Concurrency in C++

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# Agenda: Modern C++

Focussing

* Concurrency in C++

* by: [Dipl.-Ing. Martin Weitzel](http://tbfe.de)

* for: [MicroConsult GmbH Munich](http://microconsult.com)

[Creative Commons BY-SA]: https://creativecommons.org/licenses/by-sa/4.0/

---
template: plain
name: cpp11_concurrency_basics
header: ## C++11: Concurrency Basics

With C++11 support for multi-threading was introduced.._[]

---------------------------------------------------------------------------

* [Parallelizing Independent Tasks	](#parallelizing_tasks)
* [Synchronisation with Mutexes		](#mutex_synchronisation)
* [One-Time Execution			](#onetime_execution)
* [Messaging with Condition Variables	](#condition_variables)
* [Atomic Operations			](#atomic_operations)
* [Direct Use of Threads		](#direct_use_of_threads)
* [Native Threading Model Handles	](#native_thread_handles)
* [Concurrency Recommendations		](#concurrency_recommendations)

---------------------------------------------------------------------------

This part of the presentation was written with the intent to give an overview
of the provided features only, it is by far not exhaustive!

.F[:
Concurrency features in C++11 are more as "just some more library classes and
functions". Especially with regard to optimisations and how to provide
[Cache Coherence] on modern CPUs it is closely intertwined with code
generation. For more information see:  
http://en.cppreference.com/w/cpp/language/memory_model and  
http://en.cppreference.com/w/cpp/language/memory_order
]

[Cache Coherence]: http://en.wikipedia.org/wiki/Cache_coherence

---
template: plain
name: parallelizing_tasks
header: ### Parallelizing Independent Tasks

For complex tasks that can be split into independent parts, concurrency **and
scalability to multiple cores** can be easily achieved by following a simple
recipe:

1. Separate the task into a number of different functions (or calls of the same
   function with different arguments).
2. Run each such function by handing it over to `std::async`, storing the
  `future` that is returned (easiest in an `auto`-typed variable).
3. Fetch (and combine) the results by calling the member function `get` for
   each `future`.

That way all the functions may run concurrently and the last step synchronizes
by waiting for completion.

.I[
For more information on parallelizing independent task that way see:
http://en.cppreference.com/w/cpp/thread/async
]

.F[:
Technically the return value of `std::async` is an `std::future` but as this
is a template and the type is usually somewhat different to spell out, most
usages of `std::async` store the result in an `auto`-typed variable.
]

---
template: plain
name: futures_and_promises
header: #### Foundation: Futures and Promises

The foundation on which parallelizing tasks is build are [Futures and Promises].

These need not be fully understood to apply the API (exemplified on the next
pages), but may help to understand the basic machinery:

* A future is the concrete handle which a client can use to fetch the result,
  presumably made available by a different thread of execution.

* A promise is a helper class which may be used in a separate thread to make
  a result available for a client.

.I[
For more information on promises and futures see:
http://en.cppreference.com/w/cpp/thread/future
http://en.cppreference.com/w/cpp/thread/promise
http://en.cppreference.com/w/cpp/thread/packaged_task
]

[Futures and Promises]: http://en.wikipedia.org/wiki/Futures_and_promises

---
template: plain
name: parallelizing_example
header: #### Parallelizing Example

The following example calculates the sum of the first `N` elements in `data`
by splitting the work of `std::accumulate`, into two separate function calls,
that may run concurrently:._[]
```
// calculate sum of first N values in data
//
long long sum(const int data[], std::size_t N) {
    auto lower_part_sum = std::async(
        [=]{ return std::accumulate(&data[0], &data[N/2], 0LL); }
    );
    auto upper_part_sum = std::async(
        [=]{ return std::accumulate(&data[N/2], &data[N], 0LL); }
    );
    return lower_part_sum.get()
         + upper_part_sum.get();
}
```

.F[:
Note the use of lambdas above – the actual call to `std::accumulate` will only
happen when the lambda gets executed! A similar effect can be achieved as
follows:
```
  // preparing the callable for std::async with std::bind:
… std::async(std::bind(std::accumulate, &data[0], &data[N/2], 0LL)) …
… std::async(std::bind(std::accumulate, &data[N/2], &data[N], 0LL)) …
```
]

---
template: plain
header: #### Default Launch Policy

The default behavior is that the systems decides on its own whether an
asynchronously started task is run concurrently.._[]

Using `std::async` **without an explicit launch policy**

* **is just a hint** that it is acceptable to run a callable unit of code
  concurrently,

* as long as it has finished (and possibly returned a result) latest when
  the `get`-call returns, which has bee invoked on the `std::future`.

