Trends in Programming Technology you might want to keep an eye on

1. Trends in Programming Technology you might want to keep an eye on  Bent Thomsen bt@cs.aau.dk Department of Computer Science Aalborg University

2. Source: http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html

3. Source: http://langpop.com

4. Source: http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html October 2009

5. Conclusions • Nothing has changed much • Main languages have their domain • Java – for web applications • C – for system programming • (Visual) Basic – for desktop windows apps • PHP for serverside scripting • C++ when Java is (perceived) too slow • We can go home now • Wait a minute! • Something is changing • Software is getting more and more complex • Hardware has changed

6. Web-based applications today Presentation: HTML, CSS, Javascript, Flash, Java applets, ActiveX controls, Silverlight Application server Web server Content management system Business logic: C#, Java, VB, PHP, Perl, Python,Ruby … Beans, servlets, CGI, ASP.NET,… Operating System Database: SQL File system Sockets, HTTP, email, SMS, XML, SOAP, REST, Rails, reliable messaging, AJAX, … Replication, distribution, load-balancing, security, concurrency

7. The Hardware world is changing!

8. Moore’s Law • Popular belief: • Moore’s Law stopped working in 2005! • Moore’s Law (misinterpreted): • The processor speed doubles every 18 months • Moore’s Law still going strong • the number of transistors per unit area on a chip doubles every 18 months • Instead of using more and more HW real-estate on cache memory it is now used for multiple cores

9. The IT industry wakeup call • The super computing community discovered the change in hardware first • The rest of the computing industry are in for an eye-opener soon! • Some have started to worry “Multicore: This is the one which will have the biggest impact on us. We have never had a problem to solve like this. A breakthrough is needed in how applications are done on multicore devices.” – Bill Gates

10. A programmer’s view of memory This model was pretty accurate in 1985. Processors (386, ARM, MIPS, SPARC) all ran at 1–10MHz clock speed and could access external memory in 1 cycle; and most instructions took 1 cycle. Indeed the C language was as expressively time-accurate as a language could be: almost all C operators took one or two cycles. But this model is no longer accurate!

11. A modern view of memory timings So what happened? On-chip computation (clock-speed) sped up faster (1985–2005) than off-chip communication (with memory) as feature sizes shrank. The gap was filled by spending transistor budget on caches which (statistically) filled the mismatch until 2005 or so. Techniques like caches, deep pipelining with bypasses, and superscalar instruction issue burned power to preserve our illusions. 2005 or so was crunch point as faster, hotter, single-CPU Pentiums were scrapped. These techniques had delayed the inevitable.

12. The Current Mainstream Processor Will scale to 2, 4 maybe 8 processors. But ultimately shared memory becomes the bottleneck (1024 processors?!?).

13. Programming model(s) reflecting the new world are called for • Algorithm should do most work on local data !! • Programmers need to • know what is local and what is not • need to deal with communication • make decisions on parallel execution • But how can the poor programmer ensure this? • She/he has to exploit: • Data Parallelism • Task parallelism • She/he needs programming language constructs to help her/him

14. Which languages are discussed? Source: http://langpop.com

15. Source: http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html

16. Three Trends • Declarative programming languages in vogue again • Especially functional • Dynamic Programming languages are gaining momentum • Concurrent Programming languages are back on the agenda

17. Declarative Programming • Lots of talk about declarative languages: • Haskell • Scheme, Lisp, Clojure • F#, O’Caml, SML • Scala, Fortress • Lots of talk about declarative constructs in traditional languages • C#

18. What do we mean by declarative/functional? • Say what you want, without saying how • Not quite true – more a question of saying how implicitly • Functions as first class entities • Lazy or(/and) eager evaluation • Pure vs. impure • Value oriented (vs. state oriented) • Pattern matching • Generics (or parametric polymorphism)

19. Mainstream programming is going declarative Four years ago Anders Heilsberg (designer of C#) said: ``Generally speaking, it's interesting to think about more declarative styles of programming vs. imperative styles. ... Functional programming languages and queries are actually a more declarative style of programming''. ``programmers have to unlearn .. and to learn to trust that when they're just stating the ``what'' The machine is smart enough to do the ``how'' the way they want it done, or the most efficient way''. - Anders Hejlsberg

20. Name the language... C# 3.0 parameterized type of functions Func<intlist, intlist> Sort = xs => xs.Case( () => xs, (head,tail) => (Sort(tail.Where(x => x < head))) .Concat (Single(head)) .Concat (Sort(tail.Where(x => x >= head))) ); higher-order function lambda expression append type inference filter recursion Quicksort revisited

