PIM-Assembler: A Processing-in-Memory Platform for Genome Assembly
PIM-Assembler: A Processing-in-Memory Platform for Genome Assembly
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DOI：
10.1109/dac18072.2020.9218653
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Shaahin Angizi;N. Fahmi;W. Zhang;Deliang Fan
中科院分区：
文献类型：
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作者：
Shaahin Angizi;N. Fahmi;W. Zhang;Deliang Fan
在本文中，我们首次提出了一个基于优化且适合硬件友好的基因组组装算法的高通量和节能的基因组组装器，称为PIM-Asmbler。 -Scale DNA序列数据集来自全对重叠。在商品DRAM设计之上产生低成本的DRAM（约5％的芯片区域），然后通过相关的数据分配和映射方法进行优化算法级别的仿真结果表明，与CPU和最近的DRAM平台相比，PIM组装器平均实现了8.4×和2.3的明智×较高的吞吐量与GPU相比，比较/添加扩展基因组组装应用程序将执行时间和功率降低约5倍，约7.5倍。
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.