1. On Cosmic Rays, Bat Droppings, and what to do about them David Walker Princeton University with Jay Ligatti, Lester Mackey, George Reis and David August

2. A Little-Publicized Fact 1 + 1 = 23

3. How do Soft Faults Happen? High-energy particles pass through devices and collides with silicon atom Collision generates an electric charge that can flip a single bit “Galactic Particles” Are high-energy particles that penetrate to Earth’s surface, through buildings and walls “Solar Particles” Affect Satellites; Cause < 5% of Terrestrial problems Alpha particles from bat droppings

4. How Often do Soft Faults Happen?

5. NYC Tucson, AZ Denver, CO Leadville, CO IBM Soft Fail Rate Study; Mainframes; 83-86

6. How Often do Soft Faults Happen? NYC Tucson, AZ Denver, CO Leadville, CO IBM Soft Fail Rate Study; Mainframes; 83-86 [Zeiger-Puchner 2004] Some Data Points: 83-86: Leadville (highest incorporated city in the US): 1 fail/2 days 83-86: Subterrean experiment: under 50ft of rock: no fails in 9 months 2004: 1 fail/year for laptop with 1GB ram at sea-level 2004: 1 fail/trans-pacific roundtrip [Zeiger-Puchner 2004]

7. How Often do Soft Faults Happen? Soft Error Rate Trends [Shenkhar Borkar, Intel, 2004] we are approximately here 6 years from now

8. How Often do Soft Faults Happen? Soft Error Rate Trends [Shenkhar Borkar, Intel, 2004] Soft error rates go up as: Voltages decrease Feature sizes decrease Transistor density increases Clock rates increase we are approximately here 6 years from now all future manufacturing trends

9. How Often do Soft Faults Happen? In 1948, Presper Eckert notes that cascading effects of a single-bit error destroyed hours of Eniac’s work. [Zeiger-Puchner 2004] In 2000, Sun server systems deployed to America Online, eBay, and others crashed due to cosmic rays [Baumann 2002] “The wake-up call came in the end of 2001... billion-dollar factory ground to a halt every month due to... a single bit flip” [Zeiger-Puchner 2004] Los Alamos National Lab Hewlett-Packard ASC Q 2048-node supercomputer was crashing regularly from soft faults due to cosmic radiation [Michalak 2005]

10. What Problems do Soft Faults Cause? a single bit in memory gets flipped a single bit in the processor logic gets flipped and there’s no difference in external observable behavior the processor locks up the computation is silently corrupted register value corrupted (simple data fault) control-flow transfer goes to wrong place (control-flow fault) different opcode interpreted (instruction fault)

11. FT Solutions Redundancy in Information eg: Error correcting codes (ECC) pros: protects stored values efficiently cons: difficult to design for arithmetic units and control logic Redundancy in Space multiple redundant hardware devices eg: Compaq Non-stop Himalaya runs two identical programs on two processors, comparing pins on every cycle pros: efficient in time cons: expensive in hardware (double the space) Redundancy in Time perform the same computations at different times (eg: in sequence) pros: efficient in hardware (space is reused) cons: expensive in time (slower --- but not twice as slow)

12. Solutions in Time Compiler generates code containing replicated computations, fault detection checks and recovery routines eg: Rebaudengo 01, CFCSS [Oh et al. 02], SWIFT or CRAFT [Reis et al. 05],... pros: software-controlled --- new code with better reliability properties may be deployed whenever, wherever needed cons: for fixed reliability policy, slower than specialized hardware solutions

13. Solutions in Time Compiler generates code containing replicated computations, fault detection checks and recovery routines eg: Rebaudengo 01, CFCSS [Oh et al. 02], SWIFT or CRAFT [Reis et al. 05],... pros: flexibility --- new code with better reliability properties may be deployed whenever, wherever needed cons: for fixed reliability policy, slower than specialized hardware solutions cons: it might not actually work

14. “It might not actually work”

15. Agenda Answer basic scientific questions about software- controlled fault tolerance: Do software-only or hybrid SW/HW techniques actually work? For what fault models? How do we specify them? How can we prove it? Build compilers that produce software that runs reliably on faulty hardware Moreover: Let’s not replace faulty hardware with faulty software. Let’s prove every binary we produce is fault tolerant relative to the specified fault model

16. compiler front end reliability transform ordinary program fault tolerant program optimized FT program optimization A Possible Compiler Architecture

17. compiler front end reliability transform ordinary program fault tolerant program optimized FT program optimization A Possible Compiler Architecture Testing Requirements: all combinations of features multiplied by all combinations of faults

18. compiler front end reliability transform ordinary program fault tolerant program reliability proof checker optimized FT program modified proof optimization A More Reliable Compiler Architecture

