Save 37% off PRO during our Black Friday Sale! »

The Morning Paper: foundations & frontiers

The Morning Paper: foundations & frontiers

Talk from Craft-Conf 2017

6fb292826ed5ca167629b80525873651?s=128

Adrian Colyer

April 28, 2017
Tweet

Transcript

  1. The Morning Paper: foundations and frontiers Adrian Colyer @adriancolyer

  2. blog.acolyer.org 550 Foundations Frontiers

  3. Brain storm 01 02 05 04 rainstorm 03 5 Reasons

    to <3 Papers Thinking tools Raise Expectations Applied Lessons Order of magnitude breakthroughs Heads-up 3
  4. 4 01 02 03 04 05 Software development Distributed Systems

    & Big Data Infrastructure implications Security ML & DL
  5. Software Development 5

  6. None
  7. $PRGXOHLVDXQLWRIZRUNDVVLJQPHQW  6KRUWHQGHYHORSPHQW WLPH  ,PSURYHV\VWHP IOH[LELOLW\  ,PSURYH XQGHUVWDQGDELOLW\!

    EHWWHURYHUDOOGHVLJQ Ɣ ,QGHSHQGHQW GHSOR\PHQW Ɣ )LQHJUDLQHGVFDOLQJ Ɣ )DXOWLVRODWLRQ
  8. &RS\ULJKW0D[LP3RSRY5)6WRFN3KRWR ŸEYVVWWVTeZgV_Vdd`W R^`Uf]RcZkReZ`_Zd UVaV_UV_efa`_eYV TcZeVcZRfdVUZ_ UZgZUZ_XeYVdjdeV^ Z_e`^`Uf]VdŹ

  9. '/-/

  10. Circa 1979 (& 2016!) &RPPRQ3UREOHPV  :HZHUHEHKLQGVFKHGXOHDQG ZDQWHGWRGHOLYHUDQHDUO\ UHOHDVHEXWIRXQGWKDWZH FRXOGQşWVXEVHWWKHV\VWHP

     :HZDQWHGWRDGGDVLPSOH IHDWXUHEXWIRXQGLWZRXOG KDYHUHTXLUHGUHZULWLQJDOORU PRVWRIWKHFXUUHQWFRGH  :HZDQWHGWRVLPSOLI\WKH V\VWHPE\UHPRYLQJVRPH IHDWXUHEXWWDNLQJDGYDQWDJH RILWPHDQWUHZULWLQJODUJH VHFWLRQVRIWKHFRGH  :HZDQWHGDFXVWRP GHSOR\PHQW HJLQGHYRUWHVW HQYLURQPHQWV EXWWKHV\VWHP ZDVQşWIOH[LEOHHQRXJK
  11. J>;HKB;I0 C_Yhei[hl_Y[7_iWbbem[Zjeki[c_Yhei[hl_Y[8_\\0 Ɣ 7_i[ii[dj_Wbboi_cfb[hX[YWki[_jki[i8 Ɣ 8_idejikXijWdj_Wbboceh[Yecfb[nX[YWki[_j_idejWbbem[Zjeki[7 Ɣ J^[h[_iWki[\kbikXi[jYedjW_d_d]8WdZdej7 Ɣ J^[h[_ideYedY[_lWXb[ki[\kbikXi[jYedjW_d_d]7Xkjdej8

    7dZe\Yekhi["_jZe[idej_djheZkY[WdoYoYb[i_djej^[Z[f[dZ[dYo]hWf^
  12. ,&6$ ,&6(

  13. Ÿ2WeVcViR^Z_Z_XYf_UcVUd`WVcc`cac`_V 5CDaRTVd`gVcU`kV_d`W`aV_d`fcTVR_U T`^^VcTZR]ac`[VTedhVYRgV`SdVcgVUeYRe eYVcVRcV[fdeRWVhUZdeZ_TeejaVd`W RcTYZeVTefcVZddfVdR_UeYVdV`TTfc`gVcR_U `gVcRXRZ_žŹ

  14. %) %XJ)UHTXHQF\%& %XJFKXUQ&) &KDQJH)UHTXHQF\&& &KDQJH&KXUQ 9`h^fTY h`cdVW`c RcTYZeVTefcV Y`eda`ed0

  15. C7?DIEKH9;IE<C7?DJ;D7D9; 9EIJI0 '$ KdijWXb[_dj[h\WY[ ($ ?cfb_Y_jYheii#ceZkb[Z[f[dZ[dYo )$ Kd^[Wbj^o_dj[h\WY[_d^[h_jWdY[ ^_[hWhY^o *$

    9heii#ceZkb[YoYb[ +$ 9heii#fWYaW][YoYb[
  16. 7KHGDWDVD\V 7KHWZRPRVWLPSRUWDQWDUHDVWRSD\DWWHQWLRQWRDUH Ɣ WKHLQWHUIDFHVRIWKHPRGXOHVDQGKRZZHOOWKH\KLGH LQIRUPDWLRQVRWKDWFKDQJHVFDQEHPDGHZLWKRXW FDVFDGHVDQG Ɣ WKHXVHVVWUXFWXUHRIWKHV\VWHP

