Languages and Compilers SProg og Oversttere Lecture 14

Languages and Compilers (SProg og Oversættere) Lecture 14 Concurrency and distribution Bent Thomsen Department of Computer Science Aalborg University With acknowledgement to John Mitchell whose slides this lecture is based on. 1

Concurrency, distributed computing, the Internet • • Traditional view: Let the OS deal with this => It is not a programming language issue! End of Lecture • Wait-a-minute … • Maybe “the traditional view” is getting out of date? 2

Languages with concurrency constructs Maybe the “traditional view” was always out of date? • Simula • Modula 3 • Occam • Concurrent Pascal • ADA • Linda • CML • Facile • Jo-Caml • Java • C# • Fortress • … 3

Categories of Concurrency: 1. Physical concurrency - Multiple independent processors • • • Uni-processor with I/O channels (multi-programming) Multiple CPU (parallel programming) Network of uni- or multi- CPU machines (distributed programming) 2. Logical concurrency - The appearance of physical concurrency is presented by time-sharing one processor (software can be designed as if there were multiple threads of control) • Concurrency as a programming abstraction Def: A thread of control in a program is the sequence of program points reached as control flows through the program 4

Introduction Reasons to Study Concurrency 1. It involves a different way of designing software that can be very useful—many real-world situations involve concurrency – Control programs – Simulations – Client/Servers – Mobile computing – Games 2. Computers capable of physical concurrency are now widely used – High-end servers – Grid computing – Game consoles – Dual Core CPUs, Quad Core … 32 Core in 3 years 5

The Multi-Core Challenge • “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 • “It’s time we rethought some of the basics of computing. It’s scary and lots of fun at the same time. ” – Burton Smith 6

The promise of concurrency • Speed – If a task takes time t on one processor, shouldn’t it take time t/n on n processors? • Availability – If one processor is busy, another may be ready to help • Distribution – Processors in different locations can collaborate to solve a problem or work together • Humans do it so why can’t computers? – Vision, cognition appear to be highly parallel activities 7

Challenges • Concurrent programs are harder to get right – Folklore: Need an order of magnitude speedup (or more) to be worth the effort • Some problems are inherently sequential – Theory – circuit evaluation is P-complete – Practice – many problems need coordination and communication among sub-problems • Specific issues – Communication – send or receive information – Synchronization – wait for another process to act – Atomicity – do not stop in the middle and leave a mess 8

Why is concurrent programming hard? • Nondeterminism – Deterministic: two executions on the same input will always produce the same output – Nondeterministic: two executions on the same input may produce different output • Why does this cause difficulty? – May be many possible executions of one system – Hard to think of all the possibilities – Hard to test program since some cases may occur infrequently 9

Traditional C Library for concurrency System Calls - fork( ) - wait( ) - pipe( ) - write( ) - read( ) Examples 10

Process Creation Fork( ) NAME fork() – create a new process SYNOPSIS # include <sys/types. h> # include <unistd. h> pid_t fork(void) RETURN VALUE success parent- child pid child- 0 failure -1 11

Fork()- program structure #include <sys/types. h> #include <unistd. h> #include <stdio. h> Main() { pid_t pid; if((pid = fork())>0){ /* parent */ } else if ((pid==0){ /*child*/ } else { /* cannot fork* } exit(0); } 12

Wait() system call Wait()- wait for the process whose pid reference is passed to finish executing SYNOPSIS #include<sys/types. h> #include<sys/wait. h> pid_t wait(int *stat)loc) The unsigned decimal integer process ID for which to wait RETURN VALUE success- child pid failure- -1 and errno is set 13

Wait()- program structure #include <sys/types. h> #include <unistd. h> #include <stdlib. h> #include <stdio. h> Main(int argc, char* argv[]) { pid_t child. PID; if((child. PID = fork())==0){ /*child*/ } else { /* parent* wait(0); } exit(0); } 14

Pipe() system call Pipe()- to create a read-write pipe that may later be used to communicate with a process we’ll fork off. SYNOPSIS int pipe(pfd) int pfd[2]; PARAMETER Pfd is an array of 2 integers, which that will be used to save the two file descriptors used to access the pipe RETURN VALUE: 0 – success; -1 – error. 15

