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Introduction
############

This guide describes the internal architecture of the OpenMM library.  It is
targeted at developers who want to add features to OpenMM, either by modifying
the core library directly or by writing plugins.  If you just want to write
applications that use OpenMM, you do not need to read this guide; the Users
Manual tells you everything you need to know.  This guide is *only* for
people who want to contribute to OpenMM itself.

It is organized as follows:

* Chapter :ref:`the-core-library` describes the architecture of the core OpenMM library.  It
  discusses how the high level and low level APIs relate to each other, and the
  flow of execution between them.
* Chapter :ref:`writing-plugins` describes in detail how to write a plugin.  It focuses on the two
  most common types of plugins: those which define new Forces, and those which
  implement new Platforms.
* Chapter :ref:`the-reference-platform` discusses the architecture of the reference Platform, providing
  information relevant to writing reference implementations of new features.
* Chapter :ref:`the-cpu-platform` discusses the architecture of the CPU Platform, providing
  information relevant to writing CPU implementations of new features.
* Chapter :ref:`the-opencl-platform` discusses the architecture of the OpenCL Platform, providing
  information relevant to writing OpenCL implementations of new features.
* Chapter :ref:`the-cuda-platform` discusses the architecture of the CUDA Platform, providing
  information relevant to writing CUDA implementations of new features.
* Chapter :ref:`common-compute` describes the Common Compute framework, which lets you
  write a single implementation of a feature that can be used for both OpenCL and CUDA.


This guide assumes you are already familiar with the public API and how to use
OpenMM in applications.  If that is not the case, you should first read the
Users Manual and work through some of the example programs.  Pay especially
close attention to the “Introduction to the OpenMM Library” chapter, since it
introduces concepts that are important in understanding this guide.


.. _the-core-library:

The Core Library
################

OpenMM is based on a layered architecture, as shown in the following diagram:

.. figure:: ../images/ArchitectureLayers.jpg
   :align: center
   :width: 100%

   :autonumber:`Figure,Architecture Layers`\ : OpenMM architecture

The public API layer consists of the classes you access when using OpenMM in an
application: System; Force and its subclasses; Integrator and its subclasses;
and Context.  These classes define a public interface but do no computation.

The next layer down consists of “implementation” classes that mirror the public
API classes: ContextImpl, ForceImpl, and a subclass of ForceImpl for each
subclass of Force (HarmonicBondForceImpl, NonbondedForceImpl, etc.).  These
objects are created automatically when you create a Context.  They store
information related to a particular simulation, and define methods for
performing calculations.

Note that, whereas a Force is logically “part of” a System, a ForceImpl is
logically “part of” a Context.  (See :autonumref:`Figure,API Relationships`\ .)  If you create many Contexts
for simulating the same System, there is still only one System and only one copy
of each Force in it.  But there will be separate ForceImpls for each Context,
and those ForceImpls store information related to their particular Contexts.


.. figure:: ../images/SystemContextRelationships.jpg
   :align: center

   :autonumber:`Figure,API Relationships`\ : Relationships between public API and implementation layer objects

Also note that there is no “IntegratorImpl” class, because it is not needed.
Integrator is already specific to one Context.  Many Contexts can all simulate
the same System, but each of them must have its own Integrator, so information
specific to one simulation can be stored directly in the Integrator.

The next layer down is the OpenMM Low Level API (OLLA).  The important classes
in this layer are: Platform; Kernel; KernelImpl and its subclasses; and
KernelFactory.  A Kernel is just a reference counted pointer to a KernelImpl;
the real work is done by KernelImpl objects (or more precisely, by instances of
its subclasses).  A KernelFactory creates KernelImpl objects, and a Platform
ties together a set of KernelFactories, as well as defining information that
applies generally to performing computations with that Platform.

All of these classes (except Kernel) are abstract.  A particular Platform
provides concrete subclasses of all of them.  For example, the reference
platform defines a Platform subclass called ReferencePlatform, a KernelFactory
subclass called ReferenceKernelFactory, and a concrete subclass of each abstract
KernelImpl type: ReferenceCalcNonbondedForceKernel extends
CalcNonbondedForceKernel (which in turn extends KernelImpl),
ReferenceIntegrateVerletStepKernel extends IntegrateVerletStepKernel, and so on.

We can understand this better by walking through the entire sequence of events
that takes place when you create a Context.  As an example, suppose you create a
System; add a NonbondedForce to it; create a VerletIntegrator; and then create a
Context for them using the reference Platform.  Here is what happens.

#. The Context constructor creates a ContextImpl.
#. The ContextImpl calls :code:`createImpl()` on each Force in the System,
   which creates an instance of the appropriate ForceImpl subclass.
#. The ContextImpl calls :code:`contextCreated()` on the Platform(), which
   in turn calls :code:`setPlatformData()` on the ContextImpl.  This allows
   Platform-specific information to be stored in a ContextImpl.  Every Platform has
   its own mechanism for storing particle masses, constraint definitions, particle
   positions, and so on.  ContextImpl therefore allows the Platform to create an
   arbitrary block of data and store it where it can be accessed by that Platform’s
   kernels.
#. The ContextImpl  calls :code:`createKernel()` on the Platform several
   times to get instances of various kernels that it needs:
   CalcKineticEnergyKernel, ApplyConstraintsKernel, etc.