  17. ,GHQWLI\LQJDQGTXDQWLI\LQJDUFKLWHFWXUDOGHEW Ɣ $UFKLWHFWXUDOGHEWVFRQVXPHRIWKHWRWDOSURMHFWPDLQWHQDQFHHIIRUWLQ SURMHFWVVWXGLHG Ɣ 7KHWRSILYHPRGXODULW\GHEWVDORQHFRQVXPHRIWKHWRWDOHIIRUW Ɣ 0RGXODULW\YLRODWLRQLVWKHPRVWFRPPRQDQGH[SHQVLYHGHEWRYHUDOOLW DFFRXQWVIRURIWKHWRWDOHIIRUWLQ+%DVH Ɣ

    7RSGHEWVRQO\LQYROYHDVPDOOQXPEHURIILOHVPRGXOHVEXWFRQVXPHDODUJH DPRXQWRIWKHWRWDOSURMHFWHIIRUW Ɣ $ERXWKDOIRIDOODUFKLWHFWXUDOGHEWVDFFXPXODWHLQWHUHVWDWDFRQVWDQWUDWH
  18. Ÿ2]^`deR]]TReRdec`aYZTWRZ]fcVd%)Z_ e`eR]ų*#RcVeYVcVdf]e`W Z_T`ccVTeYR_U]Z_X`W_`_WReR]Vcc`cd Via]ZTZe]jdZX_R]]VUZ_d`WehRcVŹ

  19. Ÿ5VdaZeVR]]eYVVWW`ced`WgR]ZUReZ`_ cVgZVhR_UeVdeZ_XT`_WZXfcReZ`_ Vcc`cddeZ]]TRfdV^R_jYZXYZ^aRTe Z_TZUV_ed`We`URjŶd:_eVc_VeR_UT]`fU djdeV^dŹ

  20. Distributed Systems and Big Data 20

  21. Frank McSherry Scalability - but at what COST? 21

  22. 22

  23. But you have BIG Data! 23 Zipf Distribution ³:RUNLQJVHWVDUH =LSIGLVWULEXWHG:HFDQ

    WKHUHIRUHVWRUHLQPHPRU\DOO EXWWKHYHU\ODUJHVW GDWDVHWV´
  24. Musketeer 24 One for all?

  25. Approx Hadoop 25 32x!

  26. HopFS - FAST’17 26

  27. Redundancy does not imply fault tolerance - FAST’17 27 ŸRdZ_X]VWZ]VdjdeV^WRf]eTR_

    Z_UfTVTReRdec`aYZT`feT`^VdZ_ ^`de^`UVc_UZdecZSfeVUde`cRXV djdeV^dUReR]`ddT`ccfaeZ`_ f_RgRZ]RSZ]ZejR_UZ_d`^V TRdVdeYVdacVRU`WT`ccfaeZ`_ e``eYVcZ_eRTecVa]ZTRdŹ
  28. Infrastructure implications 28

  29. +XPDQ FRPSXWHUV DW'U\GHQE\1$&$ 1$6$  'U\GHQ)OLJKW5HVHDUFK&HQWHU 3KRWR&ROOHFWLRQ KWWSZZZGIUFQDVDJRY*DOOHU\3KRWR3ODFHV+7 0/(KWPO/LFHQVHGXQGHU3XEOLF'RPDLQYLD &RPPRQV

    KWWSVFRPPRQVZLNLPHGLDRUJZLNL)LOH+XPDQBFR PSXWHUVBB'U\GHQMSJPHGLD)LOH+XPDQBFRPSXW HUVBB'U\GHQMSJ
  30. Computing on a Human Scale 30 10ns 70ns 10ms 10s

    1:10s 116d 5HJLVWHUV // )LOHRQ GHVN 0DLQ PHPRU\ 2IILFHILOLQJ FDELQHW +'' 7ULSWRWKH ZDUHKRXVH
  31. Compute HTM Persistent Memory NI FPGA GPUs Memory NVDIMMs Persistent

    Memory Networking 100GbE RDMA Storage NVMe Next-gen NVM Next Generation Hardware All Change Please 31
  32. 2-10m Computing on a Human Scale 32 10s 1:10s 116d

    File on desk Office filing cabinet Trip to the warehouse 4x capacity fireproof local filing cabinets 23-40m Phone another office (RDMA) 3h20m Next-gen warehouse
  33. The New ~Numbers Everyone Should Know 33 Latency Bandwidth Capacity/IOPS

