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CIC-PIM: Trading spare computing power for memory space in graph processing
Journal of Parallel and Distributed Computing ( IF 3.8 ) Pub Date : 2020-09-22 , DOI: 10.1016/j.jpdc.2020.09.008
Yongxuan Zhang , Hong Jiang , Fang Wang , Yu Hua , Dan Feng , Yongli Cheng , Yuchong Hu , Renzhi Xiao

Shared-memory graph processing is usually more efficient than in a cluster in terms of cost effectiveness, ease of programming and runtime. However, the limited memory capacity of a single machine and the huge sizes of graphs restrains its applicability. Hence, it is imperative to reduce memory footprint. We observe that index compression holds promise and propose CIC-PIM, a lightweight encoding with chunked index compression, to reduce the memory footprint and the runtime of graph algorithms. CIC-PIM aims for significant space saving, real random-access support and high cache efficiency by exploiting the ubiquitous power-law and sparseness features of large scale graphs. The basic idea is to divide index structures into chunks of appropriate size and compress the chunks with our lightweight fixed-length byte-aligned encoding. After CIC-PIM compression, two-fold larger graphs are processed with all data fit in memory, resulting in speedups or fast in-memory processing unattainable previously.



中文翻译:

CIC-PIM:将多余的计算能力用于图形处理中的存储空间

就成本效益,易于编程和运行时而言,共享内存图处理通常比集群中更高效。但是,一台机器的有限内存容量和庞大的图形限制了其适用性。因此,必须减少内存占用量。我们观察到索引压缩有望实现,并提出了CIC-PIM(一种具有分块索引压缩的轻量级编码),以减少内存占用量和图形算法的运行时间。CIC-PIM的目标是通过利用大规模图形的普适幂律和稀疏特性来显着节省空间,提供真正的随机访问支持并提高缓存效率。基本思想是将索引结构划分为适当大小的块,并使用我们的轻量级固定长度字节对齐编码压缩这些块。

更新日期:2020-09-29
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