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Automatic optimization of stream programs via source program operator graph transformations

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Abstract

Distributed data stream processing is a data analysis paradigm where massive amounts of data produced by various sources are analyzed online within real-time constraints. Execution performance of a stream program/query executed on such middleware is largely dependent on the ability of the programmer to fine tune the program to match the topology of the stream processing system. However, manual fine tuning of a stream program is a very difficult, error prone process that demands huge amounts of programmer time and expertise which are expensive to obtain. We describe an automated process for stream program performance optimization that uses semantic preserving automatic code transformation to improve stream processing job performance. We first identify the structure of the input program and represent the program structure in a Directed Acyclic Graph. We transform the graph using the concepts of Tri-OP Transformation and Bi-Op Transformation. The resulting sample program space is pruned using both empirical as well as profiling information to obtain a ranked list of sample programs which have higher performance compared to their parent program. We successfully implemented this methodology on a prototype stream program performance optimization mechanism called Hirundo. The mechanism has been developed for optimizing SPADE programs which run on System S stream processing run-time. Using five real world applications (called VWAP, CDR, Twitter, Apnoea, and Bargain) we show the effectiveness of our approach. Hirundo was able to identify a 31.1 times higher performance version of the CDR application within seven minutes time on a cluster of 4 nodes.

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Acknowledgements

This research was supported by the Japan Science and Technology Agency’s CREST project titled “Development of System Software Technologies for post-Peta Scale High Performance Computing”.

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Authors and Affiliations

  1. Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan

    Miyuru Dayarathna

  2. Department of Computer Science, Tokyo Institute of Technology/IBM Research-Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan

    Toyotaro Suzumura

Authors
  1. Miyuru Dayarathna
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  2. Toyotaro Suzumura
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Correspondence to Miyuru Dayarathna.

Additional information

Communicated by Divyakant Agrawal.

Appendices

Appendix A: Data flow graphs of the high performance sample applications

This appendix lists the optimized data flow graphs of input applications that are produced by Hirundo (Figs. 31–37).

Fig. 31
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DAG of 8SCSV_16F_AG_F_SI sample application of VWAP which produced highest consistent performance compared to the original VWAP application on cluster X

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Fig. 32
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DAG of 6SCSV_48F_AG_F_SI sample application of VWAP which produced highest consistent performance compared to the original VWAP application on cluster Y

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Fig. 33
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DAG of 4SCSV_4U_2F_4AG_4SI sample application of CDR which produced highest consistent performance compared to the original CDR application on clusters X and Y

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Fig. 34
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DAG of U_2F_2F_F_AG_SI sample application of Twitter which produced highest consistent performance compared to the original Twitter application on cluster X

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Fig. 35
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DAG of U_4F_2F_F_AG_SI sample application of Twitter which produced highest consistent performance compared to the original Twitter application on cluster Y

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Fig. 36
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DAG of SCSV_F_AG_F_2F_2AG_4F_F_AG_F_J_J_SI sample application of Apnoea which produced highest consistent performance compared to the original Apnoea application on cluster X

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Fig. 37
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DAG of SCSV_FOR_S_2F_2AG_2F_F_J_F_SI_FOR_E sample application of Bargain which produced highest consistent performance compared to the original Bargain application on cluster X

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Appendix B: Some example transformations for quadri-operator transformation

An example collection of transformed operator blocks possible using Quadri-Operator Transformation (Quadri-OP transformation) are shown in Fig. 38. Note that these Quadri-OP transformations are listed only for illustrating the complexity involved in transforming four operator blocks (A_B_C_D) at a time. We list few possible transformations only for two corresponding transformations produced by Tri-OP Transformation. Transformed operator block from Tri-OP transformation shown in Fig. 38(a) has been produced by applying 1-2-1 transformation pattern. Similar possible transformed operator blocks from Quadri-OP transformation are shown in Figs. 38(a-1) to 38(a-4). Similar analogous transformed operator blocks for Tri-OP transformed operator block of 2-2-2 transformation (shown in Fig. 38(b)) are shown in Figs. 38(b-1) to 38(b-7). These transformed operator blocks are produced only for a transformation depth of 2. Hence, transformations even at smaller transformation depths produce significantly larger numbers of transformed operator blocks for Quadri-OP transformation which makes Quadri-OP transformation not suitable for Hirundo’s code transformer. Furthermore, as can be observed from these figures such as Figs. 38(a), 38(a-3) and Figs. 38(b), 38(b-1); many of the Quadri-OP transformed operator blocks can be produced by using Tri-OP transformation along with operator fusion. Due to these reasons we conduct only up to Tri-OP transformation in Hirundo’s program generator.

Fig. 38
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Some transformations possible using Quadri-Operator Transformation

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Dayarathna, M., Suzumura, T. Automatic optimization of stream programs via source program operator graph transformations. Distrib Parallel Databases 31, 543–599 (2013). https://doi.org/10.1007/s10619-013-7130-x

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