    Register 0.25ns L1 cache 1ns L2 cache 3ns 8MB L3 cache 11ns 45MB DRAM 62ns 120GBs 6TB - 4 socket NVRAM’ DIMM 620ns 60GBs 24TB - 4 socket 1-sided RDMA in Data Center 1.4us 100GbE ~700K IOPS RPC in Data Center 2.4us 100GbE ~400K IOPS NVRAM’ NVMe 12us 6GBs 16TB/disk,~2M/600K NVRAM’ NVMf 90us 5GBs 16TB/disk, ~700/600K
  34. No Compromises - FaRM 34 TPC-C (90 nodes) 4.5M tps

    99%ile 1.9ms KV (per node) 6.3M qps at peak throughput 41μs
  35. No Compromises 35 ³7KLVSDSHUGHPRQVWUDWHVWKDWQHZVRIWZDUHLQPRGHUQ GDWDFHQWHUVFDQHOLPLQDWHWKHQHHGWRFRPSURPLVH,W GHVFULEHVWKHWUDQVDFWLRQUHSOLFDWLRQDQGUHFRYHU\ SURWRFROVLQ)D50DPDLQPHPRU\GLVWULEXWHGFRPSXWLQJ SODWIRUP)D50SURYLGHVGLVWULEXWHG$&,'WUDQVDFWLRQV ZLWKVWULFWVHULDOL]DELOLW\KLJKDYDLODELOLW\KLJK WKURXJKSXWDQGORZODWHQF\7KHVHSURWRFROVZHUH

    GHVLJQHGIURPILUVWSULQFLSOHVWROHYHUDJHWZRKDUGZDUH WUHQGVDSSHDULQJLQGDWDFHQWHUVIDVWFRPPRGLW\ QHWZRUNVZLWK5'0$DQGDQLQH[SHQVLYHDSSURDFKWR SURYLGLQJQRQYRODWLOH'5$0´
  36. DrTM The Doctor will see you now 36 5.5M tps

    on TPC-C 6-node cluster.
  37. Security 37

  38. Making smart contracts smarter CCS ‘16 38 19,366 contracts $30M

    USD 8,833 vulnerable 27.9% 15.7% 340 83  (UURU  H[FHSWLRQ KDQGOLQJ  7UDQVDFWLRQ RUGHULQJ 5HHQWUDQF\ KDQGOLQJ 7LPHVWDPS RUGHULQJ
  39. OSDI ‘16 Scone: Secure Linux containers with Intel SGX 39

  40. NDSS ‘17 Thou shalt not depend on me 40 37%

    vulnerable jQuery -> 36.7%, Angular -> 40.1%
  41. ML & DL 41

  42. lessons from Google Machine Learning Systems 42 Feature Management Visualisation

    Relative Metrics Systematic Bias Correction Alerts on action Thresholds 01 02 03 04 05
  43. ICLR 2015 Explaining and harnessing adversarial examples 43

  44. CVPR ‘15 Deep neural networks are easily fooled 44

  45. Wrapping Up 45

  46. Brain storm 01 02 05 04 rainstorm 03 5 Reasons

    to <3 Papers Thinking tools Raise Expectations Applied Lessons Order of magnitude breakthroughs Heads-up 46
  47. Don’t just take my word for it... 47 HYV_:eR]\e`cVdVRcTYVcdhYV_:eR]\ e`aV`a]VhR_eZ_Xe`V_XRXVZ_

    V_ecVacV_VfcdYZa:eV]]eYV^eYReZWj`f cVRUcVdVRcTYaRaVcdT`_dZdeV_e]jZW j`fdVcZ`fd]jdefUjYR]WRU`kV_aRaVcd RhVV\R_Uj`fU`eYReW`ceh`jVRcd RWeVceY`dVeh`jVRcdj`fhZ]]YRgV ]VRc_VUR]`eEYZdZdRWR_eRdeZT Z_gVde^V_eZ_j`fc`h_]`_XeVc^ UVgV]`a^V_e $QGUHZ1J ³,QVLGHWKHPLQGWKDWEXLOW*RRJOH%UDLQ´  KWWSZZZKXIILQJWRQSRVWFRPDX DQGUHZQJBQBKWPO
  48. Don’t just take my word for it... 48 :U`_Ŷe\_`hY`heYVYf^R_ScRZ_ h`c\dSfeZeŶdR]^`de^RXZTR]hYV_

    j`fcVRUV_`fXY`ceR]\e`V_`fXY ViaVcedhYV_j`fYRgVV_`fXYZ_afed _VhZUVRddeRceRaaVRcZ_X $QGUHZ1J ³,QVLGHWKHPLQGWKDWEXLOW*RRJOH%UDLQ´ KWWSZZZKXIILQJWRQSRVWFRPDXDQGUHZQJBQBKWPO
  49. A new paper every weekday Published at http://blog.acolyer.org. 01 Delivered

    Straight to your inbox If you prefer email-based subscription to read at your leisure. 02 Announced on Twitter I’m @adriancolyer. 03 Go to a Papers We Love Meetup A repository of academic computer science papers and a community who loves reading them. 04 Share what you learn Anyone can take part in the great conversation. 05
  50. THANK YOU ! @adriancolyer