GreenDisc: A HW/SW energy optimization framework in globally distributed computation

96e2d41af0b2b857d8ec7cd65d170e72?s=47 GreenLSI
December 03, 2012

GreenDisc: A HW/SW energy optimization framework in globally distributed computation

Marina Zapater attends as speaker to UCAmI 2012.

The main goal of this conference is to provide a discussion forum where researchers and practitioners on Ubiquitous Computing and Ambient Intelligence can meet, disseminate and exchange ideas and problems, identify some of the key issues related to these topics, and explore together possible solutions and future works.

The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991, has recently evolved to a more general paradigm known as Ambient Intelligence (AmI). Ambient Intelligence then represents a new generation of user-centred computing environments aiming to find new ways to obtain a better integration of the information technology in everyday life devices and activities.

Marina has presented our first results within the GreenDISC project, proposing several research lines that target the power optimization in computing systems. In particular, we deal with two novel and highly differentiated computer paradigms that, however, coexist and interact in the current application scenarios: the Wireless Sensor Networks (WSN) and the high-performance computing in Data Centers (DC).

For further information, please, refer to the paper:

M. Zapater, J. L. Ayala, and J. M. Moya, “GreenDisc: a HW/SW energy optimization framework in globally distributed computation,” , J. Bravo, D. López-de Ipiña, and F. Moya, Ed., Springer Berlin Heidelberg, 2012, pp. 1-8. doi:10.1007/978-3-642-35377-2_1

96e2d41af0b2b857d8ec7cd65d170e72?s=128

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December 03, 2012
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  1. GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation

    Marina  Zapater†,  José  L.  Ayala*,  José  M.  Moya‡   †CEI  Campus  Moncloa,  UCM-­‐UPM   *Universidad  Complutense  de  Madrid   ‡  Universidad  Politécnica  de  Madrid     Marina  Zapater  |  UCAMI  2012   1  
  2. Outline •  Mo?va?on   •  Proposed  solu?on   –  The

     GreenDisc  PlaIorm   •  Holis?c  Op?miza?on  Approach   –  Power  op?miza?on  of  all  implied  agents   •  Conclusions     •  Future  Work   Marina  Zapater  |  UCAMI  2012   2  
  3. Motivation •  The  key  to  next-­‐genera?on  e-­‐Health  solu?ons  are  

    wearable  personal  health  systems   –  Biomedical  monitoring    (European  ini?a?ves)   •  World-­‐wide  sensor  deployment:   –  Very  large  amount  of  data   •  Electrocardiogram  sensor  (ECG),  Electromyogram  (EMG),  O2  and   CO2 ,  temperature,  lactate,  movement  sensors.   –  Raw  data  must  be  turned  into  useful  informa?on   Marina  Zapater  |  UCAMI  2012   3    FET      “Guardian  Angels”.  European  ini?a?ve:  FET  Flagships  2013  -­‐  hap://www.ga-­‐project.eu/home  
  4. Motivation •  Need  for  an  accurate,  integrated  and  long-­‐term  

    assessment  and  feedback.   –  Acquire,  monitor  and  analize  data  24/7   •  Need  for  an  applica?on-­‐specific  architecture     –  But  also  provide  flexibility  and  enough  performance   •  Current  issues:  energy  and  heat   –  High  energy  consump?on  and  short  baaery  lifespan  of  sensor   nodes.     •  Heat  produced  by  sensor  nodes  for  biomedical  applica?ons   –  Tackle  the  computa?on  needs  to  obtain  useful  informa?on   from  all  data   •  Energy  consump?on  at  the  Data  Center  level  must  not  put  at  stake     e-­‐Health  deployments   Marina  Zapater  |  UCAMI  2012   4   Current needs and issues
  5. Contributions •  A  HW/SW  energy  op?miza?on  framework   –  Versa?le

     plaIorm  that  integrates  processing,  analysis  and   wireless  communica?on  of  biomedical  data.   –  Both  at  the  WSN-­‐level  and  at  the  Data  Center  level   •  Supports  e-­‐Health  applica?ons  (and  future   evolu?ons)  with  lower  costs  and  shorter  ?me-­‐to-­‐ market   Marina  Zapater  |  UCAMI  2012   5   GreenDisc Platform
  6. GreenDisc Platform •  Based  on  Wireless  Body  Sensor  Networks  (WBSN)

      –  All  sensors  transmit  to  a  PDA   Marina  Zapater  |  UCAMI  2012   6   Context and System Architecture
  7. GreenDisc Platform •  Based  on  Wireless  Body  Sensor  Networks  (WBSN)