   #. For each kernel, the Platform looks up which KernelFactory has been
      registered for that particular kernel.  In this case, it will be a
      ReferenceKernelFactory.
   #. It calls :code:`createKernelImpl()` on the KernelFactory, which
      creates and returns an instance of an appropriate KernelImpl subclass:
      ReferenceCalcKineticEnergyKernel, ReferenceApplyConstraintsKernel, etc.

#. The ContextImpl loops over all of its ForceImpls and calls 
   :code:`initialize()` on each one.

   #. Each ForceImpl asks the Platform to create whatever kernels it needs.  In
      this example, NonbondedForceImpl will request a CalcNonbondedForceKernel, and
      get back a ReferenceCalcNonbondedForceKernel.

#. The ContextImpl calls :code:`initialize()` on the Integrator which, like
   the other objects, requests kernels from the Platform.  In this example,
   VerletIntegrator requests an IntegrateVerletStepKernel and gets back a
   ReferenceIntegrateVerletStepKernel.


At this point, the Context is fully initialized and ready for doing computation.
Reference implementations of various KernelImpls have been created, but they are
always referenced through abstract superclasses.  Similarly, data structures
specific to the reference Platform have been created and stored in the
ContextImpl, but the format and content of these structures is opaque to the
ContextImpl.  Whenever it needs to access them (for example, to get or set
particle positions), it does so through a kernel (UpdateStateDataKernel in this
case).

Now suppose that you call :code:`step()` on the VerletIntegrator.  Here is
what happens to execute each time step.

#. The VerletIntegrator calls :code:`updateContextState()` on the
   ContextImpl.  This gives each Force an opportunity to modify the state of the
   Context at the start of each time step.

   #. The ContextImpl loops over its ForceImpls and calls 
      :code:`updateContextState()` on each one.  In this case, our only ForceImpl is
      a NonbondedForceImpl, which returns without doing anything.  On the other hand,
      if we had an AndersenThermostat in our System, its ForceImpl would invoke a
      kernel to modify particle velocities.

#. The VerletIntegrator calls :code:`calcForcesAndEnergy()` on the
   ContextImpl to request that the forces be computed.

   #. The ContextImpl calls :code:`beginComputation()` on its
      CalcForcesAndEnergyKernel.  This initializes all the forces to zero and does any
      other initialization the Platform requires before forces can be computed.  For
      example, some Platforms construct their nonbonded neighbor lists at this point.
   #. The ContextImpl loops over its ForceImpls and calls 
      :code:`calcForcesAndEnergy()` on each one.  In this case, we have a
      NonbondedForceImpl which invokes its CalcNonbondedForceKernel to compute forces.
   #. Finally, the ContextImpl calls :code:`finishComputation()` on its
      CalcForcesAndEnergyKernel.  This does any additional work needed to determine
      the final forces, such as summing the values from intermediate buffers.

#. Finally, the VerletIntegrator invokes its IntegrateVerletStepKernel.  This
   takes the forces, positions, and velocities that are stored in a Platform-
   specific format in the ContextImpl, uses them to compute new positions and
   velocities, and stores them in the ContextImpl.


.. _writing-plugins:

Writing Plugins
###############

A plugin is a dynamic library that adds new features to OpenMM.  It is typically
stored in the :code:`lib/plugins` directory inside your OpenMM installation,
and gets loaded along with all other plugins when the user calls
::

    Platform::loadPluginsFromDirectory(Platform::getDefaultPluginsDirectory());

It is also possible to load plugins from a different directory, or to load them
individually by calling :code:`Platform::loadPluginLibrary()`\ .

Every plugin must implement two functions that are declared in the
PluginInitializer.h header file:
::

    extern "C" void registerPlatforms();
    extern "C" void registerKernelFactories();

When a plugin is loaded, these two functions are invoked to register any
Platforms and KernelFactories defined by the plugin.  When many plugins are
loaded at once by calling :code:`Platform::loadPluginsFromDirectory()`\ , 
:code:`registerPlatforms()` is first called on all of them, then 
:code:`registerKernelFactories()` is called on all of them.  This allows one
plugin to define a Platform, and a different plugin to add KernelFactories to
it; the Platform is guaranteed to be registered by the first plugin before the
second plugin tries to add its KernelFactories, regardless of what order the
plugins happen to be loaded in.

Creating New Platforms
**********************

One common type of plugin defines a new Platform.  There are four such plugins
that come with OpenMM: one for the Reference platform, one for the CPU Platform,
one for the CUDA Platform, and one for the OpenCL Platform.

To define a new Platform, you must create subclasses of the various abstract
classes in the OpenMM Low Level API: a subclass of Platform, one or more
subclasses of KernelFactory, and a subclass of each KernelImpl.  That is easy to
say, but a huge amount of work to actually do.  There are many different
algorithms involved in computing forces, enforcing constraints, performing
integration, and so on, all of which together make up a Platform.  Of course,
there is no requirement that every Platform must implement every possible
feature.  If you do not provide an implementation of a particular kernel, it
simply means your Platform cannot be used for any simulation that requires that
kernel; if a user tries to do so, an exception will be thrown.

Your plugin’s :code:`registerPlatforms()` function should create an instance
of your Platform subclass, then register it by calling 
:code:`Platform::registerPlatform()`\ .  You also must register the
KernelFactory for each kernel your Platform supports.  This can be done in the 
:code:`registerKernelFactories()` function, or more simply, directly in the
Platform’s constructor.  You can use as many different KernelFactories as you
want for different kernels, but usually it is simplest to use a single
KernelFactory for all of them.  The support for multiple KernelFactories exists
primarily to let plugins add new features to existing Platforms, as described in
the next section.