19. compiler front end reliability transform ordinary program fault tolerant program reliability proof checker optimized FT program modified proof optimization Testing Requirements: all combinations of features multiplied by all combinations of faults A More Reliable Compiler Architecture

20. Central Technical Challenges Designing

21. Step 1: Lambda Zap Lambda Zap [ICFP 06] a lambda calculus that exhibits intermittent data faults + operators to detect and correct them a type system that guarantees observable outputs of well-typed programs do not change in the presence of a single fault types act as the “proofs” of fault tolerance expressive enough to implement an ordinary typed lambda calculus End result: the foundation for a fault-tolerant typed intermediate language

22. Lambda zap models simple data faults only The Fault Model ( M, F[ v1 ] )---> ( M, F[ v2 ] ) Not modelled: memory faults (better protected using ECC hardware) control-flow faults (ie: faults during control-flow transfer) instruction faults (ie: faults in instruction opcodes) Goal: to construct programs that tolerate 1 fault observers cannot distinguish between fault-free and 1-fault runs

23. Lambda to Lambda Zap: The main idea let x = 2 in let y = x + x in out y

24. Lambda to Lambda Zap: The main idea let x = 2 in let y = x + x in out y let x1 = 2 in let x2 = 2 in let x3 = 2 in let y1 = x1 + x1 in let y2 = x2 + x2 in let y3 = x3 + x3 in out [y1, y2, y3] atomic majority vote + output replicate instructions

25. Lambda to Lambda Zap: The main idea let x = 2 in let y = x + x in out y let x1 = 2 in let x2 = 2 in let x3 = 7 in let y1 = x1 + x1 in let y2 = x2 + x2 in let y3 = x3 + x3 in out [y1, y2, y3]

26. Lambda to Lambda Zap: The main idea let x = 2 in let y = x + x in out y let x1 = 2 in let x2 = 2 in let x3 = 7 in let y1 = x1 + x1 in let y2 = x2 + x2 in let y3 = x3 + x3 in out [y1, y2, y3] but final output unchanged corrupted values copied and percolate through computation

27. Lambda to Lambda Zap: Control-flow let x = 2 in if x then e1 else e2 let x1 = 2 in let x2 = 2 in let x3 = 2 in if [x1, x2, x3] then [[ e1 ]] else [[ e2 ]] majority vote on control-flow transfer recursively translate subexpressions

28. Lambda to Lambda Zap: Control-flow let x = 2 in if x then e1 else e2 let x1 = 2 in let x2 = 2 in let x3 = 2 in if [x1, x2, x3] then [[ e1 ]] else [[ e2 ]] majority vote on control-flow transfer (function calls replicate arguments, results and function itself) recursively translate subexpressions

29. Almost too easy, can anything go wrong?...

30. yes! optimization reduces replication overhead dramatically (eg: ~ 43% for 2 copies), but can be unsound! original implementation of SWIFT [Reis et al.] optimized away all redundancy leaving them with an unreliable implementation!!

31. Faulty Optimizations let x1 = 2 in let x2 = 2 in let x3 = 2 in let y1 = x1 + x1 in let y2 = x2 + x2 in let y3 = x3 + x3 in out [y1, y2, y3] In general, optimizations eliminate redundancy, fault-tolerance requires redundancy. CSE let x1 = 2 in let y1 = x1 + x1 in out [y1, y1, y1]

32. The Essential Problem voters depend on common value x1 let x1 = 2 in let y1 = x1 + x1 in out [y1, y1, y1] bad code:

33. let x1 = 2 in let x2 = 2 in let x3 = 2 in let y1 = x1 + x1 in let y2 = x2 + x2 in let y3 = x3 + x3 in out [y1, y2, y3] The Essential Problem voters depend on common value x1 let x1 = 2 in let y1 = x1 + x1 in out [y1, y1, y1] bad code: good code: voters do not depend on a common value

34. The Essential Problem voters depend on a common value let x1 = 2 in let y1 = x1 + x1 in out [y1, y1, y1] bad code: let x1 = 2 in let x2 = 2 in let x3 = 2 in let y1 = x1 + x1 in let y2 = x2 + x2 in let y3 = x3 + x3 in out [y1, y2, y3] good code: voters do not depend on a common value (red on red; green on green; blue on blue)

35. A Type System for Lambda Zap Key idea: types track the “color” of the underlying value & prevents interference between colors Colors C ::= R | G | B Types T ::= C int | C bool | C (T1,T2,T3)  (T1’,T2’,T3’)

36. Sample Typing Rules (x : T) in G --------------- G |--z x : T ------------------------ G |--z C n : C int Judgement Form: G |--z e : T where z ::= C |. simple value typing rules: ------------------------------ G |--z C true : C bool

