FMCAD	  Panel:	          Model	  Checking	  in	  the	  Cloud	                       Olivier	  Coudert	                    ...
Topics	  •  Cloud	  compuDng	  •  Distributed	  model	  checking	  •  Challenges	                                         ...
Cloud	  CompuDng	  Promises	  •  On-­‐demand	  compuDng	  resources	  •  No	  upfront	  costs	     –  pay	  as	  you	  go	...
Performance	  Scaling	          Wall	  %me	  (sec)	     100000	                                      10000	               ...
Distributed	  Model	  Checking	  •  Parallelism	  has	  many	  flavors	  •  In	  pracDce:	  MIMD	     –  Network	  of	  mac...
Explicit	  State	  ExploraDon	  •  Explore	  state	  one	  by	  one	      –  DFS	  or	  BFS	  state	  exploraDon	      –  ...
Implicit	  State	  exploraDon	  •  BDD-­‐based	     –  BFS	  state	  exploraDon	     –  Mostly	  memory	  limited	  •  Par...
Bounded	  Model	  Checking	  •  SAT-­‐based	     –  Unroll	  model	  k	  Dmes	     –  Mostly	  Dme	  limited	  •  Parallel...
Cloud	  Models	          •  Private	  cloud	  managed	  by	  EDA	  vendor	              –  Aldec	  (logic	  simulaDon)	   ...
Cloud	  Models	           •  Public	  cloud	  configured	  by	  EDA	  vendor	               –  Synopsys	  (logic	  simulaDo...
Cloud	  Models	           •  Cloud	  pla`orm	  configured	  and	  managed	  by	  a	  3rd	  party	                –  Xuropa	...
Challenges	           •  Legal	               –  SLA	               –  Liability	  in	  case	  of	  data	  loss	  or	  bre...
Challenges	           •  Technical	               –  Scalability	  of	  applicaDon	               –  Fast,	  fault-­‐toler...
Rethink	  for	  distributed	  in	  the	  cloud	             	  	          1Gpbs	  LAN	   Hard	  drive	       SSD	         ...
Conclusion	  •  Cloud	  compuDng	     –  Large,	  cheap,	  readily	  available	  compute	  grid	  •  Model	  checking	    ...
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Model checking in the cloud


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Model checking in the cloud --
Cloud computing where computing is provided as a utility is finally a reality. This new paradigm is shaping the way hardware and software is designed. One of the main attractions of the cloud is its elasticity. This empowers users with the ability to dynamically change their hardware requirements by paying for resource usage by the hour. Compute-intensive applications such as model checking can potentially benefit from such an infrastructure. In this panel, we will address the following questions:
- How can model checking leverage the advantages of distributed and multi-core systems in the cloud?

o Is this new paradigm suitable for model checking?

o What are possible solutions beyond an “embarrassingly parallel” approach of running a single property per core?

o Is there a specific subset of properties that might be more suitable to this form of analysis?

- What is needed from the research and engineering community to achieve adoption within the next 5 years?

- Would a drive to model checking in the cloud increase the industry’s adoption of formal technology?

- What issues need to be addressed for design houses to adopt this technology and will the current license model of EDA tools change to adapt to the new requirements?

