Download presentation

Presentation is loading. Please wait.

Published byReynaldo Place Modified over 2 years ago

1
MICHAL MOSKAL AND NIKHIL SWAMY RESEARCH IN SOFTWARE ENGINEERING (RISE) MICROSOFT RESEARCH, REDMOND August 8 – 11, 2013 ICFP PROGRAMMING CONTEST

2
ORGANIZE THE CONTEST? WHO, ME?! NO THANKS! That's a shame … because …

3
THE CONTEST IS IN RUDE HEALTH! More than 550 teams registered to participate You have the undivided attention of more than 1000 expert programmers for 72 hours! (mostly) Wow! 72k programmer hours! That's a really valuable resource! Organize the contest? Hell yeah!

4
WHAT QUESTION DO WE WANT 1000 EXPERT PROGRAMMERS TO ANSWER? Traditionally: Which is the best programming language?

5
WHICH IS THE BEST PROGRAMMING LANGUAGE? Boring! The answer is easy:

6
WHICH IS THE BEST PROGRAMMING LANGUAGE? The question is a bit bogus It depends on the programmer Expert programmers can use whatever and do well Even ASM has placed well in past ICFPCs It depends on the task Winning team this year used 6 languages for different sub-tasks

7
WHICH IS THE BEST PROGRAMMING LANGUAGE? Let's not focus so much on this question …

8
QUESTION THIS YEAR: What's up with program synthesis?

9
Can we calibrate research on program synthesis against what an army of crack programmers can do?

10
CALIBRATING PROGRAM SYNTHESIS Synthesis of loop-free programs; Gulwani et al.; PLDI 2011 Uses an SMT solver to synthesize bit-vector programs Scales to 16 instructions in at most 45 minutes Applications to super-optimization etc. Big improvement over prior tools Sketch (2006): Solar Lezama et al., scales to 8 instructions AHA (2002): scales to 6-8 instructions

11
CALIBRATING PROGRAM SYNTHESIS Synthesis of loop-free programs; Gulwani et al.; PLDI 2011 Uses an SMT solver to synthesize bit-vector programs Scales to 16 instructions in at most 45 minutes 16 instructions is quite a lot! SMT solvers are cool! Naïvely, search space = ~10^16 But, is that it?

12
AUGUST 8 – 11, teams wrote tools to synthesize bit-vector programs We evaluated these tools on a set of 1,800 benchmark problems Our main goal: How would the top-teams fare against the best SMT solutions? A (not-so-)secret hope: Some of the best teams would end up using SMT solvers

13
THE PROGRAM SYNTHESIS GAME GAME PLAYER Can you tell me what A(16), A(42), A(128) are? I have a secret program A. Can you guess what it is? You have 5 minutes. A(16)=17, A(42)=43, A(128)=129. Ah. I bet A = λ x. x+1 Let me check … Nope. A(9)=9. Hmm. Ok, so what is A(11) and A(12) then? Since you ask so nicely: A(11)=12 and A(12)=13 Ah ha! I guess A = λ x. if x & 1 = 0 then x else x + 1 Let me check … Yep! That's right! You score one point. query.smt2 A λ x. x+1 ? Yes! No! Counterexample: A(9) <> ( λ x.x+1) 9 query.smt2 A λ x. if x&1=0…?

14
PUNCH LINE THE WINNING TEAMS WERE AMAZING! Main goal: Calibration Winners were synthesizing programs 40 instructions long! Our reference SMT-based solutions maxed out at Recall: the difficulty is exponential in the problem size Secret-hope: SMT usage Many top-10 teams tried SMT, but all opted for hand-tuned, brute force search, with lots of smart pruning heuristics Winning team parallelized the search and used 1000 hours of compute time on Amazon EC2

15
40 VS. 16! WHAT'S UP WITH THAT? Elegant general-purpose formulations in terms of constraint solving: Relatively easy to code up and obtain decent results But, hand-tuned solutions are going to do better … MUCH BETTER If you really want to super-optimize something: Smart search for 1000 hours is cheap!

16
1. NEED TO DECIDE EQUIVALENCE EFFECTIVELY GAME PLAYER Ah ha! I guess A = λ x. if x & 1 = 0 then x else x + 1 query.smt2 A λ x. e ? Yep! That's right! You score one point. Yes! No! Counterexample: A(17) <> ( λ x.e) 17 No dice! A(17)=18.

17
\BV : FUNCTIONS ON 64-BIT VECTORS p ::= λ x.e e ::= 0 | 1 | x | op1 e | e op2 e | if0 e then e else e | fold e e λ x y.e op1 ::= not | shl1 | shr1 | shr4 | shr16 op2 ::= and | or | xor | plus Z3 implements a decidable theory of bit-vectors So, equivalence checking on \BV programs is decidable … But, it's NP-hard and can be quite expensive

18
2. NEED TO SCALE TO MILLIONS OF REQUESTS GAME PLAYER Ah ha! I guess A = λ x. if x & 1 = 0 then x else x + 1 query.smt2 A λ x. e ? Yep! That's right! You score one point. Yes! No! Counterexample: A(17) <> ( λ x.e) 17 No dice! A(17)=18.

