Hardware Accelerators in Data Centers

280 Pages·2019·9.89 MB·English

by  Christoforos Kachris

Christoforos Kachris · Babak Falsafi 
Dimitrios Soudris Editors
Hardware
Accelerators
in Data
Centers
Hardware Accelerators in Data Centers
fi
Christoforos Kachris Babak Falsa
(cid:129)
Dimitrios Soudris
Editors
Hardware Accelerators
in Data Centers
123
Editors
Christoforos Kachris Dimitrios Soudris
Microprocessors andDigital National Technical
SystemsLab University of Athens
National Technical University Athens, Greece
ofAthens
Athens, Greece
Babak Falsafi
ICIINFCOM PARSA
ÉcolePolytechnique Fédérale
deLausanne
Lausanne,Switzerland
ISBN978-3-319-92791-6 ISBN978-3-319-92792-3 (eBook)
https://doi.org/10.1007/978-3-319-92792-3
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Preface
Emergingcloudapplicationslikemachinelearning,artificialintelligence(AI),deep
neural networks, and big data analytics have created the need for more powerful
data centers that can process huge amounts of data without consuming excessive
amounts of power. To face these challenges, data center operators have to adopt
novel architectures with specialized high-performance energy-efficient computing
systems, such as hardware accelerators, that can process the increasing amount of
datainamoreenergy-efficientway.Tothisend,anewerahasemerged;theeraof
heterogeneous distributed computing systems that consist of contemporary
general-purpose processors (CPUs), general-purpose graphic processing units
(GP-GPUs), and field-programmable gate arrays (FPGAs or ACAP Adaptive
Compute Acceleration Platform). The utilization of several heterogeneous archi-
tectures poses several challenges in the domain of efficient resource utilization,
efficient scheduling, and resource management.
Inthisbook,wehavecollectedthemostpromisingandthemostrecentresearch
activitiesinthisemergingdomainofheterogeneouscomputingthatisbasedonthe
efficient utilization of hardware accelerators (i.e., FPGAs). The book contains 13
system-levelarchitecturesthatshowhowtoefficientlyutilizehardwareaccelerators
inthedatacenterstofaceemergingcloudapplications.Theproposedarchitectures
tackle several challenges such as the energy efficiency of hardware accelerators in
data centers, the resource utilization and management, and the programmability of
heterogeneousinfrastructurebasedonthehardwareaccelerators.Wehopethisbook
servesasareferencebookforanyresearcher,engineer,andacademicworksonthe
exciting new area of heterogeneous computing with accelerators.
Athens, Greece Christoforos Kachris
April 2018
v
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Christoforos Kachris, Babak Falsafi and Dimitrios Soudris
2 Building the Infrastructure for Deploying FPGAs
in the Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Naif Tarafdar, Thomas Lin, Daniel Ly-Ma, Daniel Rozhko,
Alberto Leon-Garcia and Paul Chow
3 dReDBox: A Disaggregated Architectural Perspective
for Data Centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Nikolaos Alachiotis, Andreas Andronikakis, Orion Papadakis,
Dimitris Theodoropoulos, Dionisios Pnevmatikatos,
Dimitris Syrivelis, Andrea Reale, Kostas Katrinis, George Zervas,
Vaibhawa Mishra, Hui Yuan, Ilias Syrigos, Ioannis Igoumenos,
Thanasis Korakis, Marti Torrents and Ferad Zyulkyarov
4 The Green Computing Continuum: The OPERA
Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
A. Scionti, O. Terzo, P. Ruiu, G. Giordanengo, S. Ciccia, G. Urlini,
J. Nider, M. Rapoport, C. Petrie, R. Chamberlain, G. Renaud,
D. Tsafrir, I. Yaniv and D. Harryvan
5 Energy-Efficient Acceleration of Spark Machine Learning
Applications on FPGAs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Christoforos Kachris, Elias Koromilas, Ioannis Stamelos,
Georgios Zervakis, Sotirios Xydis and Dimitrios Soudris
vii
viii Contents
6 M2DC—A Novel Heterogeneous Hyperscale Microserver
Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Ariel Oleksiak, Michal Kierzynka, Wojciech Piatek,
Micha vor dem Berge, Wolfgang Christmann, Stefan Krupop,
Mario Porrmann, Jens Hagemeyer, René Griessl, Meysam Peykanu,
Lennart Tigges, Sven Rosinger, Daniel Schlitt, Christian Pieper,
Udo Janssen, Holm Rauchfuss, Giovanni Agosta,
Alessandro Barenghi, Carlo Brandolese, William Fornaciari,
Gerardo Pelosi, Joao Pita Costa, Mariano Cecowski,
Robert Plestenjak, Justin Cinkelj, Loïc Cudennec, Thierry Goubier,
Jean-Marc Philippe, Chris Adeniyi-Jones, Javier Setoain
and Luca Ceva
7 Towards an Energy-Aware Framework for Application
Development and Execution in Heterogeneous Parallel
Architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Karim Djemame, Richard Kavanagh, Vasilios Kelefouras,
Adrià Aguilà, Jorge Ejarque, Rosa M. Badia, David García Pérez,
Clara Pezuela, Jean-Christophe Deprez, Lotfi Guedria,
Renaud De Landtsheer and Yiannis Georgiou
8 Enabling Virtualized Programmable Logic Resources
at the Edge and the Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Kimon Karras, Orthodoxos Kipouridis, Nick Zotos,
Evangelos Markakis and George Bogdos
9 Energy-Efficient Servers and Cloud . . . . . . . . . . . . . . . . . . . . . . . . 163
Huanhuan Xiong, Christos Filelis-Papadopoulos, Dapeng Dong,
Gabriel G. Castañé, Stefan Meyer and John P. Morrison
10 Developing Low-Power Image Processing Applications
with the TULIPP Reference Platform Instance. . . . . . . . . . . . . . . . 181
Tobias Kalb, Lester Kalms, Diana Göhringer, Carlota Pons,
Ananya Muddukrishna, Magnus Jahre, Boitumelo Ruf,
Tobias Schuchert, Igor Tchouchenkov, Carl Ehrenstråhle,
Magnus Peterson, Flemming Christensen, Antonio Paolillo,
Ben Rodriguez and Philippe Millet
11 Energy-Efficient Heterogeneous Computing at
exaSCALE—ECOSCALE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Konstantinos Georgopoulos, Iakovos Mavroidis, Luciano Lavagno,
Ioannis Papaefstathiou and Konstantin Bakanov
Contents ix
12 On Optimizing the Energy Consumption of Urban
Data Centers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Artemis C. Voulkidis, Terpsichori Helen Velivassaki
and Theodore Zahariadis
13 Improving the Energy Efficiency by Exceeding the Conservative
Operating Limits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Lev Mukhanov, Konstantinos Tovletoglou, Georgios Karakonstantis,
George Papadimitriou, Athanasios Chatzidimitriou,
Manolis Kaliorakis, Dimitris Gizopoulos and Shidhartha Das
Index .... .... .... .... .... ..... .... .... .... .... .... ..... .... 273
Chapter 1
Introduction
ChristoforosKachris,BabakFalsafiandDimitriosSoudris
1.1 Introduction
Emerging applications like cloud computing, machine learning, AI and big data
analytics require powerful systems that can process large amounts of data without
consuminghighpower.Furthermore,theseemergingapplicationsrequirefasttime-
to-marketandreduceddevelopmenttimes.Toaddressthelargeprocessingrequire-
ments of emerging applications, novel architectures are required to be adopted by
thedatacentervendorsandthecloudcomputingproviders.
RelyingonMoore’slaw,CPUtechnologieshavescaledinrecentyearsthrough
packinganincreasingnumberoftransistorsonchip,leadingtohigherperformance.
However,on-chipclockfrequencieswereunabletofollowthisupwardtrenddueto
strictpower-budgetconstraints.Thus,afewyearsago,aparadigmshifttomulticore
processorswasadoptedasanalternativesolutionforovercomingtheproblem.With
multicoreprocessors,wecouldincreaseserverperformancewithoutincreasingtheir
clockfrequency.Unfortunately,thissolutionwasalsofoundnottoscalewellinthe
longer term. The performance gains achieved by adding more cores inside a CPU
comeatthecostofvarious,rapidlyscalingcomplexities:inter-corecommunication,
memorycoherencyand,mostimportantly,powerconsumption[1].
