Speculative Parallelization. Devarshi Ghoshal

Size: px

Start display at page:

Download "Speculative Parallelization. Devarshi Ghoshal"

Domenic Gilmore
6 years ago
Views:

1 Speculative Parallelizatin Devarshi Ghshal Indiana University, Blmingtn 10/10/2011 1

2 Agenda Speculative Parallelizatin FastFrward-A Speculatin using Checkpint/Restart System Design Sftware-based Speculatin Systems Analysis Perfrmance Benchmarks Current Status Future Wrk References Indiana University, Blmingtn 10/10/2011 2

3 Speculative Parallelizatin A technique t execute lps, which cannt be classified as parallel at cmpile time, in parallel Writing with sequential semantics & letting the system figure ut whether a regin can really be parallelized safely In case f any dependency, the invlved iteratins are stpped and re-executed in rder Indiana University, Blmingtn 10/10/2011 3

4 Executin Semantics- Case 1: Crrect Speculatin 1S 1 V S Speculatr V Verifier 2S 2 V 3S 3 V Case 1: All speculatins are crrect Indiana University, Blmingtn 10/10/2011 4

5 Case 2: Incrrect Speculatin 1S 1 V S Speculatr V Verifier 3 V 2S 3S 2 V 3S 3 V Case 2: Result f 2S is wrng Indiana University, Blmingtn 10/10/2011 5

FastFrward- Speculatin using Checkpint/Restart Checkpint/Restart Duplicate and unrll prcesses dynamically N interventin by the kernel Everything ccurs in user-space Lw

6 FastFrward- Speculatin using Checkpint/Restart Checkpint/Restart Duplicate and unrll prcesses dynamically N interventin by the kernel Everything ccurs in user-space Lw verhead, maximum prtability Distributed speculatin ver clusters Using DMTCP High intercnnectin netwrks fr migrating and exchanging data Indiana University, Blmingtn 10/10/2011 6

7 Example vid f(){ duble a[num_elements]; duble r[num_elements]; int p[num_elements]; : fr(i = 0; i < NUM_ELEMENTS; i++){ a[p[i]] = cmpute_sme_value(); } : fr(i = 0; i < NUM_ELEMENTS; i++){ r[i] = use_value(a); } : } Indiana University, Blmingtn 10/10/2011 7

8 FastFrward Transfrmed Prgram vid f(){ duble a[num_elements]; duble r[num_elements]; int p[num_elements]; : fr(i = 0; i < NUM_ELEMENTS; i++){ a[p[i]] = cmpute_sme_value(); } : fr(i = 0; i < NUM_ELEMENTS; i++){ r[i] = use_value(a); } : } vid f(){ : if((type=dmtcpcheckpint()) == VERIFIER){ fr(i = 0; i < NUM_ELEMENTS; i++){ a[p[i]] = cmpute_sme_value(); } } else{ // SPECULATOR #pragma mp parallel fr fr(i = 0; i < NUM_ELEMENTS; i++){ a[p[i]] = cmpute_sme_value(); } } : } Indiana University, Blmingtn 10/10/2011 8

9 Executin Pattern Speculative regin-1 Verifier-1 Sequential regin-1 Speculative regin-2 Verifier-2 Speculative regin-3 Verifier-3 Sequential regin-2 FastFrward-ed Prgram t Sequential regin-3 Indiana University, Blmingtn Original 10/10/ Prgram

10 Intra-nde FastFrward Indiana University, Blmingtn 10/10/

11 Inter-nde FastFrward Indiana University, Blmingtn 10/10/

12 Implementatin Directry-Service MPI-Helper threads: Remte-cmmunicatin Thread Lcal-cmmunicatin Thread Multi-level speculatin One speculatr, many verifiers mdel Kill_frm_parent, Verifier_fails Idle Kill_frm_parent, Verifier_succeeds Speculating FF_frk Verifying Indiana University, Blmingtn 10/10/

13 Sftware-based Speculatin Systems FastTrack Sftware Behavir Oriented Parallelizatin Transactin Memry Indiana University, Blmingtn 10/10/

Fast Track Creates dual track regins which invlves cde that can be run speculatively Runs unptimized cde parallely (against sequential versin) n multiple prcessrs Checks

14 Fast Track Creates dual track regins which invlves cde that can be run speculatively Runs unptimized cde parallely (against sequential versin) n multiple prcessrs Checks crrectness after sequential versin is executed Prceeds with speculative versin if results are crrect / sequential versin therwise Indiana University, Blmingtn 10/10/

15 Lp Semantics Indiana University, Blmingtn 10/10/

16 Functin Semantics Indiana University, Blmingtn 10/10/

17 System Design Cmpiler supprt Recrds changes made by bth dual track regins Cmpiler s inherent supprt fr stack variables Cpy n write + access map fr glbal & heap Run time supprt Transfer pages f mdified data using shared pipe Cmpare memry state at the end f dual track regin Indiana University, Blmingtn 10/10/

18 System Limitatins N Fault Tlerance Kernel Patching Limited Use f Resurces Special care fr all Prgram Terminatin Pints inside Speculative Regin Indiana University, Blmingtn 10/10/