Pipe() - structure /* first, define an array to store the two file descriptors*/ int pipes[2]; /* now, create the pipe*/ int rc = pipe (pipes); if(rc = = -1) { /* pipe() failed*/ perror(“pipe”); exit(1); } If the call to pipe() succeeded, a pipe will be created, pipes[0] will contain the number of its read file descriptor, and pipes[1] will contain the number of its write file descriptor. 16

Write() system call write() – used to write data to a file or other object identified by a file descriptor. SYNOPSIS #include <sys/types. h> Size_t write(int fildes, const void * buf, size_t nbyte); PARAMETER fildes is the file descriptor, buf is the base address of area of memory that data is copied from, nbyte is the amount of data to copy RETURN VALUE The return value is the actual amount of data written, if this differs from nbyte then something has gone wrong 17

Read() system call read() – read data from a file or other object identified by a file descriptor SYNOPSIS #include <sys/types. h> Size_t read(int fildes, void *buf, size_t nbyte); ARGUMENT fildes is the file descriptor, buf is the base address of the memory area into which the data is read, nbyte is the maximum amount of data to read. RETURN VALUE The actual amount of data read from the file. The pointer is incremented by the amount of data read. 18

Solaris 2 Synchronization • Implements a variety of locks to support multitasking, multithreading (including real-time threads), and multiprocessing. • Uses adaptive mutexes for efficiency when protecting data from short code segments. • Uses condition variables and readers-writers locks when longer sections of code need access to data. • Uses turnstiles to order the list of threads waiting to acquire either an adaptive mutex or reader-writer lock. 19

Windows 2000 Synchronization • Uses interrupt masks to protect access to global resources on uniprocessor systems. • Uses spinlocks on multiprocessor systems. • Also provides dispatcher objects which may act as wither mutexes and semaphores. • Dispatcher objects may also provide events. An event acts much like a condition variable. 20

Basic question • Maybe the library approach is not such a good idea? • How can programming languages make concurrent and distributed programming easier? 21

Language support for concurrency • Help promote good software engineering • Allowing the programmer to express solutions more closely to the problem domain • No need to juggle several programming models (Hardware, OS, library, …) • Make invariants and intentions more apparent (part of the interface and/or type system) • Allows the compiler much more freedom to choose different implementations • Base the programming language constructs on a wellunderstood formal model => formal reasoning may be less hard and the use of tools may be possible 22

What could languages provide? • Abstract model of system – abstract machine => abstract system • Example high-level constructs – Communication abstractions • Synchronous communication • Buffered asynchronous channels that preserve msg order – Mutual exclusion, atomicity primitives • Most concurrent languages provide some form of locking • Atomicity is more complicated, less commonly provided – Process as the value of an expression • Pass processes to functions • Create processes at the result of function calls 23

Design Issues for Concurrency: 1. 2. 3. 4. 5. 6. How is cooperation synchronization provided? How is competition synchronization provided? How and when do tasks begin and execution? Are tasks statically or dynamically created? Are there any syntactic constructs in the language? Are concurrency construct reflected in the type system? 7. How to generate code for concurrency constructs? 8. How is the run-time system affected? 24

Run-time system for concurrency • Processes versus Threads Fibres Process thread library Operating System Fibres are sometimes called green threads 25

Multithreading in Java: multithreading models Many-to-One model: Green threads in Solaris LWP Kernel Java application (JVM) CPU User space Kernel space (Green threads) 26

Multithreading in Java: multithreading models Many-to-One: Green threads in Solaris • Multiple ULTs to one KLT • Threads library is stored in Java Development Kit (JDK). Thread library is a package of code for user level thread management, i. e. scheduling thread execution and saving thread contexts, etc. . In Solaris threads library is called “green threads”. • Disadvantages : • One thread is blocked, all threads are blocked • Can not run on multiprocessors in parallel 27

Multithreading in Java: multithreading models One-to-One model: in Windows NT LWP Java Application (JVM) User space Kernel space CPU 28

