RETHINK big: European roadmap for hardware anc networking optimizations for big data

RETHINK big: Eu opean Roadmap o Ha dwa e and Ne wo king Op imiza ions o Big Da a Gina Alio o, Paul Ca pen e , Ad ián C is al, Osman Unsal Ba celona Supe compu ing Cen e Ba celona, Spain Ma cus Leich Technische Uni e si ä Be lin Be lin, Ge many Ch is ophe A a e Thales Resea ch & Technology Palaiseau cedex F ance Abs ac —This pape discusses he esul s o he RETHINK big P ojec , a 2-yea Collabo a i e Suppo Ac ion unded by he Eu opean Commission in o de o w i e he Eu opean Roadmap o Ha dwa e and Ne wo king op imiza ions o Big Da a. This indus y-d i en p ojec was led by he Ba celona Supe compu ing Cen e (BSC), and i included la ge indus y pa ne s, SMEs and academia. The oadmap iden i ies business oppo uni ies om 89 in-dep h in e iews wi h 70 Eu opean indus y s akeholde s in he a ea o Big Da a and p edic s he u u e echnologies ha will dis up he s a e o he a in Big Da a p ocessing in e ms o ha dwa e and ne wo king op imiza ions. Mo eo e , i p esen s coo dina ed echnology de elopmen ecommenda ions ( ocused on op imiza ions in ne wo king and ha dwa e) ha would be in he bes in e es o Eu opean Big Da a companies o unde ake in conce as a ma e o compe i i e ad an age. Keywo ds— oadmap, Big Da a, ne wo k, ha dwa e I. I NTRODUCTION Big Da a is a la ge and di e se ield [1] [2] [3] which is cha ac e ized by la ge da a olumes, eloci ies and a ie ies, wi h s anda d and adi ional me hods and a chi ec u es unable o p ocess and s o e he da a wi hin a easonable ime ame. P ocessing and s o age bo lenecks a e leading o he adop ion o specialized Big Da a-op imized ha dwa e and ne wo king echnologies, especially by he majo Big Da a playe s. Fo ins ance, Mic oso has employed FPGA accele a ion o “mee he needs o Da acen e wo kloads [ ha ] demand high compu a ional capabili ies, lexibili y, powe e iciency, and low cos ” [4], esul ing in a 29% educ ion in ail la ency o i s p op ie a y Bing sea ch engine. In 2015, Mic oso ook Big Da a ha dwa e op imiza ion a s ep u he by employing FPGAs o “ a ious kinds o deep lea ning app oaches” [5] and i has ins alled FPGAs in e e y Azu e cloud se e wo ldwide, in o de o c ea e a cloud o a i icial in elligence [6]. In June 2016, N idia announced plans o “ ap in o he big da a business [which] will be a illion-dolla business o e he nex ew yea s” [7]. The deep lea ning ha dwa e pla o m ea u ing GPU-accele a ed aining combined wi h ASIC- accele a ed gameplay was c i ical o Google DeepMind’s AlphaGo p og am, which bea he Eu opean Go champion in ou namen condi ions [8]. N idia is also pushing GPUs in he DRIVE PX on-boa d compu e pla o m o compu e ision and deep lea ning o Ad anced D i e Assis ance Sys ems (ADAS) [9]. Facebook is ocused on he OpenCompu e open ha dwa e ini ia i e [10] [11], and i is decoupling he so wa e om he ha dwa e in he ne wo k swi ch business, while a he same ime mo ing owa d disagg ega ed ne wo k a chi ec u e. O e all, he e is a end owa ds no el ha dwa e o Big Da a- ela ed op imiza ion a he la ge hype scale s, as hey a e o en he i s o see he p oblem as well as he i s o sol e i . This pape discusses he esul s o he RETHINK big P ojec , a 2-yea Collabo a i e Suppo Ac ion unded by he Eu opean Commission in o de o w i e he Eu opean Roadmap o Ha dwa e and Ne wo king op imiza ions o Big Da a. Sec ion II desc ibes he RETHINK big P ojec and me hodology and Sec ion III desc ibes how he RETHINK big oadmap i s in o o he ela ed ini ia i es in he Eu opean con ex . Sec ion IV discusses he oadmap con ibu ions ela ing o ne wo k a chi ec u e, node a chi ec u e and low- le el so wa e suppo , and Sec ion V summa izes he oadmap’s key indings and ecommenda ions. Mo e de ail is ound in he comple e RETHINK big oadmap [12]. Finally, Sec