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  <title>The Boost Statechart Library - Performance</title>
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        <h3><a href="../../../index.htm"><img alt="C++ Boost" src=
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        <h1 align="center">The Boost Statechart Library</h1>

        <h2 align="center">Performance</h2>
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  <dl class="index">
    <dt><a href="#SpeedVersusScalabilityTradeoffs">Speed versus scalability
    tradeoffs</a></dt>

    <dt><a href="#MemoryManagementCustomization">Memory management
    customization</a></dt>

    <dt><a href="#RttiCustomization">RTTI customization</a></dt>

    <dt><a href="#ResourceUsage">Resource usage</a></dt>
  </dl>

  <h2><a name="SpeedVersusScalabilityTradeoffs" id=
  "SpeedVersusScalabilityTradeoffs">Speed versus scalability
  tradeoffs</a></h2>

  <p>Quite a bit of effort has gone into making the library fast for small
  simple machines <b>and</b> scaleable at the same time (this applies only to
  <code>state_machine&lt;&gt;</code>, there still is some room for optimizing
  <code>fifo_scheduler&lt;&gt;</code>, especially for multi-threaded builds).
  While I believe it should perform reasonably in most applications, the
  scalability does not come for free. Small, carefully handcrafted state
  machines will thus easily outperform equivalent Boost.Statechart machines.
  To get a picture of how big the gap is, I implemented a simple benchmark in
  the BitMachine example. The Handcrafted example is a handcrafted variant of
  the 1-bit-BitMachine implementing the same benchmark.</p>

  <p>I tried to create a fair but somewhat unrealistic <b>worst-case</b>
  scenario:</p>

  <ul>
    <li>For both machines exactly one object of the only event is allocated
    before starting the test. This same object is then sent to the machines
    over and over</li>

    <li>The Handcrafted machine employs GOF-visitor double dispatch. The
    states are preallocated so that event dispatch &amp; transition amounts
    to nothing more than two virtual calls and one pointer assignment</li>
  </ul>

  <p>The Benchmarks - running on a 3.2GHz Intel Pentium 4 - produced the
  following dispatch and transition times per event:</p>

  <ul>
    <li>Handcrafted:

      <ul>
        <li>MSVC7.1: 10ns</li>

        <li>GCC3.4.2: 20ns</li>

        <li>Intel9.0: 20ns</li>
      </ul>
    </li>

    <li>1-bit-BitMachine with customized memory management:

      <ul>
        <li>MSVC7.1: 150ns</li>

        <li>GCC3.4.2: 320ns</li>

        <li>Intel9.0: 170ns</li>
      </ul>
    </li>
  </ul>

  <p>Although this is a big difference I still think it will not be
  noticeable in most&nbsp;real-world applications. No matter whether an
  application uses handcrafted or Boost.Statechart machines it will...</p>

  <ul>
    <li>almost never run into a situation where a state machine is swamped
    with as many events as in the benchmarks. A state machine will almost
    always spend a good deal of time waiting for events (which typically come
    from a human operator, from machinery or from electronic devices over
    often comparatively slow I/O channels). Parsers are just about the only
    application of FSMs where this is not the case. However, parser FSMs are
    usually not directly specified on the state machine level but on a higher
    one that is better suited for the task. Examples of such higher levels
    are: Boost.Regex, Boost.Spirit, XML Schemas, etc. Moreover, the nature of
    parsers allows for a number of optimizations that are not possible in a
    general-purpose FSM framework.<br>
    Bottom line: While it is possible to implement a parser with this
    library, it is almost never advisable to do so because other approaches
    lead to better performing and more expressive code</li>

    <li>often run state machines in their own threads. This adds considerable
    locking and thread-switching overhead. Performance tests with the
    PingPong example, where two asynchronous state machines exchange events,
    gave the following times to process one event and perform the resulting
    in-state reaction (using the library with
    <code>boost::fast_pool_allocator&lt;&gt;</code>):

      <ul>
        <li>Single-threaded (no locking and waiting): 840ns / 840ns</li>

        <li>Multi-threaded with one thread (the scheduler uses mutex locking
        but never has to wait for events): 6500ns / 4800ns</li>

        <li>Multi-threaded with two threads (both schedulers use mutex
        locking and exactly one always waits for an event): 14000ns /
        7000ns</li>
      </ul>

      <p>As mentioned above, there definitely is some room to improve the
      timings for the asynchronous machines. Moreover, these are very crude
      benchmarks, designed to show the overhead of locking and thread context
      switching. The overhead in a real-world application will typically be
      smaller and other operating systems can certainly do better in this
      area. However, I strongly believe that on most platforms the threading
      overhead is usually larger than the time that Boost.Statechart spends
      for event dispatch and transition. Handcrafted machines will inevitably
      have the same overhead, making raw single-threaded dispatch and
      transition speed much less important</p>
    </li>