Multithreading in Java: multithreading models One-to-One model: in Windows NT • One ULT to one KLT • Realized by Windows NT threads package. • The kernel maintains context information for the process and for individual thread. • Disadvantage: The time of switching one thread to another thread at kernel level is much longer than at user level. 29

Multithreading in Java: multithreading models Many-to-Many model: Native threads in Solaris LWP CPU Kernel Java Application User space (Native threads) Kernel space 30

Multithreading in Java: multithreading models Many-to-Many model: Native threads in Solaris • Two level model or combined model of ULT and KLT • In Solaris operating system, native threads library can be invoked by setting THREADS_FLAG in JDK to native environment. • A user level threads library (Native threads), provided by JDK, can schedule user-level threads above kernel-level threads. • The kernel only need to manage threads that are currently active. • Solve the problems in two models above 31

Synchronization • Kinds of synchronization: 1. Cooperation – Task A must wait for task B to complete some specific activity before task A can continue its execution e. g. , the producer-consumer problem 2. Competition – When two or more tasks must use some resource that cannot be simultaneously used e. g. , a shared counter – Competition is usually provided by mutually exclusive access (approaches are discussed later) 32

Basic issue: conflict between processes • Critical section – Two processes may access shared resource(s) – Inconsistent behaviour if two actions are interleaved – Allow only one process in critical section • Deadlock – Process may hold some locks while awaiting others – Deadlock occurs when no process can proceed 33

Concurrent Pascal: cobegin/coend • Limited concurrency primitive • Example x : = 0; cobegin x : = 1; x : = x+1 end; begin x : = 2; x : = x+1 end; coend; print(x); x : = 1 execute sequential blocks in parallel x : = x+1 x : = 0 print(x) x : = 2 x : = x+1 Atomicity at level of assignment statement 34

Mutual exclusion • Sample action procedure sign_up(person) begin number : = number + 1; list[number] : = person; end; • Problem with parallel execution cobegin sign_up(fred); sign_up(bill); end; bob bill fred 35

Locks and Waiting <initialze concurrency control> cobegin <wait> sign_up(fred); // critical section <signal> end; begin <wait> sign_up(bill); // critical section <signal> end; Need atomic operations to implement wait end; 36

Mutual exclusion primitives • Atomic test-and-set – Instruction atomically reads and writes some location – Common hardware instruction – Combine with busy-waiting loop to implement mutex • Semaphore – – Avoid busy-waiting loop Keep queue of waiting processes Scheduler has access to semaphore; process sleeps Disable interrupts during semaphore operations • OK since operations are short 37

Monitor Brinch-Hansen, Dahl, Dijkstra, Hoare • Synchronized access to private data. Combines: – private data – set of procedures (methods) – synchronization policy • At most one process may execute a monitor procedure at a time; this process is said to be in the monitor. • If one process is in the monitor, any other process that calls a monitor procedure will be delayed. lock interface • Modern terminology: synchronized object encapsulated state 38

Java Concurrency • Threads – Create process by creating thread object • Communication – Shared variables – Method calls • Mutual exclusion and synchronization – Every object has a lock (inherited from class Object) • synchronized methods and blocks – Synchronization operations (inherited from class Object) • wait : pause current thread until another thread calls notify • notify : wake up waiting threads • notify. All 39

Java Threads • Thread – Set of instructions to be executed one at a time, in a specified order • Java thread objects – Object of class Thread – Methods inherited from Thread: • start : method called to spawn a new thread of control; causes VM to call run method • (suspend : freeze execution) • (interrupt : freeze execution and throw exception to thread) • (stop : forcibly cause thread to halt) – Objects can implement the Runnable interface and be passed to a thread public interface Runnable { public void run(); } 40

Interaction between threads • Shared variables – Two threads may assign/read the same variable – Programmer responsibility • Avoid race conditions by explicit synchronization!! • Method calls – Two threads may call methods on the same object • Synchronization primitives – Each object has internal lock, inherited from Object – Synchronization primitives based on object locking 41

Synchronization example • Objects may have synchronized methods • Can be used for mutual exclusion – Two threads may share an object. – If one calls a synchronized method, this locks the object. – If the other calls a synchronized method on the same object, this thread blocks until the object is unlocked. 42