ion VI concludes he pape . II. RETHINK BIG P ROJECT The Eu opean Union, ecognizing he ends o in e na ional Big Da a playe s and in an e o o ge he bes e u n on hei in es men in companies in he a ea o Big Da a analy ics, called o a Roadmap o p o ide a coo dina ed se o echnology de elopmen ecommenda ions ( ocused on op imiza ions in ne wo king and ha dwa e) ha would be in he bes in e es o Eu opean Big Da a companies o unde ake in conce as a ma e o compe i i e ad an age. The p oduc ion o his oadmap has been unded as a p ojec , RETHINK big EC-GA No. 619788, which was led by he Ba celona Supe compu ing Cen e and included pa ne s om la ge indus y o SME o academia, as shown in Table 1. In his oadmap, we iden i ied business oppo uni ies om Eu opean indus y s akeholde s in he a ea o Big Da a and p edic ed u u e echnologies ha will dis up he s a e o he a in Big Da a p ocessing in e ms o ha dwa e and ne wo king op imiza ions. We hen iden i ied a c i ical mass o hese s akeholde s ha see a clea compe i i e ad an age enabled by emb acing speci ic u u e echnologies. Finally, we de eloped ecommenda ions o he Eu opean Commission ha will ul ima ely acili a e imely Eu opean indus y access o hese u u e echnologies. © 20xx IEEE. Pe sonal use o his ma e ial is pe mi ed. Pe mission om IEEE mus be ob ained o all o he uses, in any cu en o u u e media, including ep in ing/ epublishing his ma e ial o ad e ising o p omo ional pu poses, c ea ing new collec i e wo ks, o esale o edis ibu ion o se e s o lis s, o euse o any copy igh ed componen o his wo k in o he wo ks. III. A E UROPEAN R OADMAP The RETHINK big oadmap is one piece o he amewo k o oadmaps (Figu e 1) being pu oge he o he Eu opean Commission. While compiling he oadmap, i was de e mined ha many Big Da a compu e p oblems we e ins ances o compu e p oblems linked o he ending o Moo e’s law and Denna d scaling, and beyond. As such, he oadmap scope is limi ed o p oblems wi h a di ec impac on Big Da a Eu opean indus y, and gene al compu e p oblems a e handled by he Eu opean Technology Pla o m (ETP) Roadmaps (NEM, NESSI, EPoSS and Pho onics21). The oadmap was de eloped wi h conside a ion o High Pe o mance Compu ing (HPC), since HPC also ou inely uses ex emely la ge da ase s [13]. Ou oadmap, howe e , is limi ed o ac i i ies wi h a clea bene i o EU Big Da a Indus y, as HPC- ela ed aspec s a e co e ed by ETP4HPC. The same is ue o so-called In e ne o Things on i s way o becoming he In e ne o E e y hing. The key o his nascen compu e a ea seems o be he da a i sel , and we belie e ha he oppo uni ies p o ided by IoT will be “enabled by and dependen on he emendous da a collec ions and compu e capaci ies in he back-end machines and da acen e s ha use such da a” [14]. As such, we main ain ou ocus on hese back-end machines and da a cen e s, lea ing all o he aspec s o be co e ed unde he Alliance o In e ne o Things Ini ia i e and he egula ion and s anda ds o communica ion a he ne wo k le el unde he wo k o he 5G-PPP (Public P i a e Pa ne ship). Finally, while no discussion ega ding Big Da a is comple e wi hou discussion o so wa e, he ea men o so wa e in he oadmap was limi ed o suppo o ha dwa e and ne wo king op imiza ions o Big Da a. We lea e he mo e de ailed discussion o he Big Da a analy ics applica ions and he da a i sel o he oadmap o he Big Da a Value Associa ion (BDVA). IV. T ECHNICAL D ISCUSSION A. Ne wo k The ne wo k is he mos pe asi e elemen o any mode n echnology-based business. As such, he oadmap explains he po en ial op imiza ions o Big Da a wi h inno a i e echnologies applied o hese appliances - speci ically ou e s and swi ches - as ela ed o i ualiza ion. The analysis conside s ne wo k equi emen s o Big Da a wo kloads, whe he inside a public cloud, a p i a e da a cen e o e en in a u