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

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Presentation on theme: "1 Trends in Programming Technology you might want to keep an eye on Bent Thomsen Department of Computer Science Aalborg University."— Presentation transcript:

1 1 Trends in Programming Technology you might want to keep an eye on Bent Thomsen Department of Computer Science Aalborg University

2 2 Source:

3 3 Source:

4 4 Source: October 2009

5 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 6 Web-based applications today Presentation: HTML, CSS, Javascript, Flash, Java applets, ActiveX controls, Silverlight Business logic: C#, Java, VB, PHP, Perl, Python,Ruby … Database: SQL File system Application server Web server Content management system Operating System Sockets, HTTP, email, SMS, XML, SOAP, REST, Rails, reliable messaging, AJAX, … Replication, distribution, load-balancing, security, concurrency Beans, servlets, CGI, ASP.NET,…

7 7 The Hardware world is changing!

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

13 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 14 Which languages are discussed? Source:

15 15 Source:

16 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 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 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 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 20 Name the language... Func Sort = xs => xs.Case( () => xs, (head,tail) => (Sort(tail.Where(x => x < head))).Concat (Single(head)).Concat (Sort(tail.Where(x => x >= head))) ); Quicksort revisited C# 3.0 type inference append higher-order function parameterized type of functions recursion filter lambda expression

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

22 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 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 24 F# on one slide 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 NOTE: parentheses optional on application NOTE: sequencing NOTE: local binding, sequencing, return NOTE: type inferred val data: int * int * int val sqr: int -> int NOTE: pipelining operator NOTE: pattern matching

25 25 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  "A Conversation With Guy Steele Jr." Dr. Dobb's Journal (04/05) Vol. 30, No. 4, P. 17; Woehr, Jack J. 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. Beyond Java

26 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 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 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 29 Scala is Object Oriented 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. 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) } object instead of static members var: Type instead of Type var Scala’s version of the extended for loop (use <- as an alias for  ) Arrays are indexed args(i) instead of args[i]

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

31 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 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 33 Java vs. Clojure

34 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

35 35

36 36

37 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 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 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 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 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 42 Message Passing/Actors –Erlang –Scala Actors –F#/Axum

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

44 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 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 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 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 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 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 50 So how would you like to programme in 20 years?

51 51 How would you like to program in 20 years? Research project (codename P2025) –Reviewing the state-of-the-art –Experimenting with advanced programming Functional and OO integration Programmatic Program Construction –Developing a new programming language ”The P-gang”: Kurt Nørmark Lone Leth Bent Thomsen Simon Kongshøj (Petur Olsen og Thomas Bøgholm) (Thomas Vestdam)

52 52 Approach Basic Research (Grundforskning) – someone has to do it … We want to influence the next generation of mainstream programming languages Integration playground for new language ideas Constructive: design and implement –languages and compilers –Experimental systems Openness and open source Umbrella covering the research work of the group in coming years –Research, Master Thesis (speciale), DAT7/8 and PhD Several Master Student projects already done

53 53 Bent Thomsen MSc. (Cand. Scient) in CS and maths, AAU, 1981-1987 –Modal transition systems PhD Computing, Imperial College, 1987-1990 –Calculus of Higher Order Concurrent systems European Computer Manufacturers Research Centre, Munich, 1990-1996 –Facile programming language –Calumet teleconferencing –Mobile service Agents ICL (now Fujitsu), 1996-2002 –Agent programming –Mobile Enabling ICL AAU, DPT, 2002-(2025??) –Programming –Compiler construction and language design –Mobile Software Technologies –Super Computer Programming –Hard-real time programming in Java –Indoor location based systems –P2025

54 54 Some of my Background Knowledge Mapping and Visiting in Functional and Object Oriented Programming Kurt Nørmark, Bent Thomsen, and Lone Leth Thomsen JOT: Journal of Object Technology Computational Abstraction Steps Kurt Nørmark, Bent Thomsen, and Lone Leth Thomsen Submitted for publication in JOT: Journal of Object Technology Towards Transactional Memory for Real-Time Systems. Martin Schoberl, Bent Thomsen and Lone Leth Thomsen Technical report. 09-001 Department of Computer Science, AAU, 2009

55 55 Some of my Background Knowledge “Towards Global Computations Guided by Concurrency Theory”, Bent Thomsen and Lone Leth, in Chapter 4, in Current Trends in Theoretical Computer Science, Entering the 21st century, G. Paun, G. Rozenberg and A. Salomaa (Editors), World Scientific Publishing, 2001. “Programming Languages, Analysis Tools and Concurrency Theory”, Bent Thomsen, ACM Computing Surveys 28A(4), December 1996. ( thomsen/a57-thomsen.html) “FACILE - from Toy to Tool”, Bent Thomsen, Lone Leth and Tsung-Min Kuo, Chapter 5 in ML with Concurrency: Design, Analysis, Implementation, and Application, Flemming Nielson (Editor), Springer- Verlag, 1996.

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