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\begin{document}
\title{CODES Best Practices}

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\begin{abstract}
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This document outlines best practices for developing models in the
CODES/ROSS framework.  The reader should already be familiar with ROSS
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and discrete event simulation in general; those topics are covered in the primary
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ROSS documentation. Additionally, the GETTING\_STARTED file presents a better
introduction/overview to CODES - this guide should be consulted after becoming
familiar with CODES/ROSS.
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%
The main purpose of this document is to help the reader produce
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CODES models in a consistent, modular style so that components can be more
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easily shared and reused.  It also includes a few tips to help avoid common
simulation bugs.
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\end{abstract}

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\section{CODES: modularizing models}
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This section covers some of the basic principles of how to organize model
components to be more modular and easier to reuse across CODES models.

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\subsection{Units of time}
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ROSS does not dictate the units to be used in simulation timestamps.
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The \texttt{tw\_stime} type could represent any time unit
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(e.g. days, hours, seconds, nanoseconds, etc.).  When building CODES
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models you should \emph{always treat timestamps as double precision floating
point numbers representing nanoseconds}, however.
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All components within a model must agree on the time units in order to
advance simulation time consistently.  Several common utilities in the
CODES project expect to operate in terms of nanoseconds.
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\subsection{Organizing models by LP types}

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ROSS allows you to use as many different LP types as you would like to
construct your models.  Try to take advantage of this as much as possible by
organizing your simulation so that each component of the system that you are
modeling is implemented within its own LP type.  For example, a storage
system model might use different LPs for hard disks, clients, network
adapters, and servers.  There are multiple reasons for dividing up models
like this:

\begin{itemize}
\item General modularity: makes it easier to pull out particular components
(for example, a disk model) for use in other models.
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\item Simplicity: if each LP type is only handling a limited set of
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events, then the event structure, state structure, and event handler
functions will all be much smaller and easier to understand.
\item Reverse computation: it makes it easier to implement reverse
computation, not only because the code is simpler, but also because you can
implement and test reverse computation per component rather than having to
apply it to an entire model all at once before testing.
\end{itemize}

It is also important to note that you can divide up models not just by
hardware components, but also by functionality, just as
you would modularize the implementation of a distributed file system.  For
example, a storage daemon might include separate LPs for replication, failure
detection, and reconstruction.  Each of those LPs can share the same network
card and disk resources for accurate modeling of resource usage.  They key
reason for splitting them up is to simplify the model and to encourage
reuse.

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One hypothetical downside to splitting up models into multiple LP types is that it likely
means that your model will generate more events than a monolithic model
would have.  Remember that \emph{ROSS is really efficient at generating and
processing events}, though!  It is usually a premature optimization to try to optimize a model by
replacing events with function calls in cases where you know the necessary
data is available on the local MPI process.  Also recall that any information
exchanged via event automatically benefits by shifting burden for
tracking/retaining event data and event ordering into ROSS rather than your
model.  This can help simplify reverse computation by breaking complex
operations into smaller, easier to understand (and reverse) event units with
deterministic ordering.

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\subsection{Protecting data structures}

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ROSS operates by exchanging events between LPs.  If an LP is sending
an event to another LP of the same type, then in general it can do so
by allocating an event structure (e.g. \texttt{tw\_event\_new()}),
populating the event structure, and transmitting it
(e.g. \texttt{tw\_event\_send()}).  If an LP is sending an event to
another LP of a \emph{different} type, however, then it should use an
explicit API to do so without exposing the other LP's event structure
definition.  Event structures are not a robust API for exchanging data
across different LP types.  If one LP type accesses the event (or state)
structure of another LP type, then it entangles the two components such
that one LP is dependent upon the internal architecture of another LP.
This not only makes it difficult to reuse components, but also makes it
difficult to check for incompatibilities at compile time.  The compiler
has no way to know which fields in a struct must be set before sending
an event.