Creating New Forces
*******************

Another common type of plugin defines new Forces and provides implementations of
them for existing Platforms.  (Defining new Integrators is not specifically
discussed here, but the process is very similar.)  There are two such plugins
that come with OpenMM.  They implement the AMOEBA force field and Drude
oscillators, respectively.

As an example, suppose you want to create a new Force subclass called
StringForce that uses the equations of String Theory to compute the interactions
between particles.  You want to provide implementations of it for all four
standard platforms: Reference, CPU, CUDA, and OpenCL.

The first thing to realize is that this *cannot* be done with only a plugin
library.  Plugins are loaded dynamically at runtime, and they relate to the low
level API; but you must also provide a public API.  Users of your class need to
create StringForce objects and call methods on them.  That means providing a
header file with the class declaration, and a (non-plugin) library with the
class definition to link their code against.  The implementations for particular
Platforms can be in plugins, but the public API class itself cannot.  Or to put
it differently, the full “plugin” (from the user’s perspective) consists of
three parts: the library OpenMM loads at runtime (which is what OpenMM considers
to be the “plugin”), a second library for users to link their code against, and
a header file for them to include in their source code.

To define the API, you will need to create the following classes:

#. StringForce.  This is the public API for your force, and users will directly
   link against the library containing it.
#. StringForceImpl.  This is the ForceImpl subclass corresponding to
   StringForce.  It should be defined in the same library as StringForce, and
   StringForce’s :code:`createImpl()` method should create an instance of it.
#. CalcStringForceKernel.  This is an abstract class that extends KernelImpl,
   and defines the API by which StringForceImpl invokes its kernel.  You only need
   to provide a header file for it, not an implementation; those will be provided
   by Platforms.


Now suppose you are writing the OpenCL implementation of StringForce.  Here are
the classes you need to write:

#. OpenCLCalcStringForceKernel.  This extends CalcStringForceKernel and provides
   implementations of its virtual methods.  The code for this class will probably
   be very complicated (and if it actually works, worth a Nobel Prize).  It may
   execute many different GPU kernels and create its own internal data structures.
   But those details are entirely internal to your own code.  As long as this class
   implements the virtual methods of CalcStringForceKernel, you can do anything you
   want inside it.
#. OpenCLStringForceKernelFactory.  This is a KernelFactory subclass that knows
   how to create instances of OpenCLCalcStringForceKernel.


Both of these classes should be packaged into a dynamic library (.so on Linux,
.dylib on Mac, .dll on Windows) that can be loaded as a plugin.  This library
must also implement the two functions from PluginInitializer.h. 
:code:`registerPlatforms()` will do nothing, since this plugin does not
implement any new Platforms.  :code:`registerKernelFactories()` should call
\ :code:`Platform::getPlatformByName("OpenCL")` to get the OpenCL Platform,
then create a new OpenCLStringForceKernelFactory and call 
:code:`registerKernelFactory()` on the Platform to register it.  If the OpenCL
Platform is not available, you should catch the exception then return without
doing anything.  Most likely this means there is no OpenCL runtime on the
computer your code is running on.


.. _the-reference-platform:

The Reference Platform
######################

The reference Platform is written with simplicity and clarity in mind, not
performance.  (It is still not always as simple or clear as one might hope, but
that is the goal.)  When implementing a new feature, it is recommended to create
the reference implementation first, then use that as a model for the versions in
other Platforms.

When using the reference Platform, the “platform-specific data” stored in
ContextImpl is of type ReferencePlatform::PlatformData, which is declared in
ReferencePlatform.h.  It has fields for storing positions, velocities, box
vectors, and other types of data.

The PlatformData’s vector of forces contains one element for each particle.  At
the start of each force evaluation, all elements of it are set to zero.  Each
Force adds its own contributions to the vector, so that at the end, it contains
the total force acting on each particle.

There are a few additional classes that contain useful static methods.
SimTKOpenMMUtilities has various utility functions, of which the most important
is a random number generator.  ReferenceForce provides methods for calculating
the displacement between two positions, optionally taking periodic boundary
conditions into account.


.. _the-cpu-platform:

The CPU Plaform
###############

CpuPlatform is a subclass of ReferencePlatform.  It provides optimized versions
of a small number of kernels, while using the reference implementations for all
the others.  Any kernel implementation written for the reference Platform will
work equally well with the CPU platform.  Of course, if that kernel happens to
be a performance bottleneck, you will probably want to write an optimized
version of it.  But many kernels have negligible effect on performance, and for
these you can just use the same implementation for both platforms.

If you choose to do that, you can easily support both platforms with a single
plugin library.  Just implement :code:`registerKernelFactories()` like this:
::

    extern "C" void registerKernelFactories() {
        for (int i = 0; i < Platform::getNumPlatforms(); i++) {
            Platform& platform = Platform::getPlatform(i);
            if (dynamic_cast<ReferencePlatform*>(&platform) != NULL) {
                // Create and register your KernelFactory.
            }
        }
    }

The loop identifies every ReferencePlatform, either an instance of the base
class or of a subclass, and registers a KernelFactory for every one.


.. _the-opencl-platform:

The OpenCL Platform
###################

The OpenCL Platform is much more complicated than the reference Platform.  It
also provides many more tools to simplify your work, but those tools themselves
can be complicated to use correctly.  This chapter will attempt to explain how
to use some of the most important ones.  It will *not* teach you how to
program with OpenCL.  There are many tutorials on that subject available
elsewhere, and this guide assumes you already understand it.