    <li>almost always allocate events with <code>new</code> and destroy them
    after consumption. This will add a few cycles, even if event memory
    management is customized</li>

    <li>often use state machines that employ orthogonal states and other
    advanced features. This forces the handcrafted machines to use a more
    adequate and more time-consuming book-keeping</li>
  </ul>

  <p>Therefore, in real-world applications event dispatch and transition not
  normally constitutes a bottleneck and the relative gap between handcrafted
  and Boost.Statechart machines also becomes much smaller than in the
  worst-case scenario.</p>

  <h3>Detailed performance data</h3>

  <p>In an effort to identify the main performance bottleneck, the example
  program "Performance" has been written. It measures the time that is spent
  to process one event in different BitMachine variants. In contrast to the
  BitMachine example, which uses only transitions, Performance uses a varying
  number of in-state reactions together with transitions. The only difference
  between in-state-reactions and transitions is that the former neither enter
  nor exit any states. Apart from that, the same amount of code needs to be
  run to dispatch an event and execute the resulting action.</p>

  <p>The following diagrams show the average time the library spends to
  process one event, depending on the percentage of in-state reactions
  employed. 0% means that all triggered reactions are transitions. 100% means
  that all triggered reactions are in-state reactions. I draw the following
  conclusions from these measurements:</p>

  <ul>
    <li>The fairly linear course of the curves suggests that the measurements
    with a 100% in-state reaction ratio are accurate and not merely a product
    of optimizations in the compiler. Such optimizations might have been
    possible due to the fact that in the 100% case it is known at
    compile-time that the current state will never change</li>

    <li>The data points with 100% in-state reaction ratio and speed optimized
    RTTI show that modern compilers seem to inline the complex-looking
    dispatch code so aggressively that dispatch is reduced to little more
    than it actually is, one virtual function call followed by a linear
    search for a suitable reaction. For instance, in the case of the 1-bit
    Bitmachine, Intel9.0 seems to produce dispatch code that is equally
    efficient like the two virtual function calls in the Handcrafted
    machine</li>

    <li>On all compilers and in all variants the time spent in event dispatch
    is dwarfed by the time spent to exit the current state and enter the
    target state. It is worth noting that BitMachine is a flat and
    non-orthogonal state machine, representing a close-to-worst case.
    Real-world machines will often exit and enter multiple states during a
    transition, what further dwarfs pure dispatch time. This makes the
    implementation of constant-time dispatch (as requested by a few people
    during formal review) an undertaking with little merit. Instead, the
    future optimization effort will concentrate on state-entry and
    state-exit</li>

    <li>Intel9.0 seems to have problems to optimize/inline code as soon as
    the amount of code grows over a certain threshold. Unlike with the other
    two compilers, I needed to compile the tests for the 1, 2, 3 and 4-bit
    BitMachine into separate executables to get good performance. Even then
    was the performance overly bad for the 4-bit BitMachine. It was much
    worse when I compiled all 4 tests into the same executable. This surely
    looks like a bug in the compiler</li>
  </ul>

  <h4>Out of the box</h4>

  <p>The library is used as is, without any optimizations/modifications.</p>

  <p><img alt="PerformanceNormal1" src="PerformanceNormal1.gif" border="0"
  width="371" height="284"><img alt="PerformanceNormal2" src=
  "PerformanceNormal2.gif" border="0" width="371" height="284"><img alt=
  "PerformanceNormal3" src="PerformanceNormal3.gif" border="0" width="371"
  height="284"><img alt="PerformanceNormal4" src="PerformanceNormal4.gif"
  border="0" width="371" height="284"></p>

  <h4>Native RTTI</h4>

  <p>The library is used with <code><a href=
  "configuration.html#ApplicationDefinedMacros">BOOST_STATECHART_USE_NATIVE_RTTI</a></code>
  defined.</p>

  <p><img alt="PerformanceNative1" src="PerformanceNative1.gif" border="0"
  width="371" height="284"><img alt="PerformanceNative2" src=
  "PerformanceNative2.gif" border="0" width="371" height="284"><img alt=
  "PerformanceNative3" src="PerformanceNative3.gif" border="0" width="371"
  height="284"><img alt="PerformanceNative4" src="PerformanceNative4.gif"
  border="0" width="371" height="284"></p>

  <h4>Customized memory-management</h4>

  <p>The library is used with customized memory management
  (<code>boost::fast_pool_allocator</code>).</p>