37. Sample Typing Rules G |--z e1 : R bool G |--z e2 : G bool G |--z e3 : B bool G |--z e4 : T G |--z e5 : T ----------------------------------------------------- G |--z if [e1, e2, e3] then e4 else e5 : T Judgement Form: G |--z e : T where z ::= C |. G |--z e1 : R int G |--z e2 : G int G |--z e3 : B int G |--z e4 : T ------------------------------------ G |--z out [e1, e2, e3]; e4 : T sample expression typing rules: G |--z e1 : C int G |--z e2 : C int ------------------------------------------------- G |--z e1 + e2 : C int

38. Sample Typing Rules Judgement Form: G |--z e : T where z ::= C |. recall “zap rule” from operational semantics: ( M, F[ v1 ] ) ---> ( M, F[ v2 ] ) before: |-- v1 : T after: |-- v2 ?? T ==> how will we obtain type preservation?

39. Sample Typing Rules Judgement Form: G |--z e : T where z ::= C |. recall “zap rule” from operational semantics: ---------------------- G |--C C v : C U ( M, F[ v1 ] ) ---> ( M, F[ v2 ] ) before: |-- v1 : C U after: |--C v2 : C U by rule: no conditions “faulty typing” occurs within a single color only.

40. Theorems Theorem 1: Well-typed programs are safe, even when there is a single error. Theorem 2: Well-typed programs executing with a single error simulate the output of well-typed programs with no errors [with a caveat]. Theorem 3: There is a correct, type-preserving translation from the simply-typed lambda calculus into lambda zap [that satisfies the caveat]. ICFP 06

41. The Caveat out [2, 3, 3] bad, but well-typed code: outputs 3 after no faults out [2, 3, 3] outputs 2 after 1 fault out [2, 2, 3] Goal: 0-fault and 1-fault executions should be indistinguishable Solution: computations must independent, but equivalent More importantly: out [2, 3, 3] is obviously a symptom of a compiler bug out [2, 3, 4] is even worse – good runs never come to consensus

42. The Caveat modified typing: G |--z e1 : R U G |--z e2 : G U G |--z e3 : B U G |--z e4 : T G |--z e1 ~~ e2 G |--z e2 ~~ e3 ---------------------------------------------------------------------------- G |-- out [e1, e2, e3]; e4 : T

43. The Caveat let [x1, x2, x3] = e1 in e2 Elimination form: [e1, e2, e3] a collection of 3 items each of 3 stored in separate register single fault effects at most one Introduction form: More generally, programmers may form “triples” of equivalent values

44. The Caveat Elimination form: More generally, programmers may form “triples” of equivalent values Introduction form: G |--z e1 : R U G |--z e2 : G U G |--z e3 : B U G |--z e1 ~~ e2 G |--z e2 ~~ e3 --------------------------------------------- G |-- [e1, e2, e3] : [R U, G U, B U] G |--z e1 : [R U, G U, B U] G, x1:R U, x2:G U, x3:B U, x1 ~ x2, x2 ~ x3 |--z e2 : T --------------------------------------------- G |-- let [x1, x2, x3] = e1 in e2 : T

45. Theorems* Theorem 1: Well-typed programs are safe, even when there is a single error. Theorem 2: Well-typed programs executing with a single error simulate the output of well-typed programs with no errors. Theorem 3: There is a correct, type-preserving translation from the simply-typed lambda calculus into lambda zap. * There is still one “i” to be dotted in the proofs of these theorems. Lester Mackey, brilliant Princeton undergrad, has proven all key theorems modulo the dotted “i”.

46. Step 2: Fault Tolerant Typed Assembly Language (TAL/FT) Lambda zap is playground for studying the principles of fault tolerance in an idealized setting TAL/FT is a more realistic assembly-level, hybrid HW/SW, fault tolerance scheme with (1) a formal fault model (2) a formal definition of fault tolerance relative to memory- mapped I/O (3) a sound type system for proving compiled programs are fault tolerant

47. compiler front end reliability transform ordinary program TAL/FT reliability proof checker optimized TAL/FT modified proof optimization A More Reliable Compiler Architecture types type

48. TAL/FT: Key Ideas (Fault Model) Fault model: registers may incur arbitrary faults in between execution of any two instructions memory (including code) is protected by ECC fault model formalized as part of hardware operational semantics

49. TAL/FT: Key Ideas (Properties) ECC-protected memory Processor Mem-mapped I/O device store read Primary Goal: if there is one fault then either (1)Mem-mapped I/O device sees exactly the same sequence of stores as a fault-free execution, or (2) Hardware detects and signals a fault and mem-mapped I/O sees a prefix of the stores from a fault-free execution Secondary Goal: no false positives

