广泛的基因组组装应用,与GPU相比,它的执行时间和功耗分别减少了约5倍和约7.5倍。
PIM-Assembler: A Processing-in-Memory Platform for Genome Assembly
In this paper, for the first time, we propose a high-throughput and energy-efficient Processing-in-DRAM-accelerated genome assembler called PIM-Assembler based on an optimized and hardware-friendly genome assembly algorithm. PIM-Assembler can assemble large-scale DNA sequence dataset from all-pair overlaps. We first develop PIM-Assembler platform that harnesses DRAM as computational memory and transforms it to a fundamental processing unit for genome assembly. PIM-Assembler can perform efficient X(N)OR-based operations inside DRAM incurring low cost on top of commodity DRAM designs (∼5% of chip area). PIM-Assembler is then optimized through a correlated data partitioning and mapping methodology that allows local storage and processing of DNA short reads to fully exploit the genome assembly algorithm-level’s parallelism. The simulation results show that PIM-Assembler achieves on average 8.4× and 2.3 wise× higher throughput for performing bulk bit-XNOR-based comparison operations compared with CPU and recent processing-in-DRAM platforms, respectively. As for comparison/addition-extensive genome assembly application, it reduces the execution time and power by ∼5× and ∼ 7.5× compared to GPU.