For these reasons we encourage that a) each LP be implemented in a separate
source file and b) all event structs and state structs
be defined only within those source files.  They should not be exposed in external
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headers.  If the definitions are placed in a header then it makes it
possible for those event and state structs to be used as an ad-hoc interface
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between LPs of different types.
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\section{CODES/ROSS: general tips and tricks}
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\subsection{Event magic numbers}

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Put magic numbers at the top of each event struct and
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check them in event handler.  This makes sure that you don't accidentally
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send the wrong event type to an LP, and aids debugging.
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\subsection{Avoiding event timestamp ties}
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Event timestamp ties in ROSS occur when two or more events have the same
timestamp. These have a variety of unintended consequences, most significant of
which is hampering both reproducability and determinism in simulations. To
avoid this, use codes\_local\_latency for events with small or zero time deltas
to add some random noise. codes\_local\_latency must be reversed, so use
codes\_local\_latency\_reverse in reverse event handlers.
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One example of this usage is exchanging events between LPs without really
consuming significant time (for example, to transfer information from a server
to its locally attached network card). It is tempting to use a timestamp of 0,
but this would cause timestamp ties in ROSS. Use of codes\_local\_latency for
timing of local event transitions in this case can be thought of as bus
overhead or context switch overhead.
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\subsection{Organizing event structures}

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Since a single event structure contains data for all of the different types of
events processed by the LP, use a type enum + unions as an organizational
strategy. Keeps the event size down and makes it a little clearer what
variables are used by which event types.
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\subsection{Validating across simulation modes}

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During development, you should do test runs with serial, parallel conservative,
and parallel optimistic runs to make sure that you get consistent results.
These modes stress different aspects of the model.
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\subsection{Working with floating-point data}

Floating point variables are particularly tricky to use in optimistic
simulations, as rounding errors prevent rolling back to a consistent state by
merely performing the inverse operations (e.g., $a+b-b \neq a$). Hence, it is
instead preferable to simply store the local floating-point state in the event
structure and perform assignment on rollback.

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\subsection{How to complete a simulation}

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Most core ROSS examples are design to intentionally hit
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the end timestamp for the simulation (i.e. they are modeling a continuous,
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steady state system). This isn't necessarily true for other models. Quite
simply, set g\_tw\_ts\_end to an arbitrary large number when running simulations
that have a well-defined end-point in terms of events processed. 
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Within the LP finalize function, do not call tw\_now. The time returned may not
be consistent in the case of an optimistic simulation.
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\subsection{Handling non-trivial event dependencies}

In storage system simulations, it will often be the case that clients, servers,
or both issue multiple asynchronous (parallel) operations, performing some
action upon the completion of them. More generally, the problem is: an event
issuance (an ack to the client) is based on the completion of more than one
asynchronous/parallel events (local write on primary server, forwarding write to
replica server). Further complicating the matter for storage simulations, there
can be any number of outstanding requests, each waiting on multiple events. 

In ROSS's sequential and conservative parallel modes, the necessary state can
easily be stored in the LP as a queue of statuses for each set of events,
enqueuing upon asynchronous event issuances and updating/dequeuing upon each
completion. Each LP can assign unique IDs to each queue item and propagate the
IDs through the asynchronous events for lookup purposes. However, in optimistic
mode we may remove an item from the queue and then be forced to re-insert it
during reverse computation.

Naively, one could simply never remove queue items, but of course memory will
quickly be consumed.

An elegant solution to this is to \emph{cache the status state in the event
structure that causes the dequeue}. ROSS's reverse computation semantics ensures
that this event will be reversed before the completion events of any of the
other asynchronous events, allowing us to easily recover the state. Furthermore,
events are garbage-collected as the GVT, reducing memory management complexity.
However, this strategy has the disadvantage of increasing event size
accordingly.

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\section{Best practices quick reference}

NOTE: these may be integrated with the remaining notes or used as a summary of
section(s).