50. TAL/FT: Key Ideas (Mechanisms) Compiler strategy create two redundant computations as lambda zap two copies ==> fault detection fault recovery handled by a higher-level process Hardware support special instructions & modified store buffer for implementing reliable stores special instructions for reliable control-flow transfers Type system Simple typing mechanisms based on original TAL [Morrisett, Walker, et al.] Redundant values with separate colors like in lambda zap Value identities needed for equivalence checking tracked using singleton types combined with some ideas drawn from traditional Hoare logics

51. Current & Future Work Build the first compiler that can automatically generate reliability proofs for compiled programs TAL/FT refinement and implementation type- and reliability-preserving optimizations Study alternative fault detection schemes fault detection & recovery on current hardware exploit multi-core alternatives Understand the foundational theoretical principles that allow programs to tolerate transient faults general purpose program logics for reasoning about faulty programs

52. Other Research I Do PADS [popl 06, sigmod 06 demo, popl 07] automatic generation of tools (parsers, printers, validators, format translators, query engines, etc.) for “ad hoc” data formats with Kathleen Fisher (AT&T) Program Monitoring [popl 00, icfp 03, pldi 05, popl 06,...] semantics, design and implementation of programs that monitor other programs for security (or other purposes) TAL & other type systems [popl 98, popl 99, toplas 99, jfp 02,... ] theory, design and implementation of type systems for compiler target and intermediate languages

53. Conclusions Semi-conductor manufacturers are deeply worried about how to deal with soft faults in future architectures (10+ years out) Using proofs and types I think we are going to be able to develop highly reliable software that runs on unreliable hardware

54. end!

55. The Caveat

56. Function O.S. follows

57. Lambda to Lambda Zap: Control-flow let f = \x.e in f 2 let [f1, f2, f3] = \x. [[ e ]] in [f1, f2, f3] [2, 2, 2] majority vote on control-flow transfer

58. Lambda to Lambda Zap: Control-flow let f = \x.e in f 2 let [f1, f2, f3] = \x. [[ e ]] in [f1, f2, f3] [2, 2, 2] majority vote on control-flow transfer (M; let [f1, f2, f3] = \x.e1 in e2) ---> (M,l=\x.e1; e2[ l / f1][ l / f2][ l / f3]) operational semantics:

59. TAL/FT Hardware store queue replicated program counters ECC-protected Caches/Memory

60. Related Work Follows

61. Software Mitigation Techniques Examples: N-version programming, EDDI, CFCSS [Oh et al. 2002], SWIFT [Reis et al. 2005],... Hybrid hardware-software techniques: Watchdog Processors, CRAFT [Reis et al. 2005],... Pros: immediate deployment would have benefitted Los Alamos Labs, etc... policies may be customized to the environment, application reduced hardware cost Cons: For the same universal policy, slower (but not as much as you’d think).

62. Software Mitigation Techniques Examples: N-version programming, EDDI, CFCSS [Oh et al. 2002], SWIFT [Reis et al. 2005], etc... Hybrid hardware-software techniques: Watchdog Processors, CRAFT [Reis et al. 2005], etc... Pros: immediate deployment: if your system is suffering soft error-related failures, you may deploy new software immediately would have benefitted Los Alamos Labs, etc... policies may be customized to the environment, application reduced hardware cost Cons: For the same universal policy, slower (but not as much as you’d think). IT MIGHT NOT ACTUALLY WORK!

63. Mitigation Techniques Hardware: error-correcting codes redundant hardware Pros: fast for a fixed policy Cons: FT policy decided at hardware design time mistakes cost millions one-size-fits-all policy expensive Software and hybrid schemes: replicate computations Pros: immediate deployment policies customized to environment, application reduced hardware cost Cons: for the same universal policy, slower (but not as much as you’d think).

64. Mitigation Techniques Hardware: error-correcting codes redundant hardware Pros: fast for fixed policy Cons: FT policy decided at hardware design time mistakes cost millions one-size-fits-all policy expensive Software and hybrid schemes: replicate computations Pros: immediate deployment policies customized to environment, application reduced hardware cost Cons: for the same universal policy, slower (but not as much as you’d think). It may not actually work! much research in HW/compilers community completely lacking proof

65. Solutions in Time Solutions in Hardware replication of instructions and checking implemented in special- purpose hardware eg: Reinhardt & Mukherjee 2000 pros: transparent to software cons: one-size-fits-all reliability policy cons: can’t fix existing problem; specialized hardware has reduced market Solutions in Software (or hybrid Hardware/Software) compiler generates replicated instructions and checking code eg: Reis et al. 05 pros: flexibility: new reliability policies may be deployed whenever needed cons: for fixed reliability policy, slower than specialized hardware solutions cons: it might not actually work