u e High Pe o mance / Big Da a embedded sys em. We examine hese equi emen s om he pe spec i e o he “da a ecei ing end”, meaning he ne wo k communica ion inside o he Da a Cen e . As a esul , we conside he nascen IoT senso s ma ke , he In e ne o mobile in as uc u e challenges aced by he global elecom ne wo ks, and he ac ual access o he da a by businesses (including egula o y and p i acy conce ns) om his pe spec i e. The “ne wo k” consis s o mul iple unc ions embedded a di e en laye s ac oss many physical de ices anging om he se e mo he boa d and in e aces o he op o ack swi ches, ou e s and ope a o in as uc u e. Un il now, he ne wo king ha dwa e li ecycle has been d i en by he ques o inc easing bandwid h. Bu oday’s ma ke landscape is apidly changing unde he p essu e o demand coming om Big Da a, mobile phones and IoT combined equi emen s. 1) Ne wo k appliance ha dwa e: specialized o ba e me al In eac ion o a compe i i e new landscape, hype scale s like Google and Facebook a e acing o be he i s o achie e s a e-o - he-a bandwid h (100GE). They a e also conside ing mo ing o a new a chi ec u e based on ei he ba e me al swi ches o specialized “pu pose-buil ” swi ches ha a e able o be e cope wi h hei speci ic Big Da a wo kloads. Ba e me al [16] e e s o commodi y (low-cos ) swi ches o which cus ome s mus p ocu e, sepa a ely, a hi d-pa y ne wo k ope a ing sys em (NOS) — like Big Swi ch Ligh OS, Cumulus Linux OS, Pica8 PicOS — o build hei own like Facebook did. Ne wo k ope a ing sys em suppo and se ices mus be ob ained om he hi d-pa y NOS. Addi ionally, he e a e Whi e Box swi ches ha a e commodi y-based ba e- me al swi ches wi h a p eloaded ne wo k ope a ing sys em om a hi d-pa y o adi ional ne wo king endo . 2) Ha dwa e o “so wa iza ion” o i ualiza ion This end in ne wo k a chi ec u e, howe e , goes well beyond his ba e me al ha dwa e. The p e iously men ioned “so wa iza ion” begins wi h So wa e De ined Ne wo king (SDN) which allows o he sepa a ion o con ol and da a planes, espec i ely, ia so wa e ha can un on ba e me al swi ches and o se e s wi h he addi ion o ne wo k Figu e 1: ETP/PPP Collabo a ion ( om [15]) Pa ne Name Expe ise Ba celona Supe compu ing Cen e (BSC) Compu e a chi ec u e and sys em a chi ec u e Technische Uni e si a Be lin (TUB) Da abase sys ems and in o ma ion managemen École Poly echnique Fédé ale de Lausanne (EPFL) Da abase sys ems and applica ions Cen um Voo Wiskunde en In o ma ica (CWI) Ha dwa e-conscious da abase echnologies Uni e si y o Manches e (UoM) Compu e a chi ec u e Uni e sidad Poli écnica de Mad id (UPM) Da a mining and wa ehousing ARM L d. (ARM) Silicon IP p o ide In e ne Memo y Resea ch (IMR) Web-scale sou cing pla o m o business in elligence Thales SA (THALES) Si ua ion and decision analysis, planning and op imiza ion Table 1: RETHINK big P ojec Conso ium ca ds [16]. This has he po en ial o b ing down he cos signi ican ly and can g ea ly inc ease lexibili y. As explained by Google [17], SDN is abou “a so wa e con ol plane ha abs ac s and manages complexi y…and can make 10,000 swi ches look like one.” This a chi ec u e con inues wi h Ne wo k Func ion Vi ualiza ion (NFV), which allows o he implemen a ion o secu i y, i ewalls, ou ing schemes and o he unc ions sepa a ely, again ia so wa e allowing o inc eased con ol, lexibili y and scalabili y. 3) Decons uc ing he da a cen e (beyond 400 GbE) High-end (beyond 400 Gigabi E he ne o GbE) ne wo k appliances should be a ailable a e 2020 [18], bu by hen, he en i e o ganiza ion inside he da a cen e may ha e changed. The con inuous demand o lexibili y and lowe ope a ing cos s may equi e adical ans o ma ions [19], wi h high bandwid h a ailable a all key in e connec nodes leading o composable ha dwa e – CPU, memo y, I/O and s o age ha is pu chased à