Thu 16 Jun 2016 13:30 - 14:00 at Grand Ballroom Santa Ynez - Synthesis I Chair(s): Eran Yahav

By abstracting away the complexity of distributed systems, large-scale data processing platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple means for harnessing the power of the cloud. In this paper, we ask whether we can automatically synthesize MapReduce-style distributed programs from input–output examples. Our ultimate goal is to enable end users to specify large-scale data analyses through the simple interface of examples. We thus present a new algorithm and tool for synthesizing programs composed of efficient data-parallel operations that can execute on cloud computing infrastructure. We evaluate our tool on a range of real-world big-data analysis tasks and general computations. Our results demonstrate the efficiency of our approach and the small number of examples it requires to synthesize correct, scalable programs.

Thu 16 Jun

Displayed time zone: Tijuana, Baja California change

13:30 - 15:00
Synthesis IResearch Papers at Grand Ballroom Santa Ynez
Chair(s): Eran Yahav Technion
13:30
30m
Talk
MapReduce Program Synthesis
Research Papers
Calvin Smith University of Wisconsin - Madison, Aws Albarghouthi University of Wisconsin–Madison
Media Attached
14:00
30m
Talk
Programmatic and Direct Manipulation, Together at Last
Research Papers
Ravi Chugh University of Chicago, Brian Hempel University of Chicago, Mitchell Spradlin University of Chicago, Jacob Albers University of Chicago
Pre-print Media Attached
14:30
30m
Talk
Fast Synthesis of Fast Collections
Research Papers
Calvin Loncaric University of Washington, Emina Torlak University of Washington, Michael D. Ernst University of Washington
Media Attached