1. Parallel Programming in Undergraduate Education: A View from the Ground Dan Grossman University of Washington Workshop on Directions in Multicore Programming Education March 8, 2009

2. Dan Grossman: Undergraduate Parallel Programming2 A few hats 1.Researcher in programming languages –Semantics for transactional memory, etc. 2.Teacher at University of Washington –Freshmen through advanced graduate courses –Added 10% concurrency/parallelism to some courses 3.Current co-chair of department core-curriculum revision –Updating a 20-year-old course structure –Largely juggling 40 brilliant people’s perspectives Today’s opinions, all my own, informed by all –But focus on experience from (3)

3. March 8, 2009Dan Grossman: Undergraduate Parallel Programming3 Trade-offs Do I believe every computer-science undergraduate needs to be prepared for the multicore era? Absolutely. But rather than “preaching to the choir”… What about: Web programming MapReduce Software security Software process UI design Machine learning Embedded systems Technical writing Business Co-op experience(s) Foreign language Shakespeare, Freud, Darwin, … And of course everything we already require?

4. March 8, 2009Dan Grossman: Undergraduate Parallel Programming4 First things first So before what/how/where to teach multicore programming… What does an undergraduate curriculum actually look like? –The outdated one we’re changing –Where we’re probably heading Similar to many of our peers –Where concurrency/parallelism currently appears

5. March 8, 2009Dan Grossman: Undergraduate Parallel Programming5 A few details Each University has particular constraints University of Washington: –Top-ten department at a large public university –Very selective admission to the major –Quarter system: 10-week courses –Community-college transfers 2+2 years (in theory) –Arcane credit-hour rules and such None of this matters… except it does Walk through an undergraduate career…

6. March 8, 2009Dan Grossman: Undergraduate Parallel Programming6 Curriculum overview CS1 Procedural programming in Java 1800+ students a year 88% won’t become CS majors –What should they see Massive logistical infrastructure

7. March 8, 2009Dan Grossman: Undergraduate Parallel Programming7 Curriculum overview CS1 ADTs, lists, trees, recursion, inheritance 900+ students a year 75% won’t become CS majors CS2

8. March 8, 2009Dan Grossman: Undergraduate Parallel Programming8 Curriculum overview CS1 8 required courses –back to this in a minute –what we are trimming / modernizing CS2 “300-level”

9. March 8, 2009Dan Grossman: Undergraduate Parallel Programming9 Curriculum overview CS1  20 choices –students take  5 –  1 per “area” almost no sequences CS2 “300-level” “400-level”

10. March 8, 2009Dan Grossman: Undergraduate Parallel Programming10 Curriculum overview CS1 CS2 “300-level” “400-level” capstone design synthesis experience several options widely loved

11. March 8, 2009Dan Grossman: Undergraduate Parallel Programming11 300-level today (8 required courses)

12. March 8, 2009Dan Grossman: Undergraduate Parallel Programming12 300-level today (8 required courses) discrete structures logic, proofs, sets, combinatorics, probability, …

13. March 8, 2009Dan Grossman: Undergraduate Parallel Programming13 300-level today (8 required courses) discrete structures finite automata, regexps, context-free languages, Turing machines formal models

14. March 8, 2009Dan Grossman: Undergraduate Parallel Programming14 300-level today (8 required courses) discrete structures big-O, balanced trees, heaps, hashing, sorting, graph algorithms, NP-completeness formal models data structures

15. March 8, 2009Dan Grossman: Undergraduate Parallel Programming15 300-level today (8 required courses) discrete structures functional programming, static vs. dynamic typing, modularity, ML/Haskell, Scheme, Ruby formal models data structures programming languages

16. March 8, 2009Dan Grossman: Undergraduate Parallel Programming16 300-level today (8 required courses) discrete structures boolean algebra, gates, binary numbers, finite automata, ALUs formal models data structures programming languages digital design

17. March 8, 2009Dan Grossman: Undergraduate Parallel Programming17 300-level today (8 required courses) discrete structures C, tools, “ethics”, everything else formal models data structures programming languages digital design “303”

18. March 8, 2009Dan Grossman: Undergraduate Parallel Programming18 300-level today (8 required courses) discrete structures assembly programming, CPU design, caching, pipelining formal models data structures programming languages digital design “303” architecture

19. March 8, 2009Dan Grossman: Undergraduate Parallel Programming19 300-level today (8 required courses) discrete structures probability distributions, regression, … formal models data structures programming languages digital design “303” architecturestatistics

20. March 8, 2009Dan Grossman: Undergraduate Parallel Programming20 What’s the problem Too many requirements –Field now too large for everyone to take everything Outdated –In overall focus and details Have innovated within courses, especially parallelism (more later) –Me: 1 week C threads and shared-memory in “303” –Luis Ceze: 1 week in architecture –Note: Digital design inherently parallel too discrete structures formal models data structures programming languages digital design “303” architecturestatistics

21. March 8, 2009Dan Grossman: Undergraduate Parallel Programming21 Where we’re going Revision in progress and highly tentative, but broad strokes are: –Fewer requirements Cannot require the entire “embarrassed list” –Opportunity for earlier specialization –(cf. Berkeley, Cornell, Stanford, Texas, many others) Only touching 300-level for now –Free up room to modernize 400-level later –Little parallelism at 400-level today Synchronization mechanisms in O/S New elective course on MapReduce computing

22. March 8, 2009Dan Grossman: Undergraduate Parallel Programming22 Required core math foundations probability for CS modern software design data structures + ?? hw/sw interface

23. March 8, 2009Dan Grossman: Undergraduate Parallel Programming23 Optional core (50-90%) data mgmt C/C++ programming net-centric computing hardware design ??

