Transformer: A New Paradigm for Building Data-Parallel Programming Models

Authors:
Peng Wang;Dan Meng;Jizhong Han;Jianfeng Zhan;Bibo Tu;Xiaofeng Shi;Le Wan
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences;Institute of Computing Technology, Chinese Academy of Sciences;Institute of Computing Technology, Chinese Academy of Sciences;Institute of Computing Technology, Chinese Academy of Sciences;Institute of Computing Technology, Chinese Academy of Sciences;Tencent Corporation;Tencent Corporation
Venue:
IEEE Micro
Year:
2010

Citing 0
Cited 2

Automatic performance debugging of SPMD-style parallel programs

Journal of Parallel and Distributed Computing
Input data organization for batch processing in time window based computations

Proceedings of the 28th Annual ACM Symposium on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cloud computing drives the design and development of diverse programming models for massive data processing. The Transformer programming framework aims to facilitate the building of diverse data-parallel programming models. Transformer has two layers: a common runtime system and a model-specific system. Using Transformer, the authors show how to implement three programming models: Dryad-like data flow, MapReduce, and All-Pairs.