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[Updated Apr 3 to note automatic deduction of return type.]

The ISO C++ committee met in Bellevue, WA, USA on February 24 to Mar 1. Here’s a quick summary of what we did (with links to the relevant papers to read for more details), and information about upcoming meetings.

Lambda functions and closures (N2550)

For me, easily the biggest news of the meeting was that we voted lambda functions and closures into C++0x. I think this will make STL algorithms an order of magnitude more usable, and it will be a great boon to concurrent code where it's important to be able to conveniently pass around a piece of code like an object, to be invoked wherever the program sees fit (e.g., on a worker thread).

C++ has always supported this via function objects, and lambdas/closures are merely syntactic sugar for writing function object. But, though "merely" a convenience, they are an incredibly powerful convenience for many reasons, including that they can be written right at the point of use instead of somewhere far away.

Example: Write collection to console

For example, let's say you want to write each of a collection of Widgets to the console.

// Writing a collection to cout, in today's C++, option 1:

for( vector<Widget>::iterator i = w.begin(); i != w.end(); ++i )
  cout << *i << " ";

Or we can leverage that C++ already has a special-purpose ostream_iterator type that does what we want:

// Writing a collection to cout, in today's C++, option 2:

copy( w.begin(), w.end(),
          ostream_iterator<const Widget>( cout, " " ) );

In C++0x, just use a lambda that writes the right function object on the fly:

// Writing a collection to cout, in C++0x:

for_each( w.begin(), w.end(),
                []( const Widget& w ) { cout << w << " "; } );

(Usability note: The lambda version was the only one I wrote correctly the first time as I tried these examples on compilers to check them. 'Nuff said. <tease type="shameless"> Yes, that means I tried it on a compiler. No, I'm not making any product feature announcements about VC++ version 10. At least not right now. </tease>)

Example: Find element with Weight() > 100

For another example, let's say you want to find an element of a collection of Widgets whose weight is greater than 100. Here's what you might write today:

// Calling find_if using a functor, in today's C++:

// outside the function, at namespace scope
class GreaterThan {
  int weight;
public:
  GreaterThan( int weight_ )
    : weight(weight_) { }
  bool operator()( const Widget& w ) {
    return w.Weight() > weight;
  }
};

// at point of use
find_if( w.begin(), w.end(), GreaterThan(100) );

At this point some people will point out that (a) we have C++98 standard binder helpers like bind2nd or (b) that we have Boost's bind and lambda libraries. They don't really help much here, at least not if you're interested in having the code be readable and maintainable. If you doubt, try and see.

In C++0x, you can just write:

// Calling find_if using a lambda, in C++0x:

find_if( w.begin(), w.end(),
             []( Widget& w ) { return w.Weight() > 100; } );

Ah. Much better.

Most algorithms are loops... hmm...

In fact, every loop-like algorithm is now usable as a loop. Quick examples using std::for_each and std::transform:

for_each( v.begin(), v.end(), []( Widget& w )
{
  ...
  ... use or modify w ...
  ...
} );

transform( v.begin(), v.end(), output.begin(), []( Widget& w )
{
  ...
  return SomeResultCalculatedFrom( w );
} );

Hmm. Who knows: As C++0x lambdas start to be supported in upcoming compilers, we may start getting more used to seeing "});" as the end of a loop body.

Concurrency teaser

Finally, want to pass a piece of code to be executed on a thread pool without tediously having to define a functor class out at namespace scope? Do it directly:

// Passing work to a thread pool, in C++0x:

mypool.run( [] { cout << "Hello there (from the pool)"; } );

Gnarly.

Other approved features

• N2535 Namespace associations (inline namespace)
• N2540 Inheriting constructors
• N2541 New function declarator syntax
• N2543 STL singly linked lists (forward_list)
• N2544 Unrestricted unions
• N2546 Removal of auto as a storage-class specifier
• N2551 Variadic template versions of std::min, std::max, and std::minmax 
• N2554 Scoped allocator model
• N2525 Allocator-specific swap and move behavior
• N2547 Allow lock-free atomic<T> in signal handlers
• N2555 Extended variadic template template parameters
• N2559 Nesting exceptions (aka wrapped exceptions)

Next Meetings

Here are the next meetings of the ISO C++ standards committee, with links to meeting information where available.

