This document summarizes Push, a dataflow shell that allows users to define dataflow pipelines using shell-like syntax. The shell treats everything as a pipe and aims to orchestrate dataflow execution across multiple machines. It supports features like record handling, output/input record filtering, and configurable parallelism. Research challenges include optimizing exascale pipelines, cloud integration, and work stealing. The goal is an interactive system for defining and executing data-parallel workflows across platforms.

1. Push: a Dataflow Shell
1. Observation
(Made by Streamline etc...) This
Noah Evans, Eric Van Hensbergen
command...
This...
f1 |< f3 >| f5
2. If everything’s a pipe in Dataflow
programming, why not use a shell?
... transforms to this syntax tree ...
>| ... which becomes this dataflow
pipelined command set.
... is just a large cmd |< cmd
1
combination of f3 pipe f5
0
these: $ cmd cmd cmd f5 0 14
1 pipe
pipe
10 f4
f1 irf $ f1 f3
pipe
13 1
1 6
0 f3
orf 9 pipe
pipe 0 f2
pipe
1 pipe 1
0 5
f1
0
pipe f3
f2
3. How? 4. Dataflow pipes 5. Record handling in pipes
•Shell should be orchestrator cmd1 |< cmd2 >| cmd3 •User Defined: Implicit or Explicit
•Need a way to do Pipe → •ORF (output record filter)
! |< Fanout: one to many
Fork → Exec over a large •default hashes 1 to many
Number of machines ! >| Fanin: many to one •newline separated
•Need a way of moving to ! Must be paired •IRF (Input Record Filter)
records from byte streams •default merges buffers on newlines
6. Research Challenges + Future Work 7. Conclusions
•Exascale Pipe → Fork → Exec •Systems level not language level
•Graph optimization at XCPU3 •Easy to change record handling
•Cloud Integration •Configurable degree of parallelism
•Work-stealing •Cross Platform (Win32, Linux, OSX)
•Not Batch, Interactive
Job Distribution
See Also:
laptop → GPUtask → Celltask → BG/Ptask http://www.research.ibm.com/hare
http://code.google.com/p/push/
References
•Willem de Bruijn. Adaptive Operating System Design for High Throughput I/O.
PhD thesis, Vrije Universiteit Amsterdam, 2010.
XCPU3 •M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel
programs from sequential building blocks. In Proceedings of the 2007 conference on
EuroSys, pages 59–72. ACM Press New York, NY, USA, 2007.
This work has been supported by the Department of Energy Of Office of Science
See XCPU3 poster for more Operating and Runtime Systems For Extreme Scale Scientific Computation project under
details on job distribution contract #DE-FG02-08ER25851