.W[
Be sure **not** to use the default launch policy but specify concurrent
execution explicitly (see next page) whenever it is essential that two
callable run concurrently.
]

.F[:
It is a well known effect that too many parallel threads of execution may rather
degrade performance. Especially if threads are CPU bound, it makes little sense
to have more threads as cores. Therefore the standard gives considerable freedom
to the implementation, which might implement concurrency with `std::async` with
a thread pool and turn to synchronous execution or lazy evaluation at a certain
threshold.
]

---
template: plain
header: #### Explicit Launch Policies

There is a second version of `std::async` which has a first argument to specify
the launch strategy.

The standard defines two values:

* `std::launch::async`  
  if **not set** the callable will **not** run on its own thread;._[]

* `std::launch::deferred`
  of **set** the callable will **not** be called before `get` is invoked.

.I[
For more information on launch policies see:
http://en.cppreference.com/w/cpp/thread/launch
]

.F[:
One case in which this setting this flag may make sense is to test the program
logic independent from possible problems created through race conditions or
deadlocks, originating from dependencies between the "independent tasks", that
had been overlooked.
]

---
template: plain
header: #### Catching Exceptions

If the callable started via `std::async` throws an exception, it will appear as
if it were thrown from the call to get.

Hence, if the asynchronously run task may throw, fetching the result should be
done in a try block:
```
auto task1 = std::async( … ); // whatever-is-to-do (and may throw)
auto task2 = std::async( … ); // whatelse-is-to-do (and may throw)
…
try { … task1.get() … }
} catch ( … ) { // what may be thrown from whatever-is-to-do
   … // handle the case that whatever-is-to-do threw
}
try { … task2.get() … }
} catch ( … ) { // what may be thrown from whatelse-is-to-do
   … // handle the case that whatelse-is-to-do threw
}
```

---
template: plain
header: #### Communication between Independent Tasks

First of all: If the need arises to communicate between independent tasks, this
should be taken **as a strong warning** that such tasks are actually not
independent.

.W[
If parallel tasks are not independent, further needs follow quickly with respect
to synchronize access to shared data … **with all the further intricacies following
from this**.._[]
]

Nevertheless there is one common case that requires a simple form of
communication between otherwise independent tasks.

.N[

* If there are several tasks working towards a common goal
* of which one fails, making the goal unattainable,
* the others should not waste CPU-time needlessly.

]

.F[:
In other words: Pandora's proverbial can of worms opens quickly and widely …
]

---
template: plain
header: #### Communicate Failure between Concurrent Tasks

A basic design that communicates failure between partners working towards a
common goal is outlined in the following example.._[]
.pull-left[
The workers could look about so …
```
void foo ( … , bool &die) {
    …
    for ( … ) {
        if (die) return;
        …
        … // some complex
        … // algorithm
        …
        // may fail here
        if ( … ) {
            die = true;
            return;
        }
    }
}
```
]
.pull-right[
… being run by that code:
```
bool die = false;
auto task1 = std::async(
    [&die]{ foo( … , die); }
);
auto task2 = std::async(
    [&die]{ foo( … , die); }
);
…
… task1.get() …
… task2.get() …
…
if (die) {
    // goal not reached
    …
}
```
]

.F[:
To keep this code simple it just returns in case of problems, though it requires
only a few changes if problems should be communicated to the caller via exceptions.
]

---
template: plain
name: mutex_synchronisation
header: ### Synchronisation with Mutexes

The word *Mutex* abbreviates *Mutual Exclusion* and describe the basic purpose
of the feature.

* Allow only one thread to enter a [Critical Section], typically non-atomically
  executed sequence of statements
* which temporarily invalidate an [Class Invariant], or
* in other ways accesses a resource not designed for shared use.

