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Towards Formal Approaches to System Resilience Vishal Chandra Sharma *, Arvind Haran *, Zvonimir Rakamaric *, Ganesh Gopalakrishnan *§ {vcsharma, haran,

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Presentation on theme: "Towards Formal Approaches to System Resilience Vishal Chandra Sharma *, Arvind Haran *, Zvonimir Rakamaric *, Ganesh Gopalakrishnan *§ {vcsharma, haran,"— Presentation transcript:

1 Towards Formal Approaches to System Resilience Vishal Chandra Sharma *, Arvind Haran *, Zvonimir Rakamaric *, Ganesh Gopalakrishnan *§ {vcsharma, haran, zvonimir, School of Computing, University of Utah * Supported in part by NSF Award CCF and SRC contract 2013-TJ § Faculty Associate, SUPER (http://super-scidac.org/)

2 Overview Introduction Fault Injector Case Study Fault Detector Concluding Remarks 2

3 Motivation Recent studies show resiliency as a growing area of concern [arg13] [lanl05] MTBF decreasing at a faster rate in exascale computing Dynamic voltage/frequency scaling in low power computing Our goal is to improve application-level resiliency Primary focus is to detect transient faults in a software  Silent data corruption (SDC) 3

4 Motivating Example printf(“x=%d, y=%d”,x,y) if (x 10) y++; int x = 2; int y = 11; else x++; 4

5 Motivating Example printf(“x=%d, y=%d”,x,y) int x = 2; int y = 11; else x++; if (x 10) y++; 5 Program output: x=2, y=12

6 Motivating Example printf(“x=%d, y=%d”,x,y) int x = 3; int y = 11; else x++; if (x 10) y++; 6 LSB position of x flipped

7 Motivating Example printf(“x=%d, y=%d”,x,y) int x = 3; int y = 11; else x++; if (x 10) y++; 7 Program output: x=4, y=11 LSB position of x flipped SDC in the output value of x

8 Our Contribution A LLVM-level fault Injector for evaluation purpose [llvm04] A simple case study on sorting algorithms Demonstrates effectiveness of our solution Highlights importance of design space exploration w.r.t. resiliency A software-level fault detector based on idea of predicate abstraction Applying it in resiliency research is a novel direction! Introduced by Ball to define a novel program coverage metrics [pct05] 8

9 Closely Related Work Low-cost software level detectors  iSWAT by Sahoo et. al. uses likely program invariants [iswat08]  Derives likely invariants by monitoring program properties  Hardware-assisted framework to detect false positives  Error detector by Sloan et.al. [sloan13]  Algorithm based error detector applied to linear solvers  Utilizes algorithmic properties of linear solvers to detect and isolate errors Software-level fault injectors  LLVM-level fault injector developed by Kuijif et. al. [relax10]  Publicly unavailable  A recent study done by a user suggests our fault injector has better fine-grained options [schen13]  LLFI fault injector by Thomas et. al. [thomas13]  Developed around same time as our fault injector, shares many similar features 9

10 Overview Introduction Fault Injector Case Study Fault Detector Concluding Remarks 10

11 Fault Injector 11 Kontrollable Utah’s LLVM based Fault Injector  KULFI KULFI  Indian dessert

12 KULFI: Fault Injection Logic Start Forall dynamic instructions Inject Fault with user provided probability Feasible? Stop 12 Yes No

13 KULFI: Fault Injection Process 13 Program Clang LLVM bitcode LLVM KULFI Dynamic Instruction Count Fault Injecting LLVM bitcode Program Input Vectors LLVM Execution Outcome KULFI SDC SegFault Benign

14 Overview Introduction Fault Injector Case Study Fault Detector Concluding Remarks 14

15 Case Study 15 1 Experiment 200 Fault Injection Campaigns 1 Fault Injection Campaign 100 Program Runs Sorting routines - Bubblesort, Quicksort, Mergesort, Radixsort, Heapsort Total number of fault injections = 200*100 = 20,000!

16 Case Study A dynamic instruction is chosen at random for fault injection A single-bit fault in a random bit position of the dynamic instruction’s register For each fault injection campaign, categorize outcome into SDC, Benign, or Segmentation fault categories  Benign: 41, Segmentation: 29, SDC: 30 16

17 Case Study Plot fault count from a fault category corresponding to a fault injection campaign X axis: Fault count corresponding to a fault injection campaign Y axis: Sorting routines Result shows strong clustering pattern with statistically significant distribution for each fault category 17

18 Results 18

19 Results 19

20 Results 20

21 Overview Introduction Fault Injector Case Study Fault Detector Concluding Remarks 21

22 A Software-Level Approach to Fault Detection Predicates: Boolean program conditionals Predicate State: PP: Program point between two successive program statements BV: Bit-vector representing concrete boolean values of program conditionals at a given program point Predicate State Transition: Current State  Next State 22