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Model checking in the cloud

  1. 1. FMCAD  Panel:   Model  Checking  in  the  Cloud   Olivier  Coudert   SiCAD  Inc.     October  25th,  2012   TM1   CLOUD-AIDED SILICON DESIGN
  2. 2. Topics  •  Cloud  compuDng  •  Distributed  model  checking  •  Challenges   TM CLOUD-AIDED SILICON DESIGN
  3. 3. Cloud  CompuDng  Promises  •  On-­‐demand  compuDng  resources  •  No  upfront  costs   –  pay  as  you  go  •  Scalable   –  100’s  of  cores  assembled  in  a  compute  grid   –  TB’s  of  storage   –  1Gbps  LAN,  10Gbps  HPC  •  Expand  geographic  reach   TM CLOUD-AIDED SILICON DESIGN
  4. 4. Performance  Scaling   Wall  %me  (sec)   100000   10000   1000   10   100   1000   #cores   •  Cluster  setup  Dme  :  10-­‐15mn   •  ApplicaDon:  physical  verificaDon   •  10  cores:  13h42mn   •  768  cores:  17mn   TM4   CLOUD-AIDED SILICON DESIGN
  5. 5. Distributed  Model  Checking  •  Parallelism  has  many  flavors  •  In  pracDce:  MIMD   –  Network  of  machines   –  Distributed  memory  with  mulDple  cores  •  Model  checking   –  LTL,  CTL,  etc   –  State  exploraDon   TM CLOUD-AIDED SILICON DESIGN
  6. 6. Explicit  State  ExploraDon  •  Explore  state  one  by  one   –  DFS  or  BFS  state  exploraDon   –  Need  to  recognize  visited  states   –  Mostly  memory  limited  •  ParallelizaDon   –  ParDDon  state  space,  and  assign  each  parDDon  to   a  node  of  the  grid   –  ParDDon:  hashing,  windowing   TM CLOUD-AIDED SILICON DESIGN
  7. 7. Implicit  State  exploraDon  •  BDD-­‐based   –  BFS  state  exploraDon   –  Mostly  memory  limited  •  ParallelizaDon   –  ParDDon  variables,  and  assign  each  parDDon  to  a   node  of  the  grid   –  ParDDon  made  of  consecuDve  variables   –  BDD  node  management  is  breadth-­‐first   –  Distributed  hash-­‐tables  for  BDD  operaDons  caches   TM CLOUD-AIDED SILICON DESIGN
  8. 8. Bounded  Model  Checking  •  SAT-­‐based   –  Unroll  model  k  Dmes   –  Mostly  Dme  limited  •  ParallelizaDon   –  ParDDon  Boolean  space  (assume  some  variables   have  some  constants  values)   –  Conflict  clauses  need  to  be  shared   TM CLOUD-AIDED SILICON DESIGN
  9. 9. Cloud  Models   •  Private  cloud  managed  by  EDA  vendor   –  Aldec  (logic  simulaDon)   –  Nimbic  (3D  simulaDon)   –  Tabula  (FPGA  synthesis)   –  Cadence  (reference  flow)   use   EDA  vendor   configure   TM9   CLOUD-AIDED SILICON DESIGN
  10. 10. Cloud  Models   •  Public  cloud  configured  by  EDA  vendor   –  Synopsys  (logic  simulaDon  in  AWS)   EDA  vendor   configure   TM10   CLOUD-AIDED SILICON DESIGN
  11. 11. Cloud  Models   •  Cloud  pla`orm  configured  and  managed  by  a  3rd  party   –  Xuropa  (SW  evaluaDon  in  AWS,  used  by  Synopsys,  Cadence,  and  Xilinx)   –  Plunify  (FPGA  synthesis  in  AWS)   –  SiCAD   EDA  vendor   Pla`orm   EDA  vendor   EDA  vendor   EDA  vendor   TM11   CLOUD-AIDED SILICON DESIGN
  12. 12. Challenges   •  Legal   –  SLA   –  Liability  in  case  of  data  loss  or  breach   –  Geographical  locaDon  of  data   –  Cloud  provider  origin   •  MulD-­‐party  agreement   –  MulDple  EDA  vendors,  design  house,  foundry,  cloud   provider   •  Business  model   –  SW  needs  a  pay-­‐as-­‐you-­‐go  model   –  Risk  to  cannibalize  TBL’s  revenue  for  EDA  vendors   TM12   CLOUD-AIDED SILICON DESIGN
  13. 13. Challenges   •  Technical   –  Scalability  of  applicaDon   –  Fast,  fault-­‐tolerant,  compute  grid  provisioning  and  setup   –  Volume  of  data  transfer   •  10GB  @  30Mbps:  44mn   •  10GB  @  1Gbps:  1mn20sec   •  Security   –  Highly  sensiDve  data  (design,  SW,  and  IP)   •  Data  confidenDality  –transmission,  at  rest   •  Data  integrity  –e.g.,  disaster  recovery   •  Data  availability  –upDme,  latency   •  Data  disposal  –data  removal  and  storage  disposal   –  Customer  may  want  to  keep  its  SW  usage  confidenDal   TM13   CLOUD-AIDED SILICON DESIGN
  14. 14. Rethink  for  distributed  in  the  cloud       1Gpbs  LAN   Hard  drive   SSD   RAM   0.5ms   latency   datacenter   3-­‐10ms   0.1ms   100  ns   roundtrip   bandwidth   128  MB/s   140  MB/s   100-­‐600  MB/s  6-­‐17  GB/s   capacity   N/A   up  to  8TB   256GB  -­‐  1TB   4-­‐64GB   cost   free   $0.05/GB   $0.65/GB   $5-­‐10/GB  •  Writes  are  expensive,  reads  are  cheap   –  Once  read,  data  is  cached   –  Writes  are  ~50x  slower  than  read  •  It  might  be  faster  to  move  data  chunks  in  the  LAN  than   reading  it  from  a  hard  drive  •  SSD  is  changing  the  way  data  can  be  managed   TM CLOUD-AIDED SILICON DESIGN
  15. 15. Conclusion  •  Cloud  compuDng   –  Large,  cheap,  readily  available  compute  grid  •  Model  checking   –  Need  algorithms  that  can  leverage  a  large   distributed  compuDng  network  (100-­‐1000+  cores)   –  Licensing  needs  to  follow  burst  compuDng  models   –  Security  is  a  bojleneck   TM CLOUD-AIDED SILICON DESIGN