.N[
In general, mutexes have at least two operations._[] for

* **lock**-ing and

* **unlock**-ing,

but frequently provide additional features to make their practical use more
convenient or less error prone.
]

[Critical Section]: http://en.wikipedia.org/wiki/Critical_section
[Class Invariant]: http://en.wikipedia.org/wiki/Class_invariant

.F[:
Though the operations may not be spelled exactly *lock* and *unlock* …
(especially as mutexes are somewhat related to [Semaphores], which originally
named their lock (-like) acquire operation *P* and their unlock (-like) release
operation *V*.
]

---
template: plain
name: mutex_example_1
header: #### Mutex Example (1)

The following example calculates one table from another one:._[]
```
template<typename In, typename Out, typename Transformation>
void worker(const In data[], std::size_t data_size,
            Out result[], std::size_t &total_progress,
            Transformation func) {
    static std::mutex critical_section;
    while (total_progress < data_size) {
        critical_section.lock();
        constexpr auto chunks = std::size_t{100};
        const auto beg = total_progress;
        const auto end = ((data_size - total_progress) > chunks)
                 ? total_progress += chunks
                 : total_progress = data_size;
        critical_section.unlock();
        std::transform(&data[beg], &data[end], &result[beg], func);
    }
}
```

.F[:
The work is shared by any number of `worker` task run concurrently, each
fetches and transforms a fixed number of values. This can be advantageous to
splitting the work by calculating fixed-size regions of the table in advance, if
the transformation function has a largely varying runtime depending on the
argument value.
]

---
template: plain
name: mutex_example_2
header: #### Mutex Example (2)

Assuming the transformation is to calculate square roots and there are
two arrays of size `N`, say

* `data` (filled with values to transform), and
  * `sqrts` to store the results

workers may be created (to be handed over to `std::async`) as follows:._[]
```
std::size_t processed_count = 0;
auto worker_task =
    [&]() { worker(data, N, sqrts, processed_count,
                   [](double e) { return std::sqrt(e); });
    };
```

.F[:
Given the above, a particular nifty way to create and run workers were:
```
  // assuming NCORES holds the number of cores to use by workers:
std::array<std::future<void>, NCORES> workers;
for (auto &w : workers)
    w = std::async(worker_task);
for (auto &w : workers)
    try { w.get(); } catch (...) {}
```
]

---
template: plain
name: mutexes_and_raii
header: #### Mutexes and RAII

As the mutex operations **lock** and **unlock** need to come correctly paired,
they make a good candidate to apply a technique called [RAII].._[]

It works by creating a wrapper class, executing

* the acquiring operation (or *lock*-ing in this case) in its constructor, and
* the releasing operation (or *unlock*-ing) in its destructor.

Such helper classes are available in C++11 `std::lock_guard`.

The big advantage is that unlocking the mutex is guaranteed for code blocks
defining a RAII-style (guard) object locally, no matter whether control
flow reached its end, or by `break`, `return`, or some exception.

.I[
For more information on the RAII-style use of mutexes see:
http://en.cppreference.com/w/cpp/thread/lock_guard
]

.F[:
This [TLA] is an abbreviation Bjarne Stroustrup once coined for *Resource
Acquisition is Initialisation*. In a recent interview Stroustrup revealed that he
is not particularly happy with the term he once choose. But to change it would
require to travel back in a time machine and suggest something more appropriate
to him, as today the term RAII is in much too widespread use to be replaced
by something else.
]

[RAII]: http://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization
[TLA]: http://www.catb.org/jargon/html/T/TLA.html

---
template: plain
name: mutexes_and_raii
header: #### Mutex Variants

Mutexes in C++11 come in a number of flavours, which controlling their behaviour
with respect to the following details:

* Whether or not some thread that already locked a mutex may lock it once more
  (and needs to release it as often).

* Whether or not a thread waits if it finds a mutex locked by some other thread,
  and in the latter case until *when* (clock-based time point) or or *how long*
  (duration) it waits.

For all mutex variants there are also variants of RAII-style lock guards.

.I[
For more information on the different variants of mutexes and RAII-style
wrappers see:
http://en.cppreference.com/w/cpp/thread/recursive_mutex
http://en.cppreference.com/w/cpp/thread/timed_mutex
http://en.cppreference.com/w/cpp/thread/recursive_timed_mutex and
http://en.cppreference.com/w/cpp/thread/unique_lock
 
]
  
---
template: plain
name: mutexes_and_raii
header: #### C++14: Upgradable Locks

C++14 added the class `std::shared_lock`, supporting a frequent necessity:._[]

.N.center[
[Multiple Reader/Single Write Locking Schemes](http://en.wikipedia.org/wiki/Readers%E2%80%93writer_lock)
]

* Any number of (reader) threads may successfully `shared_lock` that kind of
  mutex …
* … but only one single (writer) thread is allowed to actually `lock` it
  (unshared).