Overview
********

When using the OpenCL Platform, the “platform-specific data” stored in
ContextImpl is of type OpenCLPlatform::PlatformData, which is declared in
OpenCLPlatform.h.  The most important field of this class is :code:`contexts`
, which is a vector of OpenCLContexts.  (There is one OpenCLContext for each
device you are using.  The most common case is that you are running everything
on a single device, in which case there will be only one OpenCLContext.
Parallelizing computations across multiple devices is not discussed here.)  The
OpenCLContext stores most of the important information about a simulation:
positions, velocities, forces, an OpenCL CommandQueue used for executing
kernels, workspace buffers of various sorts, etc.  It provides many useful
methods for compiling and executing kernels, clearing and reducing buffers, and
so on.  It also provides access to three other important objects: the
OpenCLIntegrationUtilities, OpenCLNonbondedUtilities, and OpenCLBondedUtilities.
These are discussed below.

Allocation of device memory is generally done through the OpenCLArray class.  It
takes care of much of the work of memory management, and provides a simple
interface for transferring data between host and device memory.

Every kernel is specific to a particular OpenCLContext, which in turn is
specific to a particular OpenMM::Context.  This means that kernel source code
can be customized for a particular simulation.  For example, values such as the
number of particles can be turned into compile-time constants, and specific
versions of kernels can be selected based on the device being used or on
particular aspects of the system being simulated.  
:code:`OpenCLContext::createProgram()` makes it easy to specify a list of
preprocessor definitions to use when compiling a kernel.

The normal way to execute a kernel is by calling :code:`executeKernel()` on
the OpenCLContext.  It allows you to specify the total number of work-items to
execute, and optionally the size of each work-group.  (If you do not specify a
work-group size, it uses 64 as a default.)  The number of work-groups to launch
is selected automatically based on the work-group size, the total number of
work-items, and the number of compute units in the device it will execute on.

Numerical Precision
*******************

The OpenCL platform supports three precision modes:

#. **Single**\ : All values are stored in single precision, and nearly all
   calculations are done in single precision.  The arrays of positions, velocities,
   forces, and energies (returned by the OpenCLContext’s :code:`getPosq()`\ , 
   :code:`getVelm()`\ , :code:`getForce()`\ , :code:`getForceBuffers()`\ , and
   :code:`getEnergyBuffer()` methods) are all of type :code:`float4` (or 
   :code:`float` in the case of :code:`getEnergyBuffer()`\ ).
#. **Mixed**\ : Forces are computed and stored in single precision, but
   integration is done in double precision.  The velocities have type 
   :code:`double4`\ .  The positions are still stored in single precision to avoid
   adding overhead to the force calculations, but a second array of type 
   :code:`float4` is created to store “corrections” to the positions (returned by
   the OpenCLContext’s getPosqCorrection() method).  Adding the position and the
   correction together gives the full double precision position.
#. **Double**\ : Positions, velocities, forces, and energies are all stored in
   double precision, and nearly all calculations are done in double precision.


You can call :code:`getUseMixedPrecision()` and 
:code:`getUseDoublePrecision()` on the OpenCLContext to determine which mode
is being used.  In addition, when you compile a kernel by calling 
:code:`createKernel()`\ , it automatically defines two types for you to make it
easier to write kernels that work in any mode:

#. :code:`real` is defined as :code:`float` in single or mixed precision
   mode, :code:`double` in double precision mode.
#. :code:`mixed` is defined as :code:`float` in single precision mode, 
   :code:`double` in mixed or double precision mode.


It also defines vector versions of these types (\ :code:`real2`\ , 
:code:`real4`\ , etc.).

.. _computing-forces:

Computing Forces
****************

When forces are computed, they can be stored in either of two places.  There is
an array of :code:`long` values storing them as 64 bit fixed point values, and
a collection of buffers of :code:`real4` values storing them in floating point
format.  Most GPUs support atomic operations on 64 bit integers, which allows
many threads to simultaneously record forces without a danger of conflicts.
Some low end GPUs do not support this, however, especially the embedded GPUs
found in many laptops.  These devices write to the floating point buffers, with
careful coordination to make sure two threads will never write to the same
memory location at the same time.

At the start of a force calculation, all forces in all buffers are set to zero.
Each Force is then free to add its contributions to any or all of the buffers.
Finally, the buffers are summed to produce the total force on each particle.
The total is recorded in both the floating point and fixed point arrays.

The size of each floating point buffer is equal to the number of particles, rounded up to the
next multiple of 32.  Call :code:`getPaddedNumAtoms()` on the OpenCLContext
to get that number.  The actual force buffers are obtained by calling 
:code:`getForceBuffers()`\ .  The first *n* entries (where *n* is the
padded number of atoms) represent the first force buffer, the next *n*
represent the second force buffer, and so on.  More generally, the *i*\ ’th
force buffer’s contribution to the force on particle *j* is stored in
element :code:`i*context.getPaddedNumAtoms()+j`\ .

The fixed point buffer is ordered differently.  For atom *i*\ , the x component
of its force is stored in element :code:`i`\ , the y component in element 
:code:`i+context.getPaddedNumAtoms()`\ , and the z component in element 
:code:`i+2*context.getPaddedNumAtoms()`\ .  To convert a value from floating
point to fixed point, multiply it by 0x100000000 (2\ :sup:`32`\ ),
then cast it to a :code:`long`\ .  Call :code:`getLongForceBuffer()` to get the
array of fixed point values.

