Oplægget blev holdt ved et seminar i InfinIT-interessegruppen Højniveau sprog til indlejrede systemer den 11. november 2009. Læs mere om interessegruppen på http://www.infinit.dk/dk/interessegrupper/hoejniveau_sprog_til_indlejrede_systemer/

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
1

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

3. 3
Source: http://langpop.com

4. Source: http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html
4
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
5

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 Sockets, HTTP, email,
SMS, XML, SOAP,
File system
REST, Rails, reliable
Replication, distribution, messaging, AJAX, …
load-balancing, security, 6
concurrency

7. The Hardware world is changing!
7

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
8

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
9

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!
10

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.
11

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

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
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to help her/him

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

15. 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
16

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#
17

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)
18

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
19

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

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

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)
22

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
23

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

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's25 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
26

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
27

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

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

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 
31

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
32

33. Java vs. Clojure
33

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
34

35. 35

36. 36

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
37

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
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
– Optional and Named Parameters
– COM interop features
– (Co-and Contra-variance) 38

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
39

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
40

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
41

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

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

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
44

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!!!
45

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)
46

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!
47

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
48

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.
49

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

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)
51

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
52
• Several Master Student projects already done

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
53

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
http://www.jot.fm/issues/issue_2008_09/article2/index.html
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
54

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.
(http://www.acm.org/pubs/articles/journals/surveys/1996-28-4es/a57-
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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