Synchronized methods • Marked by keyword public synchronized void commit. Transaction(…) {…} • Provides mutual exclusion – At most one synchronized method can be active – Unsynchronized methods can still be called • Programmer must be careful • Not part of method signature – sync method equivalent to unsync method with body consisting of a synchronized block – subclass may replace a synchronized method with unsynchronized method – This problem is known as the inheritance anomaly 43

Aspects of Java Threads • Portable since part of language – Easier to use in basic libraries than C system calls – Example: garbage collector is separate thread • General difficulty combining serial/concur code – Serial to concurrent • Code for serial execution may not work in concurrent sys – Concurrent to serial • Code with synchronization may be inefficient in serial programs (10 -20% unnecessary overhead) • Abstract memory model – Shared variables can be problematic on some implementations – Java 1. 5 has expanded the definition of the memory model 44

C# Threads • Basic thread operations – Any method can run in its own thread, i. e. no need to pass a class implementing a run method – A thread is created by creating a Thread object – The Thread class is sealed – thus no inheritance from it – Creating a thread does not start its concurrent execution; it must be requested through the Start method – A thread can be made to wait for another thread to finish with Join – A thread can be suspended with Sleep – A thread can be terminated with Abort 45

C# Threads • Synchronizing threads – The Interlock class – The lock statement – The Monitor class • Evaluation – An advance over Java threads, e. g. , any method can run its own thread – Thread termination cleaner than in Java – Synchronization is more sophisticated 46

Polyphonic C# • An extension of the C# language with new concurrency constructs • Based on the join calculus – A foundational process calculus like the p-calculus but better suited to asynchronous, distributed systems • A single model which works both for – local concurrency (multiple threads on a single machine) – distributed concurrency (asynchronous messaging over LAN or WAN) • It is different • But it’s also simple – if Mort can do any kind of concurrency, he can do this 47

In one slide: • Objects have both synchronous and asynchronous methods. • Values are passed by ordinary method calls: – If the method is synchronous, the caller blocks until the method returns some result (as usual). – If the method is async, the call completes at once and returns void. • A class defines a collection of chords (synchronization patterns), which define what happens once a particular set of methods has been invoked. One method may appear in several chords. – – When pending method calls match a pattern, its body runs. If there is no match, the invocations are queued up. If there are several matches, an unspecified pattern is selected. If a pattern containing only async methods fires, the body runs in a new thread. 48

Extending C# with chords • Classes can declare methods using generalized chord-declarations instead of method-declarations. chord-declaration : : = method-header [ & method-header ]* body method-header : : = attributes modifiers [return-type | async] name (parms) • Interesting well-formedness conditions: 1. 2. 3. At most one header can have a return type (i. e. be synchronous). Inheritance restriction. “ref” and “out” parameters cannot appear in async headers. 49

A Simple Buffer class Buffer { String get() & async put(String s) { return s; } } • Calls to put() return immediately (but are internally queued if there’s no waiting get()). • Calls to get() block until/unless there’s a matching put() • When there’s a match the body runs, returning the argument of the put() to the caller of get(). • Exactly which pairs of calls are matched up is unspecified. 50

OCCAM • • • Program consists of processes and channels Process is code containing channel operations Channel is a data object All synchronization is via channels Formal foundation based on CSP 51

Channel Operations in OCCAM • Read data item D from channel C – D? C • Write data item Q to channel C – Q!C • If reader accesses channel first, wait for writer, and then both proceed after transfer. • If writer accesses channel first, wait for reader, and both proceed after transfer. 52

Concurrent ML • Threads – New type of entity • Communication – Synchronous channels • Synchronization – Channels – Events • Atomicity – No specific language support 53

Threads • Thread creation – spawn : (unit unit) thread_id • Example code CIO. print "begin parentn"; spawn (fn () => (CIO. print "child 1n"; )); spawn (fn () => (CIO. print "child 2n"; )); CIO. print "end parentn“ • Result child 1 begin parent child 2 end parent 54