la ca e and suppo ed by so wa e ha can econ igu e he ne wo k o speci ic wo kloads. The bene i s a e clea ; by disagg ega ing he da a cen e , we acili a e egula upg ades and po en ially elimina e he need and cos o eplacing en i e se e s, cabling and econ igu ing e e y hing. This ision will no be ealis ic wi hou new so wa e capable o e icien ly managing he complexi y o such a he e ogeneous pool o esou ces – each esou ce po en ially loca ed anywhe e in a da a cen e . This could lead o in e es ing oppo uni ies o SMEs, due o he end owa d open ha dwa e and ne wo king and po en ially mo e he ecosys em ou he hands o he big e ical chip make s. B. A chi ec u e Rega ding Big Da a compu e node ha dwa e, he e a e ou impo an ends o conside , which a e b ie ly discussed below and ou lined in de ail in he ull oadmap [12]. 1) He e ogeneous compu ing The e is a no iceable end away om gene al-pu pose a chi ec u es owa ds he e ogeneous sys ems and accele a o s. This change is mainly d i en by a slowdown in Moo e’s Law [20][21], which leads o combina ions o mul iple kinds o p ocesso s and accele a o s, GPUs, many-co es, FPGAs, and applica ion- speci ic accele a o s in o he same de ice. Despi e he po en ial bene i s o mo ing owa d he e ogeneous sys ems, he ba ie s o en y a e subs an ial, in pa icula he cos o pu chasing accele a o ha dwa e and so wa e complexi y. The e o o un a Big Da a applica ion on he e ogeneous sys ems equi es specialized skills and knowledge o ha dwa e due o he complex ools and p og amming models. E en a e in es ing in he app op ia e human capi al, a sui able Re u n on In es men (ROI) is no gua an eed, since such sys ems o en equi e hand op imiza ion. On op o his, so wa e o he e ogeneous sys ems is no po able and subjec o endo lock-in. In addi ion, many open-sou ce communi ies a e philosophically opposed o accep ing ha dwa e-speci ic so wa e pa ches [22], so only open languages and APIs a e likely o be suppo ed beyond speci ic d i e modules connec ed o using gene al- pu pose and o en es ic i e in e aces. Finally, many new echnologies ha e ye o be p o en in e ms o pe o mance due o he lack o s anda d eal-wo ld benchma ks. As a esul , o Eu opean so wa e endo s o adop he e ogeneous sys ems, hey mus keep pace wi h successi e candida e echnologies, which is no economically iable. This is e iden in ou p ojec su eys, in which he majo i y o Eu opean so wa e endo s epo ed ha hey had no ha dwa e oadmap and p e e ed o wai un il new echnologies became widely adop ed inexpensi e commodi ies. 2) Specializa ion and endo lock-in Gene al-pu pose GPU (GPGPU) is a ma u ing echnology wi h a g owing a e o adop ion, especially in he a ea o High Pe o mance Compu ing (HPC). The GPGPU ma ke is cu en ly domina ed by N idia (>95% o GPU-accele a ed sys ems in he TOP500 use N idia). GPGPUs ha e no ye achie ed wide-scale pene a ion in o da a cen e s due he unce ain ROI. Small o medium-sized da a cen e ope a o s a e unwilling o deploy GPGPUs a la ge scale, as he powe consump ion is oo high and u iliza ion oo low o jus i y he in es men . As is he case o mo ing om a GPGPU-based he e ogeneous a chi ec u e o an FPGA-based one, he e is conside able Non- ecu ing Enginee ing (NRE) cos equi ed o a change in GPU endo . 