The potential energy is also accumulated in a set of buffers, but this one is
simply a list of floating point values.  All of them are set to zero at the
start of a computation, and they are summed at the end of the computation to
yield the total energy.

The OpenCL implementation of each Force object should define a subclass of
ComputeForceInfo, and register an instance of it by calling :code:`addForce()` on
the OpenCLContext.  It implements methods for determining whether particular
particles or groups of particles are identical.  This is important when
reordering particles, and is discussed below.


Nonbonded Forces
****************

Computing nonbonded interactions efficiently is a complicated business in the
best of cases.  It is even more complicated on a GPU.  Furthermore, the
algorithms must vary based on the type of processor being used, whether there is
a distance cutoff, and whether periodic boundary conditions are being applied.

The OpenCLNonbondedUtilities class tries to simplify all of this.  To use it you
need provide only a piece of code to compute the interaction between two
particles.  It then takes responsibility for generating a neighbor list, looping
over interacting particles, loading particle parameters from global memory, and
writing the forces and energies to the appropriate buffers.  All of these things
are done using an algorithm appropriate to the processor you are running on and
high level aspects of the interaction, such as whether it uses a cutoff and
whether particular particle pairs need to be excluded.

Of course, this system relies on certain assumptions, the most important of
which is that the Force can be represented as a sum of independent pairwise
interactions.  If that is not the case, things become much more complicated.
You may still be able to use features of OpenCLNonbondedUtilities, but you
cannot use the simple mechanism outlined above.  That is beyond the scope of
this guide.

To define a nonbonded interaction, call :code:`addInteraction()` on the
OpenCLNonbondedUtilities, providing a block of OpenCL source code for computing
the interaction.  This block of source code will be inserted into the middle of
an appropriate kernel.  At the point where it is inserted, various variables
will have been defined describing the interaction to compute:

#. :code:`atom1` and :code:`atom2` are the indices of the two
   interacting particles.
#. :code:`r`\ , :code:`r2`\ , and :code:`invR` are the distance *r*
   between the two particles, *r*\ :sup:`2`\ , and 1/\ *r* respectively.
#. :code:`isExcluded` is a :code:`bool` specifying whether this pair of
   particles is marked as an excluded interaction.  (Excluded pairs are not skipped
   automatically, because in some cases they still need to be processed, just
   differently from other pairs.)
#. :code:`posq1` and :code:`posq2` are :code:`real4`\ s containing the
   positions (in the xyz fields) and charges (in the w fields) of the two
   particles.
#. Other per-particle parameters may be specified, as described below.


The following preprocessor macros will also have been defined:

#. :code:`NUM_ATOMS` is the total number of particles in the system.
#. :code:`PADDED_NUM_ATOMS` is the padded number of particles in the system.
#. :code:`USE_CUTOFF` is defined if and only if a cutoff is being used
#. :code:`USE_PERIODIC` is defined if and only if periodic boundary
   conditions are being used.
#. :code:`CUTOFF` and :code:`CUTOFF_SQUARED` are the cutoff distance and
   its square respectively (but only defined if a cutoff is being used).


Finally, two output variables will have been defined:

#. You should add the energy of the interaction to :code:`tempEnergy`\ .
#. You should add the derivative of the energy with respect to the inter-particle
   distance to :code:`dEdR`\ .


You can also define arbitrary per-particle parameters by calling 
:code:`addParameter()` on the OpenCLNonbondedUtilities.  You provide an array
in device memory containing the set of values, and the values for the two
interacting particles will be loaded and stored into variables called 
:code:`<name>1` and :code:`<name>2`\ , where <name> is the name you specify
for the parameter.  Note that nonbonded interactions are not computed until
after :code:`calcForcesAndEnergy()` has been called on every ForceImpl, so
it is possible to make the parameter values change with time by modifying them
inside :code:`calcForcesAndEnergy()`\ .  Also note that the length of the
array containing the parameter values must equal the *padded* number of
particles in the system.

Finally, you can specify arbitrary other memory objects that should be passed as
arguments to the interaction kernel by calling :code:`addArgument()`\ .  The
rest of the kernel ignores these arguments, but you can make use of them in your
interaction code.

Consider a simple example.  Suppose we want to implement a nonbonded interaction
of the form *E*\ =\ *k*\ :sub:`1`\ *k*\ :sub:`2`\ *r*\ :sup:`2`\ ,
where *k* is a per-particle parameter.  First we create a parameter as
follows
::

    nb.addParameter(ComputeParameterInfo(kparam, "kparam", "float", 1));

where :code:`nb` is the OpenCLNonbondedUtilities for the context.  Now we
call :code:`addInteraction()` to define an interaction with the following
source code:
::

    #ifdef USE_CUTOFF
    if (!isExcluded && r2 < CUTOFF_SQUARED) {
    #else
    if (!isExcluded) {
    #endif
        tempEnergy += kparam1*kparam2*r2;
        dEdR += 2*kparam1*kparam2*r;
    }

An important point is that this code is executed for every pair of particles in
the *padded* list of atoms.  This means that some interactions involve
padding atoms, and should not actually be included.  You might think, then, that
the above code is incorrect and we need another check to filter out the extra
interactions:
::

    if (atom1 < NUM_ATOMS && atom2 < NUM_ATOMS)

This is not necessary in our case, because the :code:`isExcluded` flag is
always set for interactions that involve a padding atom.  If our force did not
use excluded interactions (and so did not check :code:`isExcluded`\ ), then we
would need to add this extra check.  Self interactions are a similar case: we do
not check for :code:`(atom1 == atom2)` because the exclusion flag prevents
them from being processed, but for some forces that check is necessary.