19 Sftware Behavir Oriented Parallelizatin Prgrammable sftware speculatin Prgram parallelized based n partial infrmatin abut prgram behavir User r analysis tl marks pssibly parallel regins Runtime system executes these regins speculatively Critical-path minimizatin Value-based crrectness checking N change t the underlying hardware r perating system Indiana University, Blmingtn 10/10/

20 System Design Pssibly Parallel Regins (PPR) Marking the start and end f the regin with matching markers: BeginPPR(p) and EndPPR(p) Prtects the entire address space by dividing it int pssible shared and privatizable subsets The executin starts as the lead prcess Uses cncurrent executins t hide the speculatin verhead ff the critical path At a (pre-specified) speculatin depth k, up t k prcesses are used t execute the next k PPR instances Indiana University, Blmingtn 10/10/

21 State Islatin Thread-based systems Weak islatin The updates f ne thread are visible t ther threads BOP Strng islatin The intermediate results f the lead prcess are nt made visible t speculatin prcesses until the lead prcess finishes the first PPR Strng islatin cmes naturally with prcess-based prtectin Indiana University, Blmingtn 10/10/

22 System Limitatins N Fault Tlerance Limited Use f Resurces Extra care fr handling f lead prcess Validatin Overhead Three types f data prtectin Page-based prtectin f shared data Value-based checking Likely private data Indiana University, Blmingtn 10/10/

23 Amdahl s Law Used t find Speedup fr sme enhancement Fractin(enhanced) - The fractin f the cmputatin time in the riginal cmputer that can be cnverted t take advantage f the enhancement Speedup(enhanced) - The imprvement gained by the enhanced executin mde; that is, hw much faster the task wuld run if the enhanced mde were used fr the entire prgram Indiana University, Blmingtn 10/10/

24 Mathematical Analysis The maximum speedup, S, is given by: S = t T s Speculatin-enabled cmputatin time, T s = T + pkt + 1 p kt s Ttal running time f the riginal cde, t = T k + 1 where, k: number f speculative regins T: time t execute each regin s: speedup f each speculative regin p: prbability f each successful speculatin and, there is 1 nn-speculative regin Indiana University, Blmingtn 10/10/

25 Perfrmance Benchmarks Intra-nde FastFrward Using Shared-memry Using Named-pipes Indiana University, Blmingtn 10/10/

26 Perfrmance Benchmarks (cnt. ) Inter-nde FastFrward Varying the speedup f speculative versin ver nn-speculative Varying the available depth f speculatin Indiana University, Blmingtn 10/10/

27 Perfrmance Benchmarks (cnt. ) Varying the size f data t be cmpared fr verificatin Varying the size f checkpint data Varying the prbability f speculatin success Indiana University, Blmingtn 10/10/

28 Advantages ver Existing Systems N Parent-Child Prcess Relatinship Inherent Fault Tlerance Efficient Use f resurces in a Cluster Indiana University, Blmingtn 10/10/

29 Current Limitatins Lcal cmmunicatin using pipes Reading check-pinted data thrugh NFS Only supprts basic data-types High Energy Usage Indiana University, Blmingtn 10/10/

30 Future Wrk Extending the system t supprt recursive datastructures and memry references Optimizing Implementatin Shared memry implementatin fr inter-nde FastFrward Incremental checkpinting Checkpinting int memry Extending supprt t higher-level scripting languages Indiana University, Blmingtn 10/10/

31 References Devarshi Ghshal, Sreesudhan R Ramkumar, and Arun Chauhan. Distributed Speculative Parallelizatin using Checkpint Restart. In Prceedings f the Internatinal Cnference n Cmputatinal Science (ICCS), L.-L. Chen, Y. Wu, Aggressive cmpiler ptimizatin and parallelizatin with thread-level speculatin, in: Internatinal Cnference n Parallel Prcessing (ICPP), 2003 K. Kelsey, T. Bai, C. Ding, C. Zhang, Fast Track: A sftware system fr speculative prgram ptimizatin, in: Prceedings f the Internatinal Sympsium n Cde Generatin and Optimizatin, 2009, pp N. Shavit, D. Tuitu, Sftware transactinal memry, in: Prceedings f the Furteenth Annual ACM Sympsium n Principles f Distributed Cmputing, 1995, pp J. Ansel, K. Arya, G. Cperman, DMTCP: Transparent checkpinting fr cluster cmputatins and the desktp, in: Prceedings f the 23 rd IEEE Internatinal Parallel and Distributed Prcessing Sympsium (IPDPS), 2009 C. Tian, M. Feng, V. Nagarajan, R. Gupta, Speculative parallelizatin f sequential lps n multicres, Internatinal Jurnal f Parallel Prgramming 37 (5) (2009) Indiana University, Blmingtn 10/10/

32 Questins & Clarificatins Indiana University, Blmingtn 10/10/

Speculative Parallelization Technology s only constant is CHANGE. Devarshi Ghoshal Sreesudhan

Speculative Parallelization Technology s only constant is CHANGE Devarshi Ghoshal Sreesudhan Agenda Moore s law What is speculation? What is parallelization? Amdahl s law Communication between parallely