A Specialized Architecture for Object Serialization with Applications to Big Data Analytics
- Authors
- Jang, J.[Jang, J.]; Jung, S.J.[Jung, S.J.]; Jeong, S.[Jeong, S.]; Heo, J.[Heo, J.]; Shin, H.[Shin, H.]; Ham, T.J.[Ham, T.J.]; Lee, J.W.[Lee, J.W.]
- Issue Date
- 2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Apache Spark; Data analytics; Domain-specific architecture; Hardware-software co-design; Object serialization
- Citation
- Proceedings - International Symposium on Computer Architecture, v.2020-May, pp.322 - 334
- Indexed
- SCOPUS
- Journal Title
- Proceedings - International Symposium on Computer Architecture
- Volume
- 2020-May
- Start Page
- 322
- End Page
- 334
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/6760
- DOI
- 10.1109/ISCA45697.2020.00036
- ISSN
- 1063-6897
- Abstract
- Object serialization and deserialization (S/D) is an essential feature for efficient communication between distributed computing nodes with potentially non-uniform execution environments. S/D operations are widely used in big data analytics frameworks for remote procedure calls and massive data transfers like shuffles. However, frequent S/D operations incur significant performance and energy overheads as they must traverse and process a large object graph. Prior approaches improve S/D throughput by effectively hiding disk or network I/O latency with computation, increasing compression ratio, and/or application-specific customization. However, inherent dependencies in the existing (de)serialization formats and algorithms eventually become the major performance bottleneck. Thus, we propose Cereal, a specialized hardware accelerator for memory object serialization. By co-designing the serialization format with hardware architecture, Cereal effectively utilizes abundant parallelism in the S/D process to deliver high throughput. Cereal also employs an efficient object packing scheme to compress metadata such as object reference offsets and a space-efficient bitmap representation for the object layout. Our evaluation of Cereal using both a cycle-level simulator and synthesizable Chisel RTL demonstrates that Cereal delivers 43.4× higher average S/D throughput than 88 other S/D libraries on Java Serialization Benchmark Suite. For six Spark applications Cereal achieves 7.97× and 4.81× speedups on average for S/D operations over Java built-in serializer and Kryo, respectively, while saving S/D energy by 227.75× and 136.28×. © 2020 IEEE.
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