High-Throughput Image Alignment For Connectomics Using Frugal Snap Judgments

2020 IEEE HIGH PERFORMANCE EXTREME COMPUTING CONFERENCE (HPEC)(2020)

引用 0|浏览18
暂无评分
摘要
The accuracy and computational efficiency of image alignment directly affects the advancement of connectomics, a field which seeks to understand the structure of the brain through electron microscopy.We introduce the algorithms Quilter and Stacker that are designed to perform 2D and 3D alignment respectively on petabyte-scale data sets from connectomics. Quilter and Stacker are efficient, scalable, and can run on hardware ranging from a researcher's laptop to a large computing cluster. On a single 18-core cloud machine each algorithm achieves throughputs of more than 1 TB/hr; when combined the algorithms produce an end-to-end alignment pipeline that processes data at a rate of 0.82 TB/hr - an over 10x improvement from previous systems. This efficiency comes from both traditional optimizations and from the use of "Frugal Snap Judgments" to judiciously exploit performance-accuracy trade-offs.A high-throughput image-alignment pipeline was implemented using the Quilter and Stacker algorithms and its performance was evaluated using three datasets whose size ranged from 550 GB to 38 TB. The full alignment pipeline achieved a throughput of 0.6-0.8 TB/hr and 1.4-1.5 TB/hr on an 18-core and 112-core sharedmemory multicore, respectively. On a supercomputing cluster with 200 nodes and 1600 total cores, the pipeline achieved a throughput of 21.4 TB/hr.
更多
查看译文
关键词
frugal snap judgments,112-core shared-memory multicore,18-core cloud machine,end-to-end image-alignment pipeline,Quilter algorithms,Stacker algorithms,electron microscopy,optimizations,memory size 550.0 GByte to 38.0 TByte
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要