3) In eg a ion wi hin he compu e node The e is a end owa ds g ea e in eg a ion, o imp o e pe o mance and educe ene gy consump ion. In es ing in a ma ke -speci ic se e SoC is likely o be cos -p ohibi i e, howe e , unless he design can be suppo ed by a e ical business o i can add ess a la ge- olume ma ke (such as mobile). SoCs p o ide no lexibili y: adding a new in e ace (e.g. 40 GbE) equi es a cos ly edesign. In addi ion, an SoC mus be implemen ed using a single silicon p ocess. Since he SoC includes he pe o mance- and ene gy-c i ical p ocesso co es, he die mus be ab ica ed using an expensi e leading edge silicon echnology. An al e na i e is Sys em-in-Package (SiP), as pionee ed by he EC EUROSERVER p ojec [23]. Ha ing mul iple dies in he same package p o ides lexibili y in ha as e e ol ing echnologies may be sepa a ed om mo e slowly e ol ing ones and hus eplaced wi hou a ec ing he es o he design. In addi ion, ma ke -speci ic p oduc s can be buil om commodi y compu e chiple (s) wi h specialized chiple (s) o accele a o s and I/O in e aces wi hou designing an en i e SoC. This lexibili y may gi e smalle companies a be e oppo uni y o compe e due o igh e sys em in eg a ion. 4) Ve icaliza ion and hype scale s The inal majo end is he inc easing dominance o a small numbe o e ically- in eg a ed companies ha co-design all o pa s o he se e s ack, o a ying deg ees, anging om he use - isible so wa e, h ough (Big Da a) amewo ks, down o sys em in eg a ion and po en ially e en chip design. These companies ha e eno mous ma ke sha e and economies o scale, made mo e so h ough e iciencies om hei e ically-in eg a ed pe spec i e. In Eu ope, howe e , he indus y is agmen ed, wi h a la ge disconnec be ween echnology p o ide s and analy ics companies. Almos all analy ics companies exp essed ha hey ha e no ha dwa e oadmap, ake li le no ice o new ha dwa e ends and a e only looking a exis ing commodi y ha dwa e. Since Eu ope cu en ly has no ma ke sha e in se e compu e CPUs, he e is limi ed oppo uni y o hese companies o engage wi h he incumben supplie (s). This la ge disconnec be ween echnology p o ide s and analy ics companies ca ies a signi ican isk o being le behind by he la ge U.S. companies. C. So wa e Suppo 1) Big Da a p ocessing: que y languages o amewo ks In he ea ly yea s o da a p ocessing, da a analys s knew which answe s hey we e looking o and hei da ase s we e clean, so que y languages we e he ool-o -choice. Que y languages we e bound o a speci ic pu pose, o ins ance SQL o ela ional que ying, SPARQL o que ying Resou ce Desc ip ion F amewo k da a, and XPa h/XQue y o XML. Mo eo e , he highe -le el so wa e could be easily w i en in a way ha was independen o he ha dwa e. O e he yea s, wi h he ad en o Big Da a, se e al changes in he da a p ocessing landscape ha e ende ed que y languages di icul o use. Fi s ly, he e has been a nea ly exponen ial inc ease in he olume and a ie y o da a – da a ha is inc easingly he e ogeneous, uns uc u ed, “di y” and unp ocessed, and he e o e unsui able o he SQL abs ac ion. In addi ion, he e has been a b oad shi om local o dis ibu ed compu ing, which has equi ed he use o dis ibu ed amewo ks, such as MapReduce, Spa k and Flink, ha hide he complexi y o dis ibu ed ha dwa e. These lowe - le el amewo ks ha e no been di ec ly compa ible wi h exis ing que y languages. The consequence has been a shi away om que y languages owa ds da a analysis lib a ies and APIs a ge ing Machine Lea ning (ML) and Na u al Language P ocessing (NLP). 2) Towa ds ha dwa e dependence The push om he business in elligence communi y owa d Big Da a analy ics has esul ed in widesp ead adop ion o dis ibu ed amewo ks. A he same ime, open sou ce communi ies a e ying o p o ide sui able ML code highe -le el lib a ies (MLlib) o hese amewo ks. Addi ionally, and simila ly o he widesp ead adop ion o SQL, la ge companies and hype scale s ha e al eady s a ed o de elop hei own solu ions o NLP and ML, as specialized highe -le el lib a ies such as IBM’s Sys emT and Sys emML and Google’s Tenso - Flow. The ad an age o hese companies is ha hese highe - le el lib a ies can be un on nea ly any dis ibu ed amewo k. They allow use s o speci y compu a ions in a way ha ensu es ha he p og am can be execu ed in pa allel and he amewo ks can hen un his code on a suppo ed se o ha dwa e. E e y ime