\subsection{ROSS simulation development}

\begin{enumerate}

    \item prefer fine-grained, simple LPs to coarse-grained, complex LPs
    \begin{enumerate}
        \item can simplify both LP state and reverse computation implementation
        \item ROSS is very good at event processing, likely small difference in
            performance
    \end{enumerate}

    \item consider separating single-source generation of concurrent events with
        "feedback" events or "continue" events to self
    \begin{enumerate}
        \item generating multiple concurrent events makes rollback more difficult
    \end{enumerate}

    \item use dummy events to work around "event-less" advancement of simulation time 

    \item add a small amount of time "noise" to events to prevent ties

    \item prefer more and smaller events to fewer and larger events
    \begin{enumerate}
        \item simplifies individual event processing
        \item ROSS uses bounded event structure size in communication, so
            smaller bound $\rightarrow$  less communication overhead
    \end{enumerate}

    \item prefer placing state in event structure to LP state structure
    \begin{enumerate}
        \item simplifies reverse computation -- less persistent state
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        \item NOTE: tradeoff with previous point - consider efficiency vs.\
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            complexity
    \end{enumerate}

    \item try to implement event processing with only LP-local information
    \begin{enumerate}
        \item reverse computation with collective knowledge is difficult
    \end{enumerate}

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    \item for optimistic-mode-capable tracking of multiple asynchronous event
        dependencies, cache status in the event state signifying the last
        satisfied dependency to ease reverse computation

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\end{enumerate}

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\section{TODO}

\begin{itemize}
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    \item reference to ROSS user's guide, airport model, etc.
    \item add code examples?
    \item techniques for exchanging events across LP types (API tips)
    \item add codes-mapping overview
    \item add more content on reverse computation. Specifically, development
        strategies using it, tips on testing, common issues that come up, etc.
    \item put a pdf or latex2html version of this document on the codes web page
        when it's ready
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    \item use msg\_header at the top of all message structs
        \begin{itemize}
            \item makes debugging a lot easier if they share the same first few fields
        \end{itemize}
    \item use different starting values for event type enums - along with
        previous point, helps determine originating LP message 
    \item use self suspend (this deserves its own section)
    \item separate register / configure functions for LPs
        \begin{itemize}
            \item need to add lp type struct prior to codes\_mapping\_setup,
                and it is often useful for LP-specific configuration to have
                access to codes-mapping functionsk
                \item especially needed for global config schemes with multiple
                    annotations - need the annotations provided by
                    codes-mapping, configuration APIs to know what fields to
                    look for
        \end{itemize}
    \item lp-io
        \begin{itemize}
            \item use command-line to configure turning io on and off, and
                where (dir) to place output. Use LP-specific options in the
                configuration file to drive specific options for output within
                the LP
            \item suggested command line options
                \begin{itemize}
                    \item "--lp-io-dir=DIR" : use DIR as the directory -
                        absence of option indicates no lp-io output
                    \item "--lp-io-use-suffix=DUMMY" : add the PID of the root
                        rank to the directory name to avoid clashes between
                        multiple runs. If not specified, then the DIR option
                        will be exactly used, possibly leading to an assert.
                \end{itemize}
        \end{itemize}

    \item dealing with simulations with many 'destructive' operations and
        mutable state (esp. state used/reset in multiple event sequences)
          \begin{itemize}
              \item use self-suspend liberally!!!
              \item consider the *entire* sequence of events that affect a
                  piece of mutable/destructible state, esp. from different LPs.
                  You can get an event from the future on state that you've
                  rolled back, for example, or multiple equivalent events that
                  differ only in timestamp (e.g., event to remote -> roll back
                  -> event to remote)
          \end{itemize}
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\end{itemize}

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\begin{comment} ==== SCRATCH MATERIAL ====
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\begin{figure}
\begin{lstlisting}[caption=Example code snippet., label=snippet-example]
for (i=0; i<n; i++) {
    for (j=0; j<i; j++) {
        /* do something */
    }
}
\end{lstlisting}
\end{figure}

Figure ~\ref{fig:snippet-example} shows an example of how to show a code
snippet in latex.  We can use this format as needed throughout the document.
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\end{comment}
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\end{document}