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THE INDUSTRIAL REVOLUTION OF DATA

revsorg (FLICKR). THE INDUSTRIAL REVOLUTION OF DATA. logs sensors cameras gps ... . CRISIS (?) IN COMPUTING. moore’s law, derailed processor clock rates have stopped growing storage, memory, network continue exponentiating solution (?): multicore many processors on a single chip

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THE INDUSTRIAL REVOLUTION OF DATA

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  1. revsorg (FLICKR) THE INDUSTRIAL REVOLUTION OF DATA • logs • sensors • cameras • gps • ...

  2. CRISIS (?) IN COMPUTING • moore’s law, derailed • processor clock rates have stopped growing • storage, memory, network continue exponentiating • solution (?): multicore • many processors on a single chip • massive parallelism (for the masses)

  3. CRISIS AGAIN! • can’t clean up SW that’s already out • future does not look pretty either • few developers can produce good parallel code

  4. HEARD THIS BEFORE? • “dead parallel computer society” • Convex, Encore, Floating Point Systems, INMOS, Kendall Square Research, MasPar, nCUBE, Sequent, Thinking Machines... • shared-nothing databases • Gamma, Bubba, Teradata

  5. NASA vs. FEDEX

  6. DATAFLOW PARALLELISM • split up a large set of inputs. not the algorithm. • SQL (IBM, 1974) • widely adopted in enterprises • MapReduce (Google, 2002) • widely adopted by hackers, students, algorithmicists • very, very similar master QD processslice 3 QE processslice 2 QE processslice 2 QE processslice 2 QE processslice 1 QE processslice 1 QE processslice 1 segment1 segment2 segment3

  7. SQL OR MAP/REDUCE? • yes. • tradeoffs in programmability/usability • compatibility • cultural diversity • mix and match • see above

  8. FROM FLEETS TO SPACESHIPS • alas, parallel dataflow only works for data • wait ... nearly everything is data!

  9. RETHINKING PROGRAMMING • data-centric, declarative, distributed • NWing, robotics, machine learning, NLP, games, cloud infrastructure...

  10. RESEARCH: LINCOLN& BOOM • lincoln • a data-centric language for parallel computing • the cloud goes BOOM! • Berkeley Orders of Magnitude • OOM bigger systems,OOM less code • distributed filesystem, parallel dataflow infrastructure • built out of dataflow! • evolution: incremental ‘ilities with minimal effort

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