Bonded Forces
*************

Just as OpenCLNonbondedUtilities simplifies the task of creating nonbonded
interactions, OpenCLBondedUtilities simplifies the process for many types of
bonded interactions.  A “bonded interaction” means one that is applied to small,
fixed groups of particles.  This includes bonds, angles, torsions, etc.  The
important point is that the list of particles forming a “bond” is known in
advance and does not change with time.

Using OpenCLBondedUtilities is very similar to the process described above.  You
provide a block of OpenCL code for evaluating a single interaction.  This block
of code will be inserted into the middle of a kernel that loops over all
interactions and evaluates each one.  At the point where it is inserted, the
following variables will have been defined describing the interaction to
compute:

#. :code:`index` is the index of the interaction being evaluated.
#. :code:`atom1`\ , :code:`atom2`\ , ... are the indices of the interacting
   particles.
#. :code:`pos1`\ , :code:`pos2`\ , ... are :code:`real4`\ s containing the
   positions (in the xyz fields) of the interacting particles.


A variable called :code:`energy` will have been defined for accumulating the
total energy of all interactions.  Your code should add the energy of the
interaction to it.  You also should define :code:`real4` variables called 
:code:`force1`\ , :code:`force2`\ , ... and store the force on each atom into
them.

As a simple example, the following source code implements a pairwise interaction
of the form *E*\ =\ *r*\ :sup:`2`\ :
::

    real4 delta = pos2-pos1;
    energy += delta.x*delta.x + delta.y*delta.y + delta.z*delta.z;
    real4 force1 = 2.0f*delta;
    real4 force2 = -2.0f*delta;

To use it, call :code:`addInteraction()` on the Context’s
OpenCLBondedUtilities object.  You also provide a list of the particles involved
in every bonded interaction.

Exactly as with nonbonded interactions, you can call :code:`addArgument()`
to specify arbitrary memory objects that should be passed as arguments to the
interaction kernel.  These might contain per-bond parameters (use 
:code:`index` to look up the appropriate element) or any other information you
want.

Reordering of Particles
***********************

Nonbonded calculations are done a bit differently in the OpenCL Platform than in
most CPU based codes.  In particular, interactions are computed on blocks of 32
particles at a time (which is why the number of particles needs to be padded to
bring it up to a multiple of 32), and the neighbor list actually lists pairs of
\ *blocks*\ , not pairs of individual particles, that are close enough to
interact with each other.

This only works well if sequential particles tend to be close together so that
blocks are spatially compact.  This is generally true of particles in a
macromolecule, but it is not true for solvent molecules.  Each water molecule,
for example, can move independently of other water molecules, so particles that
happen to be sequential in whatever order the molecules were defined in need not
be spatially close together.

The OpenCL Platform addresses this by periodically reordering particles so that
sequential particles are close together.  This means that what the OpenCL
Platform calls particle *i* need not be the same as what the System calls
particle *i*\ .

This reordering is done frequently, so it must be very fast.  If all the data
structures describing the structure of the System and the Forces acting on it
needed to be updated, that would make it prohibitively slow.  The OpenCL
Platform therefore only reorders particles in ways that do not alter any part of
the System definition.  In practice, this means exchanging entire molecules; as
long as two molecules are truly identical, their positions and velocities can be
exchanged without affecting the System in any way.

Every Force can contribute to defining the boundaries of molecules, and to
determining whether two molecules are identical.  This is done through the
ComputeForceInfo it adds to the OpenCLContext.  It can specify two types of
information:

#. Given a pair of particles, it can say whether those two particles are
   identical (as far as that Force is concerned).  For example, a Force object
   implementing a Coulomb force would check whether the two particles had equal
   charges.
#. It can define *particle groups*\ .  The OpenCL Platform will ensure that
   all the particles in a group are part of the same molecule.  It also can specify
   whether two groups are identical to each other.  For example, in a Force
   implementing harmonic bonds, each group would consist of the two particles
   connected by a bond, and two groups would be identical if they had the same
   spring constants and equilibrium lengths.


Integration Utilities
*********************

The OpenCLContext’s OpenCLIntegrationUtilities provides features that are used
by many integrators.  The two most important are random number generation and
constraint enforcement.

If you plan to use random numbers, you should call 
:code:`initRandomNumberGenerator()` during initialization, specifying the
random number seed to use.  Be aware that there is only one random number
generator, even if multiple classes make use of it.  If two classes each call 
:code:`initRandomNumberGenerator()` and request different seeds, an exception
will be thrown.  If they each request the same seed, the second call will simply
be ignored.

For efficiency, random numbers are generated in bulk and stored in an array in
device memory, which you can access by calling :code:`getRandom()`\ .  Each
time you need to use a block of random numbers, call 
:code:`prepareRandomNumbers()`\ , specifying how many values you need.  It will
register that many values as having been used, and return the index in the array
at which you should start reading values.  If not enough unused values remain in
the array, it will generate a new batch of random values before returning.

To apply constraints, simply call :code:`applyConstraints()`\ .  For numerical
accuracy, the constraint algorithms do not work on particle positions directly,
but rather on the *displacements* taken by the most recent integration step.
These displacements must be stored in an array which you can get by calling 
:code:`getPosDelta()`\ .  That is, the constraint algorithms assume the actual
(unconstrained) position of each particle equals the position stored in the
OpenCLContext plus the delta stored in the OpenCLIntegrationUtilities.  It then
modifies the deltas so that all distance constraints are satisfied.  The
integrator must then finish the time step by adding the deltas to the positions
and storing them into the main position array.