no el ha dwa e becomes a ailable, hese amewo ks need o be adap ed o suppo he ha dwa e. 3) Too many abs ac ions A he lowe le els o he so wa e s ack, he e a e a la ge numbe o p og amming abs ac ions. He e ogeneous a chi ec u e app oaches come wi h di e se p og amming in e aces ha usually need o be add essed explici ly by Big Da a applica ion de elope s and canno be easily in eg a ed in o exis ing wo k lows au oma ically. E en hough e e y p og amming concep ele an o Big Da a equi es pa allelizing wo k ac oss a ailable ha dwa e, and e en hough all ele an ha dwa e s uc u es suppo pa allel p ocessing in some way, he speci ics a e incompa ible, in ha he e a e no common abs ac ions ha wo k o e e y hing. Abs ac ion o pa allelism a he da a cen e le el means using MapReduce o ano he dis ibu ed amewo k while abs ac ion o pa allelism (mul ico e) a node le el equi es ye ano he laye such as OpenMP (mul i- co e), he e ogeneous a chi ec u es (CPU, GPU, FPGA) equi e an abs ac ion such as OpenCL, and so on. On a la ge scale, MapReduce and i s successo s o ba ch and s eam p ocessing implemen ed by he Apache Spa k and Apache Flink p ojec s allow he pa allel execu ion o code on sha ed-no hing clus e s. All o hese amewo ks speci y in a decla a i e way he da a placemen and uni o pa alleliza ion, while lea ing he ac ual p ocessing in each pa allel ins ance o con en ional unc ional o p ocedu al code. The uni o pa alleliza ion suppo ed he e is an ope a ing sys em h ead. Any ha dwa e ha can execu e such a h ead, such as a CPU co e, is po en ially a ailable o MapReduce, while e e y hing else needs o be add essed explici ly by he p og amme . A he ha dwa e le el on a single node, he e a e simila misma ches: GPUs and CPUs a e ailo ed o p ocessing mul iple da a i ems a once. Fo each piece o ha dwa e, howe e di e en p og amming app oaches a e necessa y. GPUs ely on SIMT, and CPUs p o ide SIMD unc ionali y o achie e a simila goal a leas on a single CPU co e. Mul i- co e ope a ions need o be implemen ed explici ly. Mode n compile s and amewo ks like OpenMP a e capable o abs ac ing away some o hese di e ences. The ke nel-based p og amming abs ac ions o OpenCL ansla e e y well o all o he p e iously men ioned pa alleliza ion concep s, howe e e en OpenCL only ensu e co ec ness o he compu a ion on each pla o m. I does no ensu e ha he compu a ion has been op imized o execu ion on hese same pla o ms. The si ua ion wi h mo e ad anced accele a o ha dwa e in he e ogeneous a chi ec u es is simila . While FPGAs usually suppo s anda d Ha dwa e Desc ip ion Languages (HDLs) such as VHDL and Ve ilog, hese languages desc ibe he unc ionali y equi ed a a e y low le el, in a way ha is di icul o so wa e enginee s o unde s and. Finally, specialized solu ions such as ASICs, DSPs and neu omo phic ha dwa e equi e p og amming in e aces ha a e likewise specialized and o he ime being canno be accessed au oma ically om con en ional Big Da a amewo k code. V. K EY F INDINGS AND R ECOMMENDATIONS A. Indus y Key Findings Ou indings a e he esul o 89 in-dep h in e iews wi h key s akeholde s om mo e han 70 dis inc Eu opean companies, in addi ion o se e al mee ings wi h a b oad spec um o indus y s akeholde s. These companies included majo and up-and-coming playe s om elecommunica ions, ha dwa e design and manu ac u e s as well as s ong ep esen a ion om heal h, au omo i e, inancial and analy ics sec o s. (1) Indus y is s ill ocused on how o ex ac alue om hei da a, and hey a e s ill looking o he business model o u n his alue in o p o i . Consequen ly, hey a e no ocused on p ocessing (and s o age) bo lenecks, le alone on he unde lying ha dwa e. The o e whelming esponse is ha indus y does no see Big Da a ha dwa e p ocessing p oblems, only Big Da a alue oppo uni ies. We belie e ha his