Channels • Channel creation – channel : unit ‘a chan • Communication – recv : ‘a chan ‘a – send : ( ‘a chan * ‘a ) unit • Example ch = channel(); spawn (fn()=> … <A> … send(ch, 0); … <B> …); spawn (fn()=> … <C> … recv ch; … <D> …); • Result <A> <C> send/recv <B> <D> 55

CML programming • Functions – Can write functions : channels threads – Build concurrent system by declaring channels and “wiring together” sets of threads • Events – Delayed action that can be used for synchronization – Powerful concept for concurrent programming • Sample Application – e. Xene – concurrent uniprocessor window system 56

Fortress • Fortress STM 57

Fortress Atomic blocks 58

Software Transactional Memory Locks are hard to get right • Programmability vs scalability Transactional memory is appealing alternative • Simpler programming model • Stronger guarantees • Atomicity, Consistency, Isolation • Deadlock avoidance • Closer to programmer intent • Scalable implementations Questions • How to lower TM overheads – particularly in software? • How to balance granularity / scalability? • How to co-exist with other concurrency constructs? 59

Language issues in client/server programming • Communication mechanisms – RPC, Remote Objects, SOAP • Data representation languages – XDR, ASN. 1, XML • Parsing and deparsing between internal and external representation • Stub generation 60

Representation • Data must be represented in a meaningful format. • Methods: – Sender or Receiver makes right (NDR). • Network Data Representation (NDR). • Transmit architecture tag with data. – Represent data in a canonical (or standard) form • XDR • ASN. 1 • Note – these are languages, but traditional DS programmers don’t like programming languages, except C 61

XDR - e. Xternal Data Representation • XDR is a universally used standard from Sun Microsystems used to represent data in a network canonical (standard) form. • A set of conversion functions are used to encode and decode data; for example, xdr_int( ) is used to encode and decode integers. • Conversion functions exist for all standard data types – Integers, chars, arrays, … • For complex structures, RPCGEN can be used to generate conversion routines. 62

RPC Example gcc client date_clnt. c date_xdr. c date. x RPCGEN date. h RPC library -lnsl date_svc. c date_proc. c gcc date_svc 63

XDR Example #include <rpc/xdr. h>. . XDR sptr; // XDR stream pointer xdrs XDR *xdrs; // Pointer to XDR stream pointer char buf[BUFSIZE]; // Buffer to hold XDR data xdrs = (&sptr); xdrmem_create(xdrs, buf, BUFSIZE, XDR_ENCODE); . . int i = 256; xdr_int(xdrs, &i); printf(“position = %d. n”, xdr_getpos(xdrs)); sptr buf 64

Abstract Syntax Notation 1 (ASN. 1) • ASN. 1 is a formal language that has two features: – a notation used in documents that humans read – a compact encoded representation of the same information used in communication protocols. • ASN. 1 uses a tagged message format: – < tag (data type), data length, data value > • Simple Network Management Protocol (SNMP) messages are encoded using ASN. 1. 65

Distributed Objects • CORBA • Java RMI • SOAP and XML 66

CORBA • Common Object Request Broker Architecture • An industry standard developed by OMG to help in distributed programming • A specification for creating and using distributed objects • A tool for enabling multi-language, multi-platform communication • A CORBA based-system is a collection of objects that isolates the requestors of services (clients) from the providers of services (servers) by an encapsulating interface 67

Distributed Objects Proxy and Skeleton in Remote Method Invocation server client object A proxy for B Request skeleton & dispatcher for B’s class remote object B Reply Communication Remote reference module 68

CORBA objects They are different from typical programming objects in three ways: • CORBA objects can run on any platform • CORBA objects can be located anywhere on the network • CORBA objects can be written in any language that has IDL mapping. 69

Client Object Implementation IDL IDL ORB NETWORK A request from a client to an Object implementation within a network 70

IDL (Interface Definition Language) • CORBA objects have to be specified with interfaces (as with RMI) defined in a special definition language IDL. • The IDL defines the types of objects by defining their interfaces and describes interfaces only, not implementations. • From IDL definitions an object implementation tells its clients what operations are available and how they should be invoked. • Some programming languages have IDL mapping (C, C++, Small. Talk, Java, Lisp) 71