.. _the-cuda-platform:

The CUDA Platform
#################

The CUDA platform is very similar to the OpenCL platform, and most of the
previous chapter applies equally well to it, just changing “OpenCL” to “Cuda” in
class names.  There are a few differences worth noting.

Compiling Kernels
*****************

Like the OpenCL platform, the CUDA platform compiles all its kernels at runtime.
Unlike OpenCL, CUDA does not have built in support for runtime compilation.
OpenMM therefore needs to implement this itself by writing the source code out
to disk, invoking the nvcc compiler as a separate process, and then loading the
compiled kernel in from disk.

For the most part, you can ignore all of this.  Just call 
:code:`createModule()` on the CudaContext, passing it the CUDA source code.
It takes care of the details of compilation and loading, returning a CUmodule
object when it is done.  You can then call :code:`getKernel()` to look up
individual kernels in the module (represented as CUfunction objects) and 
:code:`executeKernel()` to execute them.

The CUDA platform does need two things to make this work: a directory on disk
where it can write out temporary files, and the path to the nvcc compiler.
These are specified by the “CudaTempDirectory” and “CudaCompiler” properties
when you create a new Context.  It often can figure out suitable values for them
on its own, but sometimes it needs help.  See the “Platform-Specific Properties”
chapter of the Users Manual for details.

Accumulating Forces
*******************

The OpenCL platform, as described in Section :ref:`computing-forces`\ , uses two types of buffers for
accumulating forces: a set of floating point buffers, and a single fixed point
buffer.  In contrast, the CUDA platform uses *only* the fixed point buffer
(represented by the CUDA type :code:`long` :code:`long`\ ).  This means
the CUDA platform only works on devices that support 64 bit atomic operations
(compute capability 1.2 or higher).


.. _common-compute

Common Compute
##############

Common Compute is not a platform, but it shares many elements of one.  It exists
to reduce code duplication between the OpenCL and CUDA platforms.  It allows a
single implementation to be written for most kernels that can be used by both
platforms.

OpenCL and CUDA are very similar to each other.  Their computational models are
nearly identical.  For example, each is based around launching kernels that are
executed in parallel by many threads.  Each of them groups threads into blocks,
with more communication and synchronization permitted between the threads
in a block than between ones in different blocks.  They have very similar memory
hierarchies: high latency global memory, low latency local/shared memory that
can be used for communication between the threads of a block, and local variables
that are visible only to a single thread.

Even their languages for writing kernels are very similar.  Here is an OpenCL
kernel that adds two arrays together, storing the result in a third array.
::

    __kernel void addArrays(__global const float* restrict a,
                            __global const float* restrict b,
                            __global float* restrict c
                            int length) {
        for (int i = get_global_id(0); i < length; i += get_global_size(0))
            c[i] = a[i]+b[i];
    }

Here is the corresponding CUDA kernel.
::

    __extern "C" __global__ void addArrays(const float* __restrict__ a,
                                           const float* __restrict__ b,
                                           _float* __restrict__ c
                                           int length) {
        for (int i = blockIdx.x*blockDim.x+threadIdx.x; i < length; i += blockDim.x*gridDim.x)
            c[i] = a[i]+b[i];
    }

The difference between them is largely just a mechanical find-and-replace.
After many years of writing and maintaining nearly identical kernels by hand,
it finally occurred to us that the translation could be done automatically by
the compiler.  Simply by defining a few preprocessor macros, the following
kernel can be compiled equally well either as OpenCL or as CUDA.
::

    KERNEL void addArrays(GLOBAL const float* RESTRICT a,
                          GLOBAL const float* RESTRICT b,
                          GLOBAL float* RESTRICT c
                          int length) {
        for (int i = GLOBAL_ID; i < length; i += GLOBAL_SIZE)
            c[i] = a[i]+b[i];
    }

Writing Device Code
*******************

When compiling kernels with the Common Compute API, the following macros are
defined.

.. tabularcolumns:: |l|l|L|

+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|Macro                          |OpenCL Definition                                           |CUDA Definition                             |
+===============================+============================================================+============================================+
|:code:`KERNEL`                 |:code:`__kernel`                                            |:code:`extern "C" __global__`               |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`DEVICE`                 |                                                            |:code:`__device__`                          |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`LOCAL`                  |:code:`__local`                                             |:code:`__shared__`                          |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`LOCAL_ARG`              |:code:`__local`                                             |                                            |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`GLOBAL`                 |:code:`__global`                                            |                                            |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`RESTRICT`               |:code:`restrict`                                            |:code:`__restrict__`                        |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`LOCAL_ID`               |:code:`get_local_id(0)`                                     |:code:`threadIdx.x`                         |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`LOCAL_SIZE`             |:code:`get_local_size(0)`                                   |:code:`blockDim.x`                          |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`GLOBAL_ID`              |:code:`get_global_id(0)`                                    |:code:`(blockIdx.x*blockDim.x+threadIdx.x)` |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`GLOBAL_SIZE`            |:code:`get_global_size(0)`                                  |:code:`(blockDim.x*gridDim.x)`              |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`GROUP_ID`               |:code:`get_group_id(0)`                                     |:code:`blockIdx.x`                          |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`NUM_GROUPS`             |:code:`get_num_groups(0)`                                   |:code:`gridDim.x`                           |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`SYNC_THREADS`           |:code:`barrier(CLK_LOCAL_MEM_FENCE+CLK_GLOBAL_MEM_FENCE);`  |:code:`__syncthreads();`                    |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`SYNC_WARPS`             | | if SIMT width >= 32:                                     | | if compute capability >= 7.0:            |
|                               | | :code:`mem_fence(CLK_LOCAL_MEM_FENCE)`                   | | :code:`__syncwarp();`                    |
|                               | | otherwise:                                               | | otherwise empty                          |
|                               | | :code:`barrier(CLK_LOCAL_MEM_FENCE)`                     |                                            |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`MEM_FENCE`              |:code:`mem_fence(CLK_LOCAL_MEM_FENCE+CLK_GLOBAL_MEM_FENCE);`|:code:`__threadfence_block();`              |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+
|:code:`ATOMIC_ADD(dest, value)`|:code:`atom_add(dest, value)`                               |:code:`atomicAdd(dest, value)`              |
+-------------------------------+------------------------------------------------------------+--------------------------------------------+