      –  All  sensors  transmit  to  a  PDA   •  Computa?on  takes  place  at  Data  Centers   –  Data  storage  and  processing   Marina  Zapater  |  UCAMI  2012   7   Context and System Architecture
  8. GreenDisc Platform •  Power  op?miza?on  in  the  processing  nodes  of

     the   WBSN   –  Design  of  embedded  processors  for  signal  processing   –  Op?miza?on  at  the  radio  interface   –  Design  automa?on  of  applica?ons  for  the  processing  node     •  Power  op?miza?on  in  data  centers   Marina  Zapater  |  UCAMI  2012   8   Holistic Optimization Approach
  9. Holistic Optimization •  Design  of  embedded  processors  for  signal  processing

      –  Architectural  modifica?ons  considering  the  applica?on   mapping,  the  execu?on  profile,  and  the  compiler   op?miza?ons   –  Reducing  the  energy  consump?on  of  the  main  energy   consump?on  sources   •  Selec?on  of    instruc?on  memory  architecture   •  Design  of  func?onal  units  with  tunable  architecture  (dynamic   reconfigura?on)   Marina  Zapater  |  UCAMI  2012   9   Processing nodes of the WBSN (I)
  10. Holistic Optimization •  Instruc?on  memory   produces  highest  energy  

    consump?on   •  Proper  selec?on  of   instruc?on  memory   architecture  impacts  energy   consump?on   –  SPM  -­‐  scratch  pad  memory     –  CELB  -­‐  central  loop     buffer   –  CLLB-­‐  clustered  loop  buffer   Marina  Zapater  |  UCAMI  2012   10   Processing nodes of the WBSN (I)
  11. Holistic Optimization •  Power  op?miza?on  in  the  radio  interface  

    –  Reducing  the  amount  of  informa?on  to  transmit:   •  Framework  for  signal  analysis  to  develop  compressed  sensing   techniques  for  several  bio-­‐signals   •  Case  studies  for  monitoring  bio-­‐signals  with  a  QoS  study   –  Reduce  the  overhead  of  the  transmision  protocol:   •  Study  of  the  impact  of  tuning  several  parameters  of  the  MAC  layer   (development  of  802.15.4  MAC  analy?cal  model)   Marina  Zapater  |  UCAMI  2012   11   Processing nodes of the WBSN (II)  Compressed  sensing:    signal  processing  technique  for  efficiently  acquiring  and  reconstruc?ng  a   signal.  Uses  the  signal  sparseness  or  compressibility  in  some  domain,  allowing  the  en?re  signal  to  be   determined  from  rela?vely  few  measurements.   Candes,  E.  J.  and  Wakin,  M.  B.  (2008)  An  IntroducEon  to  Compressive  Sampling.   IEEE  Signal  Processing  Magazine.  Vol  2  (pp.  21-­‐30)  
  12. Holistic Optimization •  Design  automa?on  of  applica?ons  in  the  processing

      node   –  Aims  to  reduce  the  amount  of  data  transmiaed  to  the   backbone   –  Providing  a  generic  high-­‐level  model  of  the  architecture   •  Analysis  of  the  impact  of  design  parameters  in  power   consump?on   •  Framework  for  automa?c  design  and  op?miza?on  of   applica?ons       Marina  Zapater  |  UCAMI  2012   12   Processing nodes of the WBSN (III)
  13. Holistic Optimization •  Implementa?on  of  several  resource  managing   techniques

     at  different  abstrac?on  levels   •  Exploi?ng  the  heterogeneity  of  applica?ons  and   compu?ng  resources  for  energy  minimiza?on.   –  Proper  usage  of  heterogeneity  can  lead  to  significant   energy  savings   •  Analysis  of  cooling  mechanisms  and  development  of   control  techniques     Marina  Zapater  |  UCAMI  2012   13   Energy Optimization in Data Centers
  14. Holistic Optimization •  Poten?al  benefits  of  workload  characteriza?on  and  

    dynamic  assignment  policies   Marina  Zapater  |  UCAMI  2012   14   Energy Optimization in Data Centers
  15. Conclusion and Future Work •  This  paper  shows  how  the

     GreenDisc  plaIorm  can   op?mize  the  energy  consump?on  of  next-­‐genera?on   e-­‐Health  applica?on  by  combining  the  usage  of:   –  Op?miza?on  policies  at  several  levels  of  the  WBSN   –  Agressive  energy  efficiency  policies  at  the  Data  Center   •  Future  work  will  integrate  all  steps  of  the  plaIorm   and  show  the  overall  savings  for  a  par?cular  e-­‐Health   workload.   Marina  Zapater  |  UCAMI  2012   15  
  16. Questions? Marina  Zapater  |  UCAMI  2012   16   Thank

     you  for    your  a9en:on   Marina  Zapater   Laboratorio  de  Sistemas  Integrados  (LSI)   Universidad  Politécnica  de  Madrid   marina@die.upm.es   hap://greenlsi.die.upm.es