is la gely because he indus y is no ye ma u e enough o mos companies o ully unde s and he kind o analy ics and Big Da a p ocessing ha leads o undesi able bo lenecks. (2) Eu opean companies a e no con inced o he Re u n on In es men o using no el ha dwa e. In gene al, Eu opean companies a e con en o use cu en ly a ailable ha dwa e as long as hey con inue o ecei e he mos compe i i e p icing. In some cases his was due o ex eme p ice-sensi i i y, bu o many i was simply due o isk, exace ba ed by he lack o a clean me ic o benchma k o side-by-side compa isons o no el ha dwa e. O e all, he majo i y o he companies we e no con inced ha he in es men in expensi e ha dwa e coupled wi h he pe son mon hs equi ed o make hei p oduc s wo k wi h new ha dwa e would be wo hwhile. (3) Eu ope has limi ed oppo uni ies o ha dwa e / so wa e a chi ec s o wo k oge he . The Eu opean ecosys em is highly agmen ed while media and in e ne gian s such as Google, Amazon, Facebook, Twi e and Apple and o he s (also known as hype scale s) a e pu suing e icaliza ion and designing hei own in as uc u es om he g ound up. Eu opean companies ha a e no closely conside ing ha dwa e and ne wo king echnologies as a means o cu ing cos and o e ing be e u u e se ices un he isk o alling u he behind. Hype scale s will con inue o ake isks and ans o m hemsel es because hey a e he “ecosys em”, mo ing e e ybody else in hei ail. (4) Dominance o non-Eu opean companies in he se e ma ke complica es he possibili y o new Eu opean en an s in he a ea o specialized a chi ec u es. The as majo i y o se e ha dwa e is based on In el p ocesso s. As a esul , In el has a huge in luence o e he di ec ion o he indus y, and hey a e wo king o u he expand his in luence, including ia hei ecen acquisi ion o Al e a, in hopes o de eloping powe ul new in eg a ed ha dwa e echnologies. B. High-le el Ac ions Summa y The RETHINK big oadmap [12] makes he ollowing wel e conc e e ecommenda ions. Mo e de ail is a ailable in he comple e oadmap documen . 1) P omo e adop ion o cu en and upcoming ne wo king s anda ds Eu ope should accele a e he adop ion o he cu en and upcoming s anda ds (10 and 40Gb E he ne ) based on low-powe consump ion componen s p oposed by Eu opean Companies and connec hese companies o end use s and da a-cen e ope a o s so ha hey can demons a e hei alue compa ed o he bigge playe s. 2) P epa e o he nex gene a ion o ha dwa e and ake ad an age o he con e gence o High Pe o mance Compu ing (HPC) and Big Da a in e es s In pa icula , Eu ope mus ake ad an age o i s s eng hs in HPC and embedded sys ems by encou aging dual-pu pose p oduc s ha b ing hese di e en communi ies oge he (e.g. HPC/Big Da a ha dwa e ha can be di e en ia ed in so wa e). This would allow new companies o sell o a bigge ma ke and dec ease he isk associa ed wi h de elopmen o new p oduc . The e is al eady a clea con e gence be ween High Pe o mance Compu ing and Big Da a. Mo eo e , la ge scien i ic expe imen s, including he La ge Had on Collide and Squa e Kilome e A ay in ol e p ocessing huge s eams o da a and a e inc easingly adop ing Big Da a echnologies. 3) An icipa e he changes in Da a Cen e design o 400Gb E he ne ne wo ks (and beyond) This includes paying special a en ion o ha dwa e de elopmen s such as pho onics- on-silicon in eg a ion and no el Da a Cen e in e connec designs equi ed a 400Gb ope a ion. 4) Reduce isk and cos o using accele a o s No el specialized ha dwa e has he po en ial o inc ease compu ing pe o mance and ene gy e iciency o app op ia e applica ions by a ac o o en o mo e. In o de o achie e hese gains, i is necessa y o e-enginee he so wa e, which is expensi e and ime consuming, and i is di icul o p edic he le el o gains ahead o ime. The use o FPGAs o compu ing is mos p ominen in inancial and oil indus