A few other symbols may or may not be defined based on the device you are running on:
:code:`SUPPORTS_DOUBLE_PRECISION` and :code:`SUPPORTS_64_BIT_ATOMICS`\ .  You
can use :code:`#ifdef` blocks with these symbols to conditionally compile code
based on the features supported by the device.  In addition, the CUDA compiler
defines the symbol :code:`__CUDA_ARCH__`\ , so you can check for this symbol if
you want to have different code blocks for CUDA and OpenCL.

Both OpenCL and CUDA define vector types like :code:`int2` and :code:`float4`\ .
The types they support are different but overlapping.  When writing common code,
use only the vector types that are supported by both OpenCL and CUDA: 2, 3, and 4
element vectors of type :code:`short`\ , :code:`int`\ , :code:`float`\ , and
:code:`double`\ .

CUDA uses functions to construct vector values, such as :code:`make_float2(x, y)`\ .
OpenCL instead uses a typecast like syntax: :code:`(float2) (x, y)`\ .  In common
code, use the CUDA style :code:`make_` functions.  OpenMM provides definitions
of these functions when compiling as OpenCL.

In CUDA, vector types are simply data structures.  You can access their elements,
but not do much more with them.  In contrast, OpenCL's vectors are mathematical
types.  All standard math operators are defined for them, as well as geometrical
functions like :code:`dot()` and :code:`cross()`\ .  When compiling kernels as
CUDA, OpenMM provides definitions of these operators and functions.

OpenCL also supports "swizzle" notation for vectors.  For example, if :code:`f`
is a :code:`float4` you can construct a vector of its first three elements
by writing :code:`f.xyz`\ , or you can swap its first two elements by writing
:code:`f.xy = f.yx`\ .  Unfortunately, there is no practical way to support this
in CUDA, so swizzle notation cannot be used in common code.  Because stripping
the final element from a four component vector is such a common operation, OpenMM
provides a special function for doing it: :code:`trimTo3(f)` is a vector of its
first three elements.

64 bit integers are another data type that needs special handling.  Both OpenCL
and CUDA support them, but they use different names for them: :code:`long` in OpenCL,
:code:`long long` in CUDA.  To work around this inconsistency, OpenMM provides
the typedefs :code:`mm_long` and :code:`mm_ulong` for signed and unsigned 64 bit
integers in device code.

Writing Host Code
*****************

Host code for Common Compute is very similar to host code for OpenCL or CUDA.
In fact, most of the classes provided by the OpenCL and CUDA platforms are
subclasses of Common Compute classes.  For example, OpenCLContext and
CudaContext are both subclasses of ComputeContext.  When writing common code,
each KernelImpl should expect a ComputeContext to be passed to its constructor.
By using the common API provided by that abstract class, it can be used for
either OpenCL or CUDA just based on the particular context passed to it at
runtime.  Similarly, OpenCLNonbondedUtilities and CudaNonbondedUtilities are
subclasses of the abstract NonbondedUtilities class, and so on.

ArrayInterface is an abstract class defining the interface for arrays stored on
the device.  OpenCLArray and CudaArray are both subclasses of it.  To simplify
code that creates and uses arrays, there is also a third subclass called
ComputeArray.  It acts as a wrapper around an OpenCLArray or CudaArray,
automatically creating an array of the appropriate type for the current
platform.  In practice, just follow these rules:

  1. Whenever you need to create an array, make it a ComputeArray.

  2. Whenever you write a function that expects an array to be passed to it,
     declare the type to be ArrayInterface.

If you do these two things, all differences between platforms will be handled
automatically.

OpenCL and CUDA have quite different APIs for compiling and invoking kernels.
To hide these differences, OpenMM provides a set of abstract classes.  To compile
device code, pass the source code to :code:`compileProgram()` on the ComputeContext.
This returns a ComputeProgram.  You can then call its :code:`createKernel()`
method to get a ComputeKernel object, which has methods for setting arguments
and invoking the kernel.

Sometimes you need to refer to vector types in host code, such as to set the
value for a kernel argument or to access the elements of an array.  OpenCL and
CUDA both define types for them, but they have different names, and in any case
you want to avoid using OpenCL-specific or CUDA-specific types in common code.
OpenMM therefore defines types for vectors in host code.  They have the same
names as the corresponding types in device code, only with the prefix :code:`mm_`\ ,
for example :code:`mm_int2` and :code:`mm_float3`\ .