In the early technology nodes, going from one node to the next allowed for a
nearly doubling of the transistor frequency, and, by reducing the voltage, power
density remained nearly constant. With the end of Dennard’s scaling, going from
B
C.Kachris ( )
InstituteofCommunicationandComputerSystems(ICCS/NTUA),
Athens,Greece
e-mail:[e-mail]
B.Falsafi
ÉcolePolytechniqueFédéraledeLausanne(EPFL),Lausanne,Switzerland
D.Soudris
DepartmentofElectricalandComputerEngineering,
NationalTechnicalUniversityofAthens,Athens,Greece
©SpringerInternationalPublishingAG,partofSpringerNature2019 1
C.Kachrisetal.(eds.),HardwareAcceleratorsinDataCenters,
https://doi.org/10.1007/978-3-319-92792-3_1
2 C.Kachrisetal.
one node to the next still increases the density of transistors, but their maximum
frequency is roughly the same and the voltage does not decrease accordingly. As
a result, the power density increases now with every new technology node. The
biggestchallengethereforenowconsistsofreducingpowerconsumptionandenergy
dissipationpermm2.
Therefore,thefailureofDennard’sscaling,towhichtheshifttomulticorechips
ispartiallyaresponse,maysoonlimitmulticorescalingjustassingle-corescaling
has been curtailed [2]. This issue has been identified in the literature as the dark
siliconerainwhichsomeoftheareasinthechiparekeptpowereddowninorderto
complywiththermalconstraints[3].Onewaytoaddressthisproblemisthroughthe
utilization of hardware accelerators. Hardware accelerators can be used to offload
theprocessor,increasethetotalthroughputandreducetheenergyconsumption.
1.2 TheEraofAcceleratorsintheDataCenters
As the requirements for processing power of the data centers continue to increase
rapidly,higherperformancecomputingsystemsarerequiredtosustaintheincreased
communicationbandwidthdemandwithinthedatacenter.Currentserverprocessors
cannotaffordablesatisfytherequiredcomputationaldemandofemergingapplication
withoutconsumingexcessivepower.Hardwareacceleratorsprovideaviablesolution
offeringhighthroughput,reducedlatencyandhigherenergyefficiencycomparedto
currentserversbasedoncommodityprocessors.
Thisbookpresentsthemostrecentandpromisingsolutionsthathavebeenpre-
sentedintheareaofthecomputinghardwareacceleratorsinheterogeneousdatacen-
ters.Hardwareacceleratorfordatacentersisaninterdisciplinarytopicforallthecom-
munitiesthatareactiveinthedomainofcomputerarchitectures,high-performance
computing,datacenterdesignandcloudcomputing.
Hardwareacceleratorsaremainlyusedinembeddedsystemstooffloadthepro-
cessors for several tasks like compression, encryption, etc. and to provide higher
performanceandlowerenergyconsumption.Currentdatacentersneedtoembrace
newtechnologiesinordertofacetheincreasednetworktrafficduetoemergingappli-
cationslikecloudcomputing.Hardwareacceleratorsbasedonreconfigurablelogic
(i.e.FPGAs)canprovidehigherthroughputandbetterenergyefficiency.
Hardwareacceleratorindatacentersisanemergingtopicthathasrecentlygained
attentionbymajordatacenterandcloudcomputingvendors.Therecentpurchaseof
Altera(amajorvendorofFPGA-basedaccelerators)byIntel,andthecollaboration
betweenIBMandXilinxshowsthatsoonthehardwareacceleratorswillpenetrate
thehyperscaledatacenterinordertoprovidehigherperformanceandhigherenergy
efficiency.Thisbookaimstoprovideanoverviewofthearchitectures,programming
frameworksandhardwareacceleratorsfortypicalcloudcomputingapplicationsin
datacenters.
Thefollowingsectiongivesanoverviewofthemainchaptersthatarepresented
inthisbook.