ies, wi h only a small numbe o companies add essing hese ma ke s. Eu ope mus lowe he ba ie o en y o he e ogeneous sys ems and accele a o s; collabo a i e p ojec s should b ing oge he end use s, applica ion p o ide s and echnology p o ide s o demons a e signi ican (10x) inc ease in h oughpu pe node on eal analy ics applica ions. 5) Encou age sys em co-design o new echnologies Eu ope mus b ing oge he end use s, applica ion p o ide s, sys em in eg a o s and echnology p o ide s o build he mos e icien in eg a ed comple e ha dwa e—so wa e solu ions. This equi es ha dwa e o mee he e ol ing needs o Big Da a, in eg a ing mo e subsys ems in o he p ocesso de ice as well as new non- ola ile memo ies and I/O in e aces. 6) Imp o e p og ammabili y o FPGAs Eu ope should also und esea ch p ojec s in ol ing p o ide s o ools, abs ac ions and high-le el p og amming languages o FPGAs o o he accele a o s wi h he aim o demons a ing he e ec i eness o his app oach using eal applica ions. Eu ope should also encou age a new en an in o he FPGA indus y. 7) Pionee ma ke s o neu omo phic compu ing and inc ease collabo a ion Fo neu omo phic compu ing and o he dis up i e echnologies, he p incipal issue is he lack o a ma ke ecosys em, wi h insu icien appe i e o isk and ew Eu opean companies wi h he size and clou o in es in such a isky di ec ion. Eu ope should encou age collabo a i e esea ch p ojec s ha b ing oge he ac o s ac oss he whole chain: end use s, applica ion p o ide s and echnology p o ide s o demons a e eal alue om neu omo phic compu ing in eal applica ions. 8) C ea e a sus ainable business en i onmen including access o aining da a Eu ope should add ess access o aining da a by encou aging he collec ion o open anonymized aining da a and encou aging he sha ing o anonymized aining da a inside EC- unded p ojec s. To add ess he lack o in o ma ion sha ing, Eu ope should encou age in e ac ion be ween ha dwa e p o ide s and Big Da a companies using ne wo k-o -excellence o simila . 9) Es ablish s anda d benchma ks I is di icul o Indus y o assess he bene i s o using no el ha dwa e. We p opose es ablishing benchma ks o compa e cu en and no el a chi ec u es using Big Da a applica ions. 10) Iden i y and build accele a ed building blocks We p opose o iden i y o en- equi ed unc ional building blocks in exis ing p ocessing amewo ks and o eplace hese blocks wi h (pa ially) ha dwa e-accele a ed implemen a ions. 11) In es iga e use o he e ogeneous esou ces Wi h edge compu ing and cloud compu ing en i onmen s calling o he e ogeneous ha dwa e pla o ms, we p opose c ea ion o dynamic scheduling and esou ce alloca ion s a egies. 12) Con inue o ask he ques ion – Do companies hink ha ha dwa e and ne wo king op imiza ions o Big Da a can sol e he majo i y o hei p oblems? As mo e companies lea n how o ex ac alue om Big Da a and de e mine which business models lead o p o i s, he numbe o se ice o e ings and p oduc s based on Big Da a analy ics will g ow sha ply. This g ow h will likely lead o an inc ease in consume expec a ions wi h espec o hese Big Da a-d i en p oduc s and se ices, and we expec companies o un in o mo e and mo e undesi able pe o mance bo lenecks ha will equi e op imized ha dwa e. VI. CONCLUSIONS This pape desc ibes he RETHINK big P ojec and i s me hodology, and i gi es a b ie o e iew o he key indings and ecommenda ions in he RETHINK big oadmap [12]. In summa y, he RETHINK big P ojec has c ea ed an indus y- led s a egic oadmap ha will maximize Eu opean indus y compe i i eness o Big Da a ha dwa e o e he nex 10 yea s. A CKNOWLEDGMENT This p ojec has ecei ed unding om he Eu opean Union’s Se en h F amewo k P og amme o esea ch, echnological de elopmen and demons a ion unde g an ag eemen n° 619788. 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