IDL Example module katytrail { module weather { struct Weather. Data { float temp; string wind_direction_and_speed; float rain_expected; float humidity; }; typedef sequence<Weather. Data> Weather. Data. Seq interface Weather. Info { Weather. Data get_weather( in string site ); Weather. Data. Seq find_by_temp( in float temperature ); }; 72

IDL Example Cont. interface Weather. Center { register_weather_for_site ( in string site, in Weather. Data site_data ); }; }; }; Both interfaces will have Object Implementations. A different type of Client will talk to each of the interfaces. The Object Implementations can be done in one of two ways. Through Inheritance or through a Tie. 73

IDL File IDL Compiler Client Implementation Client Stub File Server Skeleton File Object Implementation ORB 74

Java RMI • Overview – Supports remote invocation of Java objects – Key: Java Object Serialization Stream objects over the wire – Language specific • History – – Goal: RPC for Java First release in JDK 1. 0. 2, used in Netscape 3. 01 Full support in JDK 1. 1, intended for applets JDK 1. 2 added persistent reference, custom protocols, more support for user control. 75

Java RMI • Advantages – – True object-orientation: Objects as arguments and values Mobile behavior: Returned objects can execute on caller Integrated security Built-in concurrency (through Java threads) • Disadvantages – Java only • Advertises support for non-Java • But this is external to RMI – requires Java on both sides 76

Java RMI Components • Base RMI classes – Extend these to get RMI functionality • Java compiler – javac – Recognizes RMI as integral part of language • Interface compiler – rmic – Generates stubs from class files • RMI Registry – rmiregistry – Directory service • RMI Run-time activation system – rmid – Supports activatable objects that run only on demand 77

RMI Implementation Client Host Server Host Java Virtual Machine Client Object Remote Object Stub Skeleton 78

Java RMI Object Serialization • Java can send object to be invoked at remote site – Allows objects as arguments/results • Mechanism: Object Serialization – Object passed must inherit from serializable – Provides methods to translate object to/from byte stream • Security issues: – Ensure object not tampered with during transmission – Solution: Class-specific serialization Throw it on the programmer 79

Building a Java RMI Application • Define remote interface – Extend java. rmi. Remote • Create server code – Implements interface – Creates security manager, registers with registry • Create client code – Define object as instance of interface – Lookup object in registry – Call object • Compile and run – Run rmic on compiled classes to create stubs – Start registry – Run server then client 80

Java Serialization • • • Writes object as a sequence of bytes Writes it to a Stream Recreates it on the other end Creates a brand new object with the old data Objects can be transmitted using any byte stream (including sockets and TCP). 81

Codebase Property • Stub classpaths can be confusing – 3 VMs, each with its own classpath – Server vs. Registry vs. Client • The RMI class loader always loads stubs from the CLASSPATH first • Next, it tries downloading classes from a web server – (but only if a security manager is in force) • java. rmi. server. codebase specifies which web server 82

CORBA vs. RMI • CORBA was designed for language independence whereas RMI was designed for a single language where objects run in a homogeneous environment • CORBA interfaces are defined in IDL, while RMI interfaces are defined in Java • CORBA objects are not garbage collected because they are language independent and they have to be consistent with languages that do not support garbage collection, on the other hand RMI objects are garbage collected automatically 83

SOAP Introduction • SOAP is simple, light weight and text based protocol • SOAP is XML based protocol (XML encoding) • SOAP is remote procedure call protocol, not object oriented completely • SOAP can be wired with any protocol SOAP is a simple lightweight protocol with minimum set of rules for invoking remote services using XML data representation and HTTP wire. • Main goal of SOAP protocol – Interoperability • SOAP does not specify any advanced distributed services. 84

Why SOAP – What’s wrong with existing distributed technologies • Platform and vendor dependent solutions (DCOM – Windows) (CORBA – ORB vendors) (RMI – Java) • Different data representation schemes (CDR – NDR) • Complex client side deployment • Difficulties with firewall Firewalls allow only specific ports (port 80), but DCOM and CORBA assigns port numbers dynamically. • In short, these distributed technologies do not communicate easily with each other because of lack of standards between them. 85