C++14 also provides a RAII-style wrapper for shared locking.

.I[
For more information on shared locking see:
http://en.cppreference.com/w/cpp/thread/shared_timed_mutex and
http://en.cppreference.com/w/cpp/thread/shared_lock
]

.F[:
Note that the terms "reader" and "writer" indicates the typical use of that kind
of mutex, assuming that it is sufficient for readers to obtain the lock shared,
as it guarantees the invariants hold but no modifications are made, while writers
will need to temporarily break invariants.
]

---
template: plain
name: mutexes_and_raii
header: #### Defeating Deadlocks Caused by Mutex-Locking

As a potentially blocking mechanism mutexes are famous for creating deadlocks, i.e

* in the situation where two resources *A* and *B* are required,
* one thread acquires these in the order *first A, then B* and
* another thread acquires these in the order *first B, then A*.._[]

The obvious counter measure is to acquire locks always in the same order,
as achievable with `std::lock` and `std::try_lock`.

.I[
For more information on locking several mutexes semantically atomic (i.e.
without creating the potential for dead-locks) see:
http://en.cppreference.com/w/cpp/thread/lock and
http://en.cppreference.com/w/cpp/thread/try_lock
]

.F[:
In practice, the potential for deadlocks is often not as obvious as in this
example but much more intricate and close to impossible to spot, even in
scrutinising code reviews. So, if (accidental) deadlocks cannot be avoided,
sometimes a "self-healing" strategy is applied that works follows:  
**If more than one lock needs to be acquired, acquire at least all others with
setting a time-out.** If that hits, **release all locks** acquired so far,
**delay** for some small amount of time (usually determined in a window with
some slight randomness), then **try again** (and maybe in a different order).
]

---
template: plain
name: onetime_execution
header: ### One-Time Execution

For a particular scenario that would otherwise require the use of mutex-es to
avoid a [Race Condition], there is pre-built solution in C++11.

Executing a piece of code exactly once can be achieved in a cookbook-style as
follows:
```
// in a scope reachable from all usage points:
std::once_flag this_code_once;
…
std::call_once(this_code_once, …some callable… ); // somewhere
…
std::call_once(this_code_once, … ); // maybe somewhere else
```

For any of the callables associated with the same instance of an
`std::once_flag` via `std::call_once`, without further protection via mutexes
it is guaranteed that **at most one** is executed **at most once**.

.I[
For more information on guaranteed one time execution see:
http://en.cppreference.com/w/cpp/thread/once_flag and
http://en.cppreference.com/w/cpp/thread/call_once
]

[Race Condition]: http://en.wikipedia.org/wiki/Race_condition

---
template: plain
name: onetime_execution
header: #### One-Time Execution Example

A typical use case for guaranteed one-time execution is some initialisation,
that may be expensive and is therefore delayed until a function that depends
on it is called the first time.

The following fragment avoids parallel initialisations of `table`:
```
… foo( … ) {
    static std::once_flag init;
    static std::array<int, 1000> table;
    std::call_once(init, [&table]{
        … // precalculate table when foo runs for the first time
    });
    … //
}
```

---
template: plain
name: onetime_execution
header: #### Local `static` Initialisation

Since C++11 supports multi-threading in the core language, initialising local
`static` variables is protected to be executed at most once:
```
… foo( … ) {
   static const int z = expensive_calculation();
   … //
}
```

As since C++11 compilers are required to wrap the necessary protection around
the initialisation of static locals, also this is guaranteed to work:._[]
```
class Singleton {
    … //
public:
    static Singleton &getInstance() {
        static Singleton instance;
        return instance;
    }
};
```

.F[:
Non-believers should consider to copy the above code, paste it to
https://gcc.godbolt.org/ (or similar) after adding a member with a
runtime-dependant initialisation, and view the assembler output … 
]

---
template: plain
name: condition_variables
header: ### Notifications with Condition Variables

A well-known abstraction in concurrent programming are
combining mutexes with a signalling mechanism.

One main use of condition variables is to **avoid busy waiting** in
producer-consumer designs,

* where consumer and producer run concurrently,

* exchanging data over some buffer data structure.