19
ELASTIC SCALING ON THE WINDOWS AZURE CLOUD We were set up to run Z3 on up to 128 cores on Azure

20
THROTTLING REQUESTS Each team was assigned an authorization token Tokens were distributed in a pre-registration phase (loud complaints about this!) Token granted a team the ability to make 5 requests/20 seconds Z3 given 20 seconds to decide equivalence, but typically completed in less than 5 seconds

21
PEAK: 40 REQUESTS/SECOND ON 23 CORES

22
Z3 HANDLED A MILLION REQUESTS Z3 received approx. 1 million requests over the weekend Successfully decided all except ~300 in less than 20 seconds (many in just milliseconds) Timeouts did not contribute to score But, scores were adjusted slightly after the end of the competition No team's position changed

23
3. NEED TO GENERATE ~100K PROBLEM INSTANCES GAME PLAYER I have a secret program A. Can you guess what it is? You have 5 minutes.

24
1400 RANDOMLY GENERATED PROBLEMS ASSIGNED TO EACH TEAM Categorized by size and whether or not the program contains fold Totally: 70 categories Low barrier to entry: 300 problems are really easy to solve Increasing difficulty With some cleverness, about 800 could be solved Remaining 300 are super-hard (at least for us)

25
1400 RANDOMLY GENERATED PROBLEMS ASSIGNED TO EACH TEAM Categorized by size and whether or not the program contains fold Totally: 70 categories Contestants needed to balance risk vs. reward A large random program may be semantically equivalent to a small one But, also a bit noisy

26
+400 BONUS PROBLEMS BUILT FROM HARD NUGGETS Exactly the same 400 assigned to all teams Aim to differentiate the best teams Randomly generate 1000s of nuggets {p1, …, pn} each of size 14 Use Z3 to prove that there exists no program of size 12 or less equivalent to any of the nuggets Build larger programs from nuggets: if0 pi then pj else pk

27
WHAT WE USED Z3, F#, TypeScript, JavaScript, TouchDevelop, and Windows Azure are great tools for organizing a programming contest!

28
WINNERS

29
JUDGES' PRIZE: KUMA- Yusuke Endoh and Nayuko Watanabe are an extremely cool bunch of hackers! We were particularly impressed by your compact and elegant Ruby code and are surprised that a scripting language could perform well enough to be competitive at this computationally intensive task. That's great validation for the new generational GC produced by you and other Ruby implementers. Congratulations! RGenGC was developed by Koichi Sasada Awarded $250

30
LIGHTNING DIVISION WINNER: ITF C++ is very suitable for rapid prototyping. Kojiro Izuka, Hiroshi Maeda, Ryosuke Kayanagi University of Tsukuba, Japan Awarded $250

31
3 RD PLACE: HACK THE LOOP C#, C++, bash, awk, sed, and Excel are not too shabby Pavel Egorov, Andrew Kostousov, Alexey Mogilnikov, Sergey Azovskov, Alexey Buslavyev, Kseniya Zhagorina, Denis Dublennyh, Eugeny Klyukin, Maxim Sannikov, Vladislav Isenbaev SKB Kontur, QRGL, Facebook Russian Federation Awarded $250 DECLINED! Our team decided not to claim our prize. We would be glad if our prize will go to the needs of orphans, homeless children, functional programmers in need or other type of charity.

32
2 ND PLACE: F5 ATTACKERS C++ and Python are fine programming tools for many applications Noriyuki Futatsugi, Takashi Nakamura Tai Fukuzawa, Nobuaki Tanaka, Takaaki Hiragushi Fixstars Corporation and University of Tsukuba Japan Awarded $500

33
WINNER: UNAGITHE SYNTHESIS Java, C#, C++, PHP, Ruby, and Haskell are programming tools of choice for discriminating hackers Takuya Akiba, Yoichi Iwata, Kentaro Imajo, Toshiki Kataoka, Naohiro Takahashi, Hiroaki Iwami University of Tokyo, Google, Keio University and AtCoder Japan Awarded $1000 Thanks to SIGPLAN, John Tristan and Greg Morrisett for managing all the issues related to prizes

34
UNAGI'S SOLUTION: SCORE 1696/1800 BRUTE FORCE + PRUNING + MULTIPLE STRATEGIES IN PARALLEL RUNNING IN THE EC2 CLOUD ~(~x)=x ~(if0 x (~y) z) = if0 x y ~z ((x >1)<<1 = x<<1 ((x>>1) >1=x>>1 (x>>4)>>1=(x>>1)>>4 y>>16=0 (where y is a left variable of fold) y&x=x&y x&x=x x&~0=x x&0=0 (y&(x&z))=x&(y&z) ~x&x=0 1&(x<<1)=0 x^~y=~(x^y) if0 constant x y = x (or y) if0 x y y = y if0 x 0 x = x if0 x x y = if0 x 0 y

35
WE AREN'T QUITE DONE WITH THIS YET Lots of data to analyze Many different strategies employed, but many similar ones too Can we reverse engineer/categorize strategies from logs Many other program synthesizers around (including several in RiSE) Tune them up and run them against this problem set

36
LOOKING AHEAD 72K PROGRAMMER-HOURS IS A VALUABLE RESOURCE LET'S MAKE GOOD USE OF IT! WHAT QUESTIONS COULD WE ASK IN THE FUTURE? CROWD-SOURCED PROGRAM DEVELOPMENT/BUG-FINDING? INVARIANT DISCOVERY? SEARCHING FOR INTERPOLANTS? …?

37

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google