Base Technologies – HTTP and XML • SOAP uses the existing technologies, invents no new technology. • XML and HTTP are accepted and deployed in all platforms. • Hypertext Transfer Protocol (HTTP) – HTTP is very simple and text-based protocol. – HTTP layers request/response communication over TCP/IP. HTTP supports fixed set of methods like GET, POST. – Client / Server interaction • • • Client requests to open connection to server on default port number Server accepts connection Client sends a request message to the Server process the request Server sends a reply message to the client Connection is closed – HTTP servers are scalable, reliable and easy to administer. • SOAP can bind to any protocol – HTTP , SMTP, FTP 86

Extensible Markup Language (XML) • XML is platform neutral data representation protocol. • HTML combines data and representation, but XML contains just structured data. • XML contains no fixed set of tags, and users can build their own customized tags. <student> <full_name>Bhavin Parikh</full_name> <email>bgp 4@psu. edu</email> </student> • XML is platform and language independent. • XML is text-based and easy to handle and it can be easily extended. 87

Parsing XML Documents • Remember: XML is just text • Simple API for XML (SAX) Parsing – SAX is typically most efficient – No Memory Implementation! • Left to the Developer • Document Object Model (DOM) Parsing – “Parsing” is not fundamental emphasis. – A “DOM Object” is a representation of the XML document in a binary tree format. 88

XML in C# today Programming XML today Imperative model Xml. Document doc = new Xml. Document(); Xml. Element contacts = doc. Create. Element("contacts"); Document foreach (Customer c in customers) centric if (c. Country == "USA") { Xml. Element e = doc. Create. Element("contact"); No integrated Xml. Element name = doc. Create. Element("name"); queries name. Inner. Text = c. Company. Name; e. Append. Child(name); Xml. Element phone = doc. Create. Element("phone"); Memory phone. Inner. Text = c. Phone; intensive e. Append. Child(phone); <contacts> <contact> contacts. Append. Child(e); <name>Great Lakes Food</name> } <phone>(503) 555 -7123</phone> doc. Append. Child(contacts); </contact> … </contacts> 89

LINQ to XML in C# 3. 0 Programming XML with LINQ XElement contacts = new XElement("contacts", from c in customers where c. Country == "USA" select new XElement("contact", new XElement("name", c. Company. Name), new XElement("phone", c. Phone) ) ); Declarative model Element centric Integrated queries Smaller and faster 90

Web-based applications today Presentation: HTML, CSS, Javascript, Flash, Java applets, Active. X controls 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 Replication, distribution, load-balancing, security, concurrency Sockets, HTTP, email, SMS, XML, SOAP, REST, Rails, reliable messaging, AJAX, … 91

Languages for distributed computing • Motivation – Why all the fuss about language and platform independence? • It is extremely inefficient to parse/deparse to/from external/internal representation • 95% of all computers run Windows anyway • There is a JVM for almost any processor you can think of • Few programmers master more than one programming language anyway – Develop a coherent programming model for all aspects of an application 92

Facile Programming Language • Integration of Multiple Paradigms – – – Functions Types/complex data types Concurrency Distribution/soft real-time Dynamic connectivity • Implemented as extension to SML • Syntax for concurrency similar to CML 93

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Facile implementation • Pre-emptive scheduler implemented at the lowest level – Exploiting CPS translation => state characterised by the set of registers • Garbage collector used for linearizing data structures • Lambda level code used as intermediate language when shipping data (including code) in heterogeneous networks • Native representation is shipped when possible – i. e. same architecture and within same trust domain • Possibility to mix between interpretation or JIT depending on usage 95

Conclusion • Concurrency may be an order of magnitude more difficult to handle • Programming language support for concurrency may help make the task easier • Which concurrency constructs to add to the language is still a very active research area • If you add concurrency constructs, be sure you base them on a formal model! 96

The guiding principle Put important features in the language itself, rather than in libraries • Provide better level of abstraction • Make invariants and intentions more apparent – Part of the language syntax – Part of the type system – Part of the interface • Give stronger compile-time guarantees (types) • Enable different implementations and optimizations • Expose structure for other tools to exploit (e. g. static analysis) 97