• June 8-14, 2008: Sophia Antipolis, France
• September 14-20, 2008: San Francisco Bay area, California, USA

The meetings are public, and if you're in the area please feel free to drop by.

I recently spent an hour on the phone to talk concurrency with DevX's Alexa Weber Morales. Part 1 of that interview just went live on the web, and focuses mostly on what concurrency and parallelism are, how to take advantage of multicore chips, and whether concurrency will ever be really accessible to mainstream developers. The site seems to be having intermittent problems displaying the pages; just hit the link a few more times if it doesn't work right away.

Disclaimer: I am not responsible for the article title (yikes! "whisperer"?! my goodness gracious!) and the intro blurb vastly overstates my role (Alexa is being way too kind). But it is true that it's important for us-the-industry to bring concurrency to the mainstream in a grokkable way, as we have already successfully done with OO and GUIs in the past.

Many of you have kindly sent mail about my Effective Concurrency columns and asking when there'll be a course. Well, I'm happy to announce that the answer is: May 19-21, 2008.

Here's the brief information (more details below):

3-Day Seminar: Effective Concurrency

May 19-21, 2008
Bellevue, WA, USA
Developed and taught by Herb Sutter

This course covers the fundamental tools that software developers need to write effective concurrent software for both single-core and multi-core/many-core machines. To use concurrency effectively, we must identify and solve four key challenges:

• Leverage the ability to perform and manage work asynchronously
• Build applications that naturally run faster on new hardware having more and more cores
• Manage shared objects in memory effectively to avoid races and deadlocks
• Engineer specifically for high performance

This seminar will equip attendees to reason correctly about concurrency requirements and tradeoffs, to migrate existing code bases to be concurrency-enabled, and to achieve key success factors for a concurrent programming project. Most code examples in the course can be directly translated to popular platforms and concurrency libraries, including Linux, Windows, Java, .NET, pthreads, and the forthcoming ISO C++0x standard.

Note on class size limit and possible waitlist: There is a hard limit on attendance at this first one (really). But if the registration site says you'll get waitlisted, don't give up: Go ahead and sign up anyway because we may be able to put together a second installment of the seminar a week or two later if there's enough interest.

Finally, here's a summary of what we'll cover during the three days.

Fundamentals

• Define basic concurrency goals and requirements
• Understand applications' scalability needs
• Key concurrency patterns

Isolation: Keep Work Separate

• Running tasks in isolation and communicate via async messages
• Integrating multiple messaging systems, including GUIs and sockets
• Building responsive applications using background workers
• Threads vs. thread pools

Scalability: Re-enable the Free Lunch

• When and how to use more cores 
• Exploiting parallelism in algorithms 
• Exploiting parallelism in data structures 
• Breaking the scalability barrier

Consistency: Don’t Corrupt Shared State

• The many pitfalls of locks--deadlock, convoys, etc.
• Locking best practices
• Reducing the need for locking shared data
• Safe lock-free coding patterns
• Avoiding the pitfalls of general lock-free coding
• Races and race-related effects

Migrating Existing Code Bases to Use Concurrency

Near-Future Tools and Features

High Performance Concurrency

• Machine architecture and concurrency
• Costs of fundamental operations, including locks, context switches, and system calls
• Memory and cache effects
• Data structures that support and undermine concurrency
• Enabling linear and superlinear scaling

I hope to get to meet some of you here in the Seattle area!

There are two main ways to achieve superlinear scalability, or to use P processors to compute an answer more than P times faster...:

• Do disproportionately less work.
• Harness disproportionately more resources.

Last month, we focused on the first point by illustrating parallel search and how it naturally achieves superlinear speedups when matches are not distributed evenly because some workers get "rich" subranges and will find a match faster, which benefits the whole search because we can stop as soon as any worker finds a match.

This month, we'll conclude examining the first point with a few more examples, and then consider how to achieve superlinear speedups by harnessing more resources—quite literally, running on a bigger machine without any change in the hardware. ...