21. C# 3.0 Language Extensions Query expressions var contacts = from c in customers where c.State == "WA" select new { c.Name, c.Phone }; Local variable type inference Lambda expressions var contacts = customers .Where(c => c.State == "WA") .Select(c => new { c.Name, c.Phone }); Extension methods Anonymous types Object initializers

22. C# 3.0 Features • Implicitly Typed Local Variables • Lambda Expressions • Anonymous Types • Expression Trees • Query Expressions • Extension Methods • Object Initializers • Collection Initializers • Iterators • Lazy streams • Nullable value types • C# 2.0 already have: • Generics • Structured Value Types • First class anonymous functions (called delegates)

23. F# • A .NET language (developed by Don Syme) • Connects with all Microsoft foundation technologies • 3rd official MS language shipped with VS2010 • Aims to combine the best of Lisp, ML, Scheme, Haskell, in the context of .NET • Actually based on O’Caml • Functional, math-oriented, scalable • Aimed particularly at the "Symbolic Programming" niche at Microsoft

24. F# on one slide F# on one slide NOTE: type inferred val data: int * int * int • let data = (1,2,3) • let sqr x = x * x • let f (x,y,z) = (sqr x, sqr y, sqr z) • let sx,sy,sz = f (10,20,30) • print "hello world"; 1+2 • let show x y z = • printf "x = %d y = %d y = %d \n" x y z; • let sqrs= f (x,y,z) in • print "Hello world\n"; • sqrs • let (|>) x f = f x val sqr: int -> int NOTE: parentheses optional on application NOTE: pattern matching NOTE: sequencing NOTE: local binding, sequencing, return NOTE: pipelining operator

25. Beyond Java • "A Conversation With Guy Steele Jr."Dr. Dobb's Journal (04/05) Vol. 30, No. 4, P. 17; Woehr, Jack J. Guy Steele theorizes that programming languages are finite, and argues that the time is right for a successor to Java, which has another two decades of life left. Sun is investigating whether aligning programming languages more closely to traditional mathematical notation can reduce the burden for scientific programmers Guy Steele co-wrote the original Java specifications and in 1996 was awarded the ACM SIGPLAN Programming Language Achievement Award. Steele is a distinguished engineer and principal investigator at Sun Microsystems Laboratories, where he heads the company's Programming Language Research Group.

26. Fortress • One of the three languages DARPA spent 1BN$ on • Actually SUN only got 49.7M$ (IBM and CRAY got the rest) • First class higher order functions • Type inference • immutable and mutable variables • Traits • Like Java interfaces with code, classes without fields • Objects • Consist of fields and methods • Designed to be parallel unless explicit sequential • For loops and generators, tuples • Transactional Memory • PGAS (Partitioned Global Address Space) • Runs on top of the JVM

27. “Advances” in Syntax • Extensible syntax – follows Guy Stell’s vision of “Growing a language” • The only language I know with overloadable whitespace! • Syntax based on Parsing Expression Grammars (PEG) • Syntax resembling mathematical notation

28. Scala • Scala is an object-oriented and functional language which is completely interoperable with Java • Developed by Martin Odersky, EPFL, Lausanne, Switzerland • Uniform object model • Everything is an object • Class based, single inheritance • Mixins and traits • Singleton objects defined directly • Higher Order and Anonymous functions with Pattern matching • Genericity • Extendible • All operators are overloadable, function symbols can be pre-, post- or infix • new control structures can be defined without using macros

29. Scala programs interoperate seamlessly with Java class libraries: Method calls Field accesses Class inheritance Interface implementation all work as in Java. Scala programs compile to JVM bytecodes.Scala’s syntax resembles Java’s, but there are also some differences. Scala is Object Oriented object instead of static members var: Type instead of Type var object Example1 { def main(args: Array[String]) { val b = new StringBuilder() for (i  0 until args.length) { if (i > 0) b.append(" ") b.append(args(i).toUpperCase) } Console.println(b.toString) } } Scala’s version of the extendedfor loop(use <- as an alias for ) Arrays are indexed args(i) instead of args[i]

30. The last program can also be written in a completelydifferent style: Treat arrays as instances of general sequence abstractions. Use higher-orderfunctions instead of loops. Scala is functional Arrays are instances of sequences with map and mkString methods. map is a method of Array which applies the function on its right to each array element. object Example2 { def main(args: Array[String]) { println(args map (_.toUpperCase) mkString " ") } } A closure which applies the toUpperCase method to its String argument mkString is a method of Array which forms a string of all elements with a given separator between them.