.I[
For more information on condition variables see:
http://en.cppreference.com/w/cpp/thread/condition_variable
]

---
template: plain
name: condition_variables
header: #### Condition Variable Example (1)

The following *RingBuffer* class can put condition variables to good use …
```
template<typename T, std::size_t N>
class RingBuffer {
    std::array<T, N+1> buf;
    std::size_t p = 0, g = 0;
    bool empty() const { return p == g; }
    bool full() const { return (p+1) % buf.size() == g; }
public:
    void put(const T &val) {
        if (full())
            … // handle case no space is available
        buf[p++] = val; p %= buf.size();
    }
    void get(T &val) {
        if (empty())
            … // handle case no data is available
        val = buf[g++]; g %= buf.size();
    }
};
```
… exactly at the currently omitted points.

---
template: plain
name: condition_variables
header: #### Condition Variable Example (2)

Obviously there are two conditions, that need special attention:

* The buffer may be full when `put` is called, or
* it may be empty, when `get` is called.

Therefore two condition variables._[] are added, furthermore a mutex to protect
accessing the buffer:
```
class RingBuffer {
    … //
    … // as before
    … //
    std::condition_variable data_available;
    std::condition_variable space_available;
    std::mutex buffer_access;
public:
    … // see next page
};
```

.F[:
As the buffer space cannot be full and empty at the same time, technically
one condition variable would suffice, but for this introductory example using
two different instances seems to be clearer.
]

---
template: plain
name: condition_variables
header: #### Condition Variable Example (3)

There are two operations (of interest here), applicable to condition variables,
**sending** and **waiting for** notifications:._[]
```
class RingBuffer
   … // see previous page
public:
    void put(const T &val) {
        std::unique_lock<std::mutex> lock(buffer_access);
        space_available.wait(lock, [this]{ return !full(); });
        buf[p++] = val; p %= buf.size();
        data_available.notify_one();
    }
    void get(T &val) {
        std::unique_lock<std::mutex> lock(buffer_access);
        data_available.wait(lock, [this]{ return !empty(); });
        val = buf[g++]; g %= buf.size();
        space_available.notify_one();
    }
};
```

.F[:
Essential here is also the connection between the condition variables, the mutex
protecting the `RingBuffer` invariants, and the conditions checked as part of
waiting, which are detailed on the next page.
]

---
template: plain
name: waiting_in_details
header: #### Waiting Anatomy

Waiting on a condition variable – as shown on the last page – with
```
// as part of put:
   std::unique_lock<std::mutex> lock(buffer_access);
   space_available.wait(lock, [this]{ return !full(); });
```
```
// as part of get:
   std::unique_lock<std::mutex> lock(buffer_access);
   data_available.wait(lock, [this]{ return !empty(); });
```

is equivalent to the following, **with the mutex being locked before**:

.pull-left[
```
// as part of put:
   while (full()) { // !!full()
       buffer_access.unlock();
       // wait for notification
       buffer_access.lock();
   }
```
]
.pull-right[
```
// as part of get:
   while (empty()) { // !!empty
       buffer_access.unlock();
       // wait for notification
       buffer_access.lock();
   }
```
]

At this point **the mutex is locked** (again) and **the condition is true.**

---
template: plain
name: spurious_wakeups
header: #### Spurious Wakeups

In the example code before, the loop (in the equivalent of `wait`-ing) may seem
unnecessary, as the respective notification will be sent only after some action
has made the condition true.

Nevertheless it makes sense and may even be necessary:

* **First of all, an implementation is allowed to give [Spurious Wake-Up]s,
  so the loop is necessary anyway.**

* If notifications are sent while nobody waits on the condition variable, it is
  simply discarded, therefore

* a producer-consumer scenario is more robust if it tends to send "too many"
    notifications (of which some are discarded) …
  * … while sending "too few" could cause some thread to wait forever.

.N[
Specifying the condition check in combination with `wait`-ing *does it right*
and hence should be preferred over writing a loop explicitly.._[]
]

[Spurious Wake-Up]: http://en.wikipedia.org/wiki/Spurious_wakeup

.F[:
Thus avoiding some later maintainer considers the `while` "unnecessary" and
replace it with `if` …
]

---
template: plain
name: atomic_operations
header: ### Atomic Operation Support

With the support for atomic operations C++11 multi-threading allows to implement
 
* [Wait-Free Algorithms](http://en.wikipedia.org/wiki/Non-blocking_algorithm#Wait-freedom),

* [Lock-Free Algorithms](http://en.wikipedia.org/wiki/Non-blocking_algorithm#Lock-freedom),
  and

* [Obstruction-Free Algorithms](http://en.wikipedia.org/wiki/Non-blocking_algorithm#Obstruction-freedom).

