Sherief  Reda  School  of  Engineering    Brown  University  Providence,  RI    

1  

XPS:  EXPL:  Symbio-c  Power  Management  for    Integrated  CPU  -­‐  GPU  Pla?orms  

Supported  by  NSF  grant  1438958:  August  2014  –  August  2017  

Mo=va=on  

•  Power  and  thermal  bo@lenecks  are  one  of  the  main  reasons  mul=-­‐core  processors  became  mainstream.  

•  Customiza=on  (domain  or  applica=on  specific)  à  heterogeneous  processors.  

•  Power/thermal  management  of  heterogeneous  processors  is  key  to  maximize  energy  efficiency.  –  Main  degrees  of  freedom:  sleep  modes,  dynamic  voltage  &  frequency  

scaling  (DVFS),  and  scheduling  

2  

x86$Module1,(core0,,core1),

x86$Module2,(core2,,core3),

L2$M1, L2$M2,SIMD1,SIMD2,SIMD3,SIMD4,SIMD5,SIMD6,

UNB

GMC,

DDR3,controller,

PCIe,and,Display,,controllers,

GPU$Aux,

GPU$Aux,

GPU$Aux,

GPU$Aux,

AMD  A10-­‐5700  

Understanding  the  implica=ons  CPU+GPU  pla[orms  on  power  management  

3  

§  Parallel  programming  languages  (e.g.,  OpenCL)  for  heterogeneous  pla[orms  enable  execu=on  of  threads  on  CPU  and/or  GPU  à  scheduling  decisions  are  executed  within  the  applica=on.  

§  DVFS  and  scheduling  are  =ghtly  coupled  for  CPU+GPU  pla[orms  in  comparison  with  tradi=onal  mul=-­‐core  processors.  

à  Need  to  re-­‐think  power  management  for  heterogeneous  processors.  

x86$Module1,(core0,,core1),

x86$Module2,(core2,,core3),

L2$M1, L2$M2,SIMD1,SIMD2,SIMD3,SIMD4,SIMD5,SIMD6,

UNB

GMC,

DDR3,controller,

PCIe,and,Display,,controllers,

GPU

$Aux,

GPU

$Aux,

GPU$Aux,

GPU$Aux,

CFD (CPU-GPU) CFD (CPU)

22.2#

4.9#4.1#

7.5#

3.4#

x86#modules# L25caches# Memory#(UNB+DDR3+GMC)# GPU#(SIMD+Aux)# Others#

3.5#1.7#

4.5#

16.2#

3.2#10.7#

3.1#

5.1#

19.1#

3.7#

5.7#

1.9#3.2#

5.2#

2.7#

1.4 GHz; PTOTAL = 29.1 W; Runtime = 40.7 s

3.0 GHz; PTOTAL = 41.8 W; Runtime = 35.2 s

1.4 GHz; PTOTAL = 18.8 W;

Runtime = 369.5 s

3.0 GHz; PTOTAL = 42.2 W;

Runtime = 187.3 s

Tmax = 53.9 C Tmax = 76.4 C Tmax = 39.5 C Tmax = 88.0 C

40

60

80

Power breakdown

(W)

Thermal maps

(C)

C

AMD  A10-­‐5700  

Project  goal  

4  

Project  Goal:  Explore  a  new  paradigm  for  symbio=c  power  management  that  exploits  the  structure  and  seman=cs  of  parallel  programming  languages  for  integrated  heterogeneous  CPU-­‐GPU  pla[orms,  in  par=cular  OpenCL,  to  deliver  improved  power  management  capabili=es.  

Intel  Core  i5  4670K  

AMD  Kaveri  A10-­‐7700K    

Symbio=c  power  management  

5  

OpenCL  applica=on  

OpenCL  applica=on  

OpenCL  applica=on  

power/thermal  management  (OS)    

phase   phase   phase  

CPU  DVFS  

GPU  DVFS  

CPU+GPU    pla?orm  

schedule  

performance  &  power  budget  

schedule  

sensors  

Exis=ng  power  management  paradigms  

Proposed  Symbio=c  power  management  

paradigm  

Symbiosis  between  parallel  (OpenCL)  applica=on  and  OS-­‐based  power  management.  

power/thermal  management  (OS)  

CPU+GPU    pla?orm  

applica=on1  

applica=on  n  IBM  Power  8  

phases   DVFS   schedule  sensors  

performance  &  power  budget  

Results  from  direct  phase  iden=fica=on  

6  

0 5 10 15 20 25

10

20

30

40

50

60

time (s)

Pow

er (W

)

Symbiotic3.4 GHz1.4 GHzPC−based

•  PC  is  typical  performance  counter  based  power  management.  •  Symbio=c  is  based  on  direct  phase  iden=fica=on  from  OpenCL.  •  Phase  informa=on  is  used  to  determine  DVFS  levels.  •  15%  on  average  improvement  in  power  consump=on  compared  to  PC-­‐

based  methods  with  the  same  run=me!  

Streamcluster  from  Rodinia  

Work  in  progress  

•  Applica=on-­‐based  models  to  predict  performance  and  power  on  CPU  &  GPU.  

•  Deriving  scheduling  decisions  based  on  models  and  target  performance  and  power  budgets.  

•  Aggrega=on  of  phases  from  mul=ple  OpenCL  applica=ons.  •  Simultaneous  control  of  DVFS  and  scheduling  •  Wrappers/API  for  communica=ng  power  management  

decisions  to  applica=ons.      7  

OpenCL  applica=on  

OpenCL  applica=on  

OpenCL  applica=on  

power/thermal  management  (OS)    

phase   phase   phase  

CPU  DVFS  

GPU  DVFS  

CPU+GPU    pla?orm  

schedule  

performance  &  power  budget  

schedule  

sensors