31. Scala’s approach • Scala applies Tennent’s design principles: • concentrate on abstraction and composition capabilities instead of basic language constructs • Minimal orthogonal set of core language constructs • But it is European 

32. Clojure • Concurrent Lisp like language on JVM • Developed by Rich Hickey • Everything is an expression, except: • Symbols • Operations (op ...) • Special operations: • def if fn let loop recur do new . throw try set! quote var • Code is expressed in data structures • Functions are first-class values • Clojure is homoiconic

33. Java vs. Clojure

34. Dynamic Programming • Lots of talk about dynamic languages • PhP, Perl, Ruby • JavaScript • Lisp/Scheme • Erlang • Groovy • Clojure • Python • jPython for JVM and IronPyhon for .Net • Real-programmers don’t need types

37. Dynamic Language characteristics • (Perceived) to be less verbose • Comes with good libraries/frameworks • Interpreted or JIT to bytecode • Eval: string -> code • REPL style programming • Embeddable in larger applications as scripting language • Supports Higher Order Function! • Object oriented • JavaScript, Ruby and Python • Based on Self resp. SmallTalk • Meta Programming made easier

38. Dynamic Programming in C# 4.0 • Dynamic Lookup • A new static type called: dynamic • No static typing of operations with dynamic • Exceptions on invalid usage at runtime • Optional and Named Parameters • COM interop features • (Co-and Contra-variance) dynamic d = GetDynamicObject(…); d.M(7); // calling methods d.f= d.P; // getting and settings fields and properties d[“one”] = d[“two”]; // getting and setting thorughindexers Int i= d + 3; // calling operators string s = d(5,7); // invoking as a delegate

39. Concurrent Programming • Lots of talk about Erlang • Fortress, X10 and Chapel • Java.util.concurrency • Actors in Scala • Clojure • C omega • F# - Accelerator on GPU • .Net Parallel Extensions

40. The problem with Threads • Threads • Program counter • Own stack • Shared Memory • Create, start (stop), yield .. • Locks • Wait, notify, notifyall • manually lock and unlock • or implicit via synchronized • lock ordering is a big problem • Not compositional

41. Several directions • (Software) Transactional Memory • Enclose code in begin/end blocks or atomic blocks • Variations • specify manual abort/retry • specify an alternate path (way of controlling manual abort) • Java STM2 library • Clojure, Fortress, X10, Chapel

42. Erlang Scala Actors F#/Axum Message Passing/Actors

43. Theoretical Models • Actors • CSP • CCS • pi-calculus • join-calculus • All tried and tested in many languages over the years, but …

44. Problems with Actor like models • Actors (Agents, Process or Threads) are not free • Message sending is not free • Context switching is not free • Still need Lock acquire/release at some level and it is not free • Multiple actor coordination • reinvent transactions? • Actors can still deadlock and starve • Programmer defines granularity by choosing what is an actor

45. Other concurrency models • Dataflow • Stream Processing Functions • Futures • Tuple Spaces • Stop gap solutions based on parallelised libraries • Lots of R&D (again) in this area!!!

46. Other trends worth watching • Development methods • Away from waterfall, top-down • Towards agile/XP/Scrum • Refactoring • Frameworks, Patterns • test-driven-development • Tools • Powerful IDEs with plug-ins • Frameworks • VM and OS integrations • MS PowerShell, v8 in Android • Programming Language construction is becoming easier • Extendible Open (source) Compilers for most mainstream languages • AST (or expression trees in C#/F# and Fortress) • Generic code generators • Parsing Expression Grammars (PEG)

47. Implications for real-time • New ways of programming is back on the agenda • But .. • C as popular as ever!! • ADA is still around • No. 10 in list of languages talked about • No. 20 on skills in jobs advertised • Java still most popularity (albeit declining a little) • The only modern language serious about hard-real time!

48. What about all the new declarative and dynamic stuff? • Higher Order Programming • Elegant programming styles • Harder to analyse control flow • Usually imply use of GC • Dynamic Programming • Lots of run-time checks • Harder to analyse type violations • Frameworks written for average-time performance, not worst case analysis • VM technology is improving a lot • But special VMs are needed for RT

49. Promises for real-time • New ways of programming is back on the agenda • Understanding of HW has (again) become necessary • Semantics is back on the agenda • SOS/Calculi for Fortress, Scala, F# • Advanced type systems and type inference • Program Analysis and verification • JML and SPEC# (Design by contract) • SPIN, Blast, UPPAAL • ProVerif (Microsoft) • JavaPathfinder (NASA, Fujitsu) • WALA (IBM) osv.

50. So how would you like to programme in 20 years?