  <p><img alt="PerformanceCustom1" src="PerformanceCustom1.gif" border="0"
  width="371" height="284"><img alt="PerformanceCustom2" src=
  "PerformanceCustom2.gif" border="0" width="371" height="284"><img alt=
  "PerformanceCustom3" src="PerformanceCustom3.gif" border="0" width="371"
  height="284"><img alt="PerformanceCustom4" src="PerformanceCustom4.gif"
  border="0" width="371" height="284"></p>

  <h3><a name="DoubleDispatch" id="DoubleDispatch">Double dispatch</a></h3>

  <p>At the heart of every state machine lies an implementation of double
  dispatch. This is due to the fact that the incoming event <b>and</b> the
  active state define exactly which <a href=
  "definitions.html#Reaction">reaction</a> the state machine will produce.
  For each event dispatch, one virtual call is followed by a linear search
  for the appropriate reaction, using one RTTI comparison per reaction. The
  following alternatives were considered but rejected:</p>

  <ul>
    <li><a href=
    "http://www.objectmentor.com/resources/articles/acv.pdf">Acyclic
    visitor</a>: This double-dispatch variant satisfies all scalability
    requirements but performs badly due to costly inheritance tree
    cross-casts. Moreover, a state must store one v-pointer for <b>each</b>
    reaction what slows down construction and makes memory management
    customization inefficient. In addition, C++ RTTI must inevitably be
    turned on, with negative effects on executable size. Boost.Statechart
    originally employed acyclic visitor and was about 4 times slower than it
    is now (MSVC7.1 on Intel Pentium M). The dispatch speed might be better
    on other platforms but the other negative effects will remain</li>

    <li><a href="http://en.wikipedia.org/wiki/Visitor_pattern">GOF
    Visitor</a>: The GOF Visitor pattern inevitably makes the whole machine
    depend upon all events. That is, whenever a new event is added there is
    no way around recompiling the whole state machine. This is contrary to
    the scalability requirements</li>

    <li>Single two-dimensional array of function pointers: To satisfy
    requirement 6, it should be possible to spread a single state machine
    over several translation units. This however means that the dispatch
    table must be filled at runtime and the different translation units must
    somehow make themselves "known", so that their part of the state machine
    can be added to the table. There simply is no way to do this
    automatically <b>and</b> portably. The only portable way that a state
    machine distributed over several translation units could employ
    table-based double dispatch relies on the user. The programmer(s) would
    somehow have to <b>manually</b> tie together the various pieces of the
    state machine. Not only does this scale badly but is also quite
    error-prone</li>
  </ul>

  <h2><a name="MemoryManagementCustomization" id=
  "MemoryManagementCustomization">Memory management customization</a></h2>

  <p>Out of the box, everything (event objects, state objects, internal data,
  etc.) is allocated through <code>std::allocator&lt; void &gt;</code> (the
  default for the Allocator template parameter). This should be satisfactory
  for applications meeting the following prerequisites:</p>

  <ul>
    <li>There are no deterministic reaction time (hard real-time)
    requirements</li>

    <li>The application will never run long enough for heap fragmentation to
    become a problem. This is of course an issue for all long running
    programs not only the ones employing this library. However, it should be
    noted that fragmentation problems could show up earlier than with
    traditional FSM frameworks</li>
  </ul>

  <p>Should an application not meet these prerequisites, Boost.Statechart's
  memory management can be customized as follows:</p>

  <ul>
    <li>By passing a model of the standard Allocator concept to the class
    templates that support a corresponding parameter
    (<code>event&lt;&gt;</code>, <code>state_machine&lt;&gt;</code>,
    <code>asynchronous_state_machine&lt;&gt;</code>,
    <code>fifo_scheduler&lt;&gt;</code>, <code>fifo_worker&lt;&gt;</code>).
    This redirects all allocations to the passed custom allocator and should
    satisfy the needs of just about any project</li>

    <li>Additionally, it is possible to <b>separately</b> customize
    <b>state</b> memory management by overloading <code>operator new()</code>
    and <code>operator delete()</code> for all state classes but this is
    probably only useful under rare circumstances</li>
  </ul>

  <h2><a name="RttiCustomization" id="RttiCustomization">RTTI
  customization</a></h2>

  <p>RTTI is used for event dispatch and
  <code>state_downcast&lt;&gt;()</code>. Currently, there are exactly two
  options:</p>

  <ol>
    <li>By default, a speed-optimized internal implementation is
    employed</li>

    <li>The library can be instructed to use native C++ RTTI instead by
    defining <code><a href=
    "configuration.html#ApplicationDefinedMacros">BOOST_STATECHART_USE_NATIVE_RTTI</a></code></li>
  </ol>