The basic concept is to provide a way to know whether a certain modification of
some memory location was caused by the current thread or by another one.

.I[
For more information about atomic operation support see:
http://en.cppreference.com/w/cpp/atomic
]

---
template: plain
name: atomic_operations_example
header: #### Atomic Operations Example

The following example, demonstrating the lock-free approach with operations
(instead of using mutexes) modifies a [former example](#mutex_example_1):
```
template<typename T1, typename T2, typename Transformation>
void worker(T1 args[], std::size_t data_size, T2 result,
            std::atomic_size_t &total_progress,
            Transformation func) {
    while (total_progress < data_size) {
        constexpr auto chunks = std::size_t{100};
        std::size_t beg = total_progress.load();
        std::size_t end;
        do {
            end = ((data_size - beg) > chunks)
                ? beg + chunks
                : data_size;
        } while (!total_progress.compare_exchange_weak(beg, end));
        std::transform(&data[beg], &data[end], &sqrts[beg], func);
    }
}
```

.N[
Be sure to understand that the loop controlled by the return value of
`compare_exchange_weak` guarantees the prior calculations are (still) valid.
]

---
template: plain
name: memory_order
header: #### Memory Order (and Dependencies)

Memory order *"specifies how regular, non-atomic memory accesses are to be ordered
around an atomic operation*".._[]

There are a number of different models to select from,

* with the default providing *"sequentially consistent ordering"*,

* being closest to what most developers would expect,

* but (possibly) not with the optimal performance for a given use case.

.I[
For more information on memory order see:
http://en.cppreference.com/w/cpp/atomic/memory_order
]

.F[:
Cited from the reference further down on this page.
]

---
template: plain
name: atomic_operations
header: #### Atomic Operations Recommendation

.N[

* Except for the potential of deadlocks the challenges are similar to algorithms
  using locks:._[]

* Problems may only show for a critical timing and may be reproducible in
    particular test environments only.

* In general, a failed test may show the presence of errors, but even many
    successful tests do not guarantee their absence.

]
.W[
Beyond trivial cases – like the one shown in the example –, implementing
multi-threaded programs with atomic operations requires **substantial
expertise**.

Be sure to keep the design **as simple as possible** and have it reviewed by
other developers experienced in that particular fields, maybe both, colleagues
and hired consultants too.
]

.F[:
It may very well be the case that problematic situations depend on hardware
features like the size of cache-lines, the depth of an instruction pipeline,
or the way branch prediction works.
]

---
template: plain
name: direct_use_of_threads
header: ### Using Class `std::thread`

In the examples before the class `std::thread` was used only indirectly (via
`std::async`, building on [Futures and Promises](#futures_and_promises).

* There is also a class `std::thread`
* taking any runnable code as constructor argument,
* executing it in a separate thread.

.I[
For more information on the `std::thread` class see:
http://en.cppreference.com/w/cpp/thread
]

**There are explicitly no means provided to forcefully terminate one thread
from another one.**

.W[
Any non-portable way._[] to forcefully terminate a thread risks to entail
serious consequences later, as e.g. locks may not be released or awaited
notifications not be sent.
]

.F[:
Such as potentially existing *interrupt* or *kill* functions reachable via a
[Native Interface Handle](#native_thread_handles).
]

---
template: plain
header: #### Example for Using Class `std::thread`

In case worker **returns no value** and **throws no exception**, like in a
[former example](#mutex_example_1) (and assuming the
[same set-up](#mutex_example_2)), using class `std::thread` directly can be
straight forward (left) or done in the "nifty" way (right):

.pull-left[
```
// starting some workers
// multi-threaded ...
std::thread t1{worker_task};
std::thread t2{worker_task};
…
// ... and waiting for them
// to finish:
t1.join();
t2.join();
…
```
]
.pull-right[
```
using namespace std;
constexpr auto NCORES = 4;
// starting one worker-thread
// per core ...
array<thread, NCORES> threads;
for (auto &t : threads)
    t = thread{worker_task};
// ... and wait for all to
// finish:
for (auto &t : threads)
    t.join();
```
]

An alternative to [`join`]-ing with a thread is [`detach`]-ing it.