24. March 8, 2009Dan Grossman: Undergraduate Parallel Programming24 Where are we Done: A top-down view of a curriculum Now: How might we prepare students for multicore 1.Reorient entire curriculum unlikely, risky 2.Add concurrency and parallelism courses yes, but “we” don’t mean the same thing by that 3.Integrate concurrency and parallelism where relevant my preference some personal experience

25. March 8, 2009Dan Grossman: Undergraduate Parallel Programming25 Being radical I’m not opposed to parallel-from-day-one –After all, I’m a functional programmer at heart –And we can avoid so much unnecessary sequential thinking But I think it’s unrealistic –What can most instructors teach freshmen in 10 weeks? Even though you can do it: “Educational experiments are doomed to succeed” Albert Shanker, former Pres. Amer. Federation of Teachers (Reminds me of “my” programming-languages course.)

26. March 8, 2009Dan Grossman: Undergraduate Parallel Programming26 Senior-level courses Yesteryear: Threads + synchronization mechanisms in O/S –Historical accident: only course that needed it But: “a course on multicore stuff” means too many things –Architecture / very-low-level software –Concurrency* primary challenge: responsiveness to external events Example: O/S –Parallelism* primary challenge: use resources to increase throughput Example: scientific computing * other words we can agree on welcome

27. March 8, 2009Dan Grossman: Undergraduate Parallel Programming27 Some great, very different things …

28. March 8, 2009Dan Grossman: Undergraduate Parallel Programming28 To be clear These are all great and important topics –Not just saying that because you are here –(But probably a dozen more books from people not here) But they’re also really different –Topics, paradigms, emphases, levels of abstraction –Overlap < 30% (?) (probably seems even less to students) Do we mean students should have: –One of these? –All of these? –“My way” –…

29. March 8, 2009Dan Grossman: Undergraduate Parallel Programming29 My preference Add 1-2 weeks of parallelism to an existing core-topic course 1.Data structures / algorithms 2.Introductory architecture* 3.Programming languages 4.Compilers and run-time systems 5.C programming* (or Java, doesn’t matter) Briefly consider each of these, with more details on (5) –Something I’ve done 3 times with 2 nd -year students *Actually done at Washington

30. March 8, 2009Dan Grossman: Undergraduate Parallel Programming30 Data structures / algorithms Amdahl’s Law Span –The notion of dependencies Thread-local versioning (caching)? Basically what Guy B. suggested

31. March 8, 2009Dan Grossman: Undergraduate Parallel Programming31 Architecture Topics mentioned “at least in lecture” Amdahl’s Law Synchronization primitives (CAS, LLSC, etc.) Mutual exclusion Need for cache coherence SIMD

32. March 8, 2009Dan Grossman: Undergraduate Parallel Programming32 Programming languages Threading and synchronization primitives –An interpreter supporting concurrent threads Data races vs. application-level races Data-race freedom Similar to coverage in Michael’s book chapter, but less is okay Note: In graduate courses, I teach Concurrent ML

33. March 8, 2009Dan Grossman: Undergraduate Parallel Programming33 Compilers / run-time systems “Threads Cannot Be Implemented as a Library” [Boehm 05] Why nearly every compiler optimization violates sequential consistency Maybe why concurrent and/or parallel garbage collection is hard Separate/related note: Would like more focus on “how Java is implemented” than on “how is C converted to assembly”

34. March 8, 2009Dan Grossman: Undergraduate Parallel Programming34 C programming For students with 20 weeks of Java + 3 of C, I do this in 2 hours: 1.Race condition on a file via bash scripts 2.Thread basics (multiple call stacks, creation in C and Java) 3.Why use threads? Any one of: – Performance, Failure isolation, Responsiveness, Natural for the algorithm 4.Shared memory; the need to synchronize –Data races vs. higher-level races 5.Fork/join parallelism (in C and Java) 6.Bank-account example –First with atomic blocks (via Eclipse debugger plug-in) –Then with locks (discussing deadlock and false sharing) 7.One slide warning them against sequential consistency –So I’m not lying

35. March 8, 2009Dan Grossman: Undergraduate Parallel Programming35 Why this methodology Some “director’s commentary” on this material Using atomic blocks for pedagogy nicely separates: 1.Determining application’s critical sections (hard!) 2.Finding a good locking protocol (also hard!) I heavily emphasize basic methodology: 1.Immutable data isn’t racy (remember, I’m a functional guy) 2.Thread-local data isn’t racy 3.Rarely acquire a lock while holding one Obviously there is more to do, such as: 1.Communication beyond shared memory 2.Actually achieving speed-up or responsiveness About exposure to the issues, not (yet) about broad competence

36. March 8, 2009Dan Grossman: Undergraduate Parallel Programming36 Conclusions Computing undergoing a multicore revolution –But it’s okay for education to undergo a multicore evolution –Research should be radical and contrarian –Teaching experiments are doomed to succeed Replace 10% of your course with parallelism and/or concurrency –Go ahead and teach parallelism to freshmen if you want to –Also add focused senior-level courses Teach good habits even when assumption is sequential –High-level operations without unnecessary dependencies –Mostly functional Don’t despair: always better to have too much to teach!