  <p>There are 2 reasons to favor 2:</p>

  <ul>
    <li>When a state machine (or parts of it) are compiled into a DLL,
    problems could arise from the use of the internal RTTI mechanism (see the
    FAQ item "<a href="faq.html#Dll">How can I compile a state machine into a
    dynamic link library (DLL)?</a>"). Using option 2 is one way to work
    around such problems (on some platforms, it seems to be the only
    way)</li>

    <li>State and event objects need to store one pointer less, meaning that
    in the best case the memory footprint of a state machine object could
    shrink by 15% (an empty event is typically 30% smaller, what can be an
    advantage when there are bursts of events rather than a steady flow).
    However, on most platforms executable size grows when C++ RTTI is turned
    on. So, given the small per machine object savings, this only makes sense
    in applications where both of the following conditions hold:

      <ul>
        <li>Event dispatch will never become a bottleneck</li>

        <li>There is a need to reduce the memory allocated at runtime (at the
        cost of a larger executable)</li>
      </ul>

      <p>Obvious candidates are embedded systems where the executable resides
      in ROM. Other candidates are applications running a large number of
      identical state machines where this measure could even reduce the
      <b>overall</b> memory footprint</p>
    </li>
  </ul>

  <h2><a name="ResourceUsage" id="ResourceUsage">Resource usage</a></h2>

  <h3>Memory</h3>

  <p>On a 32-bit box, one empty active state typically needs less than 50
  bytes of memory. Even <b>very</b> complex machines will usually have less
  than 20 simultaneously active states so just about every machine should run
  with less than one kilobyte of memory (not counting event queues).
  Obviously, the per-machine memory footprint is offset by whatever
  state-local members the user adds.</p>

  <h3>Processor cycles</h3>

  <p>The following ranking should give a rough picture of what feature will
  consume how many cycles:</p>

  <ol>
    <li><code>state_cast&lt;&gt;()</code>: By far the most cycle-consuming
    feature. Searches linearly for a suitable state, using one
    <code>dynamic_cast</code> per visited state</li>

    <li>State entry and exit: Profiling of the fully optimized
    1-bit-BitMachine suggested that roughly 3 quarters of the total event
    processing time is spent destructing the exited state and constructing
    the entered state. Obviously, transitions where the <a href=
    "definitions.html#InnermostCommonContext">innermost common context</a> is
    "far" from the leaf states and/or with lots of orthogonal states can
    easily cause the destruction and construction of quite a few states
    leading to significant amounts of time spent for a transition</li>

    <li><code>state_downcast&lt;&gt;()</code>: Searches linearly for the
    requested state, using one virtual call and one RTTI comparison per
    visited state</li>

    <li>Deep history: For all innermost states inside a state passing either
    <code>has_deep_history</code> or <code>has_full_history</code> to its
    state base class, a binary search through the (usually small) history map
    must be performed on each exit. History slot allocation is performed
    exactly once, at first exit</li>

    <li>Shallow history: For all direct inner states of a state passing
    either <code>has_shallow_history</code> or <code>has_full_history</code>
    to its state base class, a binary search through the (usually small)
    history map must be performed on each exit. History slot allocation is
    performed exactly once, at first exit</li>

    <li>Event dispatch: One virtual call followed by a linear search for a
    suitable <a href="definitions.html#Reaction">reaction</a>, using one RTTI
    comparison per visited reaction</li>

    <li>Orthogonal states: One additional virtual call for each exited state
    <b>if</b> there is more than one active leaf state before a transition.
    It should also be noted that the worst-case event dispatch time is
    multiplied in the presence of orthogonal states. For example, if two
    orthogonal leaf states are added to a given state configuration, the
    worst-case time is tripled</li>
  </ol>
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  <p>Revised 
  <!--webbot bot="Timestamp" s-type="EDITED" s-format="%d %B, %Y" startspan -->03 December, 2006<!--webbot bot="Timestamp" endspan i-checksum="38512" --></p>

  <p><i>Copyright &copy; 2003-<!--webbot bot="Timestamp" s-type="EDITED" s-format="%Y" startspan -->2006<!--webbot bot="Timestamp" endspan i-checksum="770" -->
  <a href="contact.html">Andreas Huber D&ouml;nni</a></i></p>

  <p><i>Distributed under the Boost Software License, Version 1.0. (See
  accompanying file <a href="../../../LICENSE_1_0.txt">LICENSE_1_0.txt</a> or
  copy at <a href=
  "http://www.boost.org/LICENSE_1_0.txt">http://www.boost.org/LICENSE_1_0.txt</a>)</i></p>
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