23 A Software-Level Approach to Fault Detection Foo(int x, int y){ PP0: If ( x 10 ) { PP1: y++; PP2: } else { PP3: x++; PP4: } PP5: printf(“x=%d, y=%d”,x, y) } 23 Predicates: x 10 Program Points: PP0, PP1, PP2, PP3, PP4, PP5 Input Vectors: x = 2, y = 11 Predicate State at PP0: Predicate State at PP1: Predicate State Transition: 

24 A Software-Level Approach to Fault Detection 24 Start Program P Extract predicate transitions Stop Start Program P Get Predicate Transition Stop Fault Detected Check if Valid ? last transition ? Yes No Yes No Instrumented Program P1 Instrumented Program P2 Profile valid predicate transitions Detect transient faults

25 Predicate Transition Diagram (PTD) 25 Start Program Inject Fault Track Predicate Transitions Merge Predicate Transition Diagram Stop

26 PTD of Foo() 26

27 PTD of dgstrf() in SuperLU [slu99,05,11] 27 SuperLU is a direct linear solver for sparse and nonsymmetric systems of linear equations Available at:

28 PTD of BlkSchlsEqEuroNoDiv() in Blackscholes 28 Financial analysis using blackscholes PDE Part of Parsec 3.0 benchmark suite [parsec08]

29 Overview Introduction Fault Injector Case Study Fault Detector Concluding Remarks 29

30 Concluding Remarks A novel software-level fault detector Enabling infrastructure for resiliency analysis and evaluation through KULFI Recommended use during design space exploration Try out KULFI: https://github.com/soar-lab/KULFIhttps://github.com/soar-lab/KULFI 30

31 References [arg13] Snir, M., et al. Addressing Failures in Exascale Computing. No. ANL/MCS- TM-33. Argonne National Laboratory (ANL), 2013 [lanl05] Michalak, Sarah E., et al. "Predicting the number of fatal soft errors in Los Alamos National Laboratory's ASC Q supercomputer." IEEE Transactions on Device and Materials Reliability, 2005 [llvm04] C. Lattner and V. Adve, “LLVM: A compilation framework for lifelong program analysis & transformation,” in International Symposium on Code Generation and Optimization (CGO), 2004 [pct05] T. Ball, “A theory of predicate-complete test coverage and generation,” in International Conference on Formal Methods for Components and Objects (FMCO), 2005 [iswat08] S. K. Sahoo, M. lap Li, P. Ramachandran, S. V. Adve, V. S. Adve, and Y. Zhou, “Using likely program invariants to detect hardware errors,” in IEEE International Conference on Dependable Systems and Networks (DSN), 2008 [sloan13] Sloan, Joseph, Rakesh Kumar, and Greg Bronevetsky. "An algorithmic approach to error localization and partial recomputation for low-overhead fault tolerance.“, in IEEE International Conference on Dependable Systems and Networks (DSN),

32 References [slu99] Demmel, James W., et al. "A supernodal approach to sparse partial pivoting.“ SIAM Journal on Matrix Analysis and Applications, 1999 [slu05] Li, Xiaoye S. "An overview of SuperLU: Algorithms, implementation, and user interface." ACM Transactions on Mathematical Software (TOMS), 2005 [slu11] Li, X. S., Demmel, J. W., Gilbert, J. R., Grigori, L., Shao, M., & Yamazaki, I. (2011). SuperLU Users’ Guide. url: lbl. gov/~ xiaoye/SuperLU/superlu_ug. Pdf. [sprs11] Davis, Timothy A., and Yifan Hu. "The University of Florida sparse matrix collection." ACM Transactions on Mathematical Software (TOMS), 2011 [parsec08] C. Bienia, S. Kumar, J. Singh, and K. Li, “The PARSEC benchmark suite: Characterization and architectural implications,” ser. PACT, 2008 [relax10] M. de Kruijf, S. Nomura, and K. Sankaralingam, “Relax: An ar- chitectural framework for software recovery of hardware faults,” in International Symposium on Computer Architecture (ISCA), 2010 [thomas13] Thomas, Anna, and Karthik Pattabiraman. "Error Detector Placement for Soft Computation." in International Conference on Dependable Systems and Networks (DSN), [schen13] S. Chen, personal communication,

33 Acknowledgements Pedro Diniz Prabhakar Kudva Shuvendu Lahiri Karthik Pattabiraman Sui Chen Anonymous reviewers of PRDC conference who reviewed our paper 33

34 Thank you! 34


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