[`join`]: http://en.cppreference.com/w/cpp/thread/thread/join
[`detach`]: http://en.cppreference.com/w/cpp/thread/thread/detach

.W[
A program will immediately terminate if an instance of class
`std::thread` referring to an active thread gets destructed.
]

---
template: plain
header: #### Recommendations for Using Class `std::thread`

.N[

* In trivial cases (no exceptions, no result to fetch) using threads via
  instances of class `std::thread` may be considered.

* Nevertheless understand the peculiarities and know how to avoid race
  conditions, especially when `std::thread` objects go out of scope.._[]

]
.W[
It causes the program to terminate if the callable started throws an
exception or an instance of `std::thread` is still in *joinable state*
when it goes out of scope and gets destructed.
]

.F[:
At [1:00:47] the following video by Scott Meyers gives a good introduction to
the problem and some recipes how it can be avoided:
http://channel9.msdn.com/Events/GoingNative/2013/An-Effective-Cpp11-14-Sampler
]

[1:00:47]: https://www.youtube.com/watch?feature=player_detailpage&v=BezbcQIuCsY#t=3646

---
template: plain
name: native_thread_handles
header: ### Native Handles

Last and finally, most C++11 multi-threading implementation build on a
threading model provided by their execution environment.

.N[
The requirements in the standard are rather the intersection of the features
provided by well-known threading models.
]

Usually and typically the standard does not mandate any any extensions thereof,
but in some cases provides a way to "reach through" to the native thread model:

* E.g. `std::thread::native_handle` could provide ways to manipulate thread
  priorities, maybe including ways to specify protocols for [Priority Ceiling]
  or other means to circumvent [Priority Inversion].

* Also the classes for condition variables and the various kinds of mutexes have
  member functions `native_handle`.

* The enumeration `std::launch` may provide more (named) values to control
  implementation specific details in the behavior of `std::async`.

[Priority Ceiling]: http://en.wikipedia.org/wiki/Priority_ceiling_protocol
[Priority Inversion]: http://en.wikipedia.org/wiki/Priority_inversion

---
template: plain
name: concurrency_recommendations
header: ### Concurrency Recommendations

So far the presentation of C++11 concurrency support was only meant as an
overview.

.W[
Practically using concurrency features beyond parallelizing independent tasks
requires much more knowledge and experience in this area, what this presentation
can not provide.
]

A good and near to exhaustive coverage of the concurrency part of C++11 is:

.N.center[
C++ Concurrency in Action  
Practical Multithreading  
by Anthony Williams  
[ISBN-13: 978-1-9334988-77-1](http://www.cplusplusconcurrencyinaction.com)
]

---
name: boost_thread
header: ## Boost: Threads Library

[Boost.Thread]: http://www.boost.org/doc/libs/release/doc/html/thread.html

As C++11 threads emerged from [Boost.Thread] there is little difference.

Some differences between C++11 and Boost may exist (depending on the version
of the latter) and new features may appear and be tried in Boost first before
eventually getting part of an upcoming C++ standard.

.N[
There are many reasons why a pure library solution may have practical
difficulties to properly support concurrency.

Most important is in the memory model, which needs to be clearly defined so
that optimising compilers are restricted to the necessary limits – but not
(too far) beyond these.._[]
]

.F[:
A related, easy to recognize problem that demonstrates why concurrency cannot
be added as a library alone but must be part of the core language part, can be
seen when considering a long-existing features of C which is also part of C++:
the initialization of local `static` variables. By definition this takes place
when the definition is reached in the program flow for the first time and hence,
in the general case must be protected with a mutex (or similar) to avoid race
conditions (except for initialisation with a compile-time constant that can be
loaded at program startup).
]

---
name: boost_asio
header: ## Boost: Asio

[Boost.Asio]: http://www.boost.org/doc/libs/release/doc/html/boost_asio.html

The purpose of [Boost.Asio] is to support event-driven program designs by
providing a framework to dispatch incoming events to previously registered event
handlers:

* The largest group of events deals with I/O, especially the arrival of data
  from asynchronous sources (like sockets).

* Besides that it allows also an applications to send events to itself, maybe
  with a specified delay.

Event handlers are run single-threaded and hence there is no need for
synchronisation as is in multi-threaded designs. For good responsiveness an
event driven program design must keep event handlers small.

.N[
Especially an event handler should **never** delay or wait for responses of
an external client – rather register an handler to be started when the
response arrives.
]