This code base will provide accessible, performant, interoperable libraries in a new family of MD programs. Basic operations such as coordinate and topology intake, user input parsing, and energy evaluations are managed through a common set of C++ classes and CUDA or HIP kernels. In many instances, HPC applications will be constructed using the same data arrays and layouts as their CPU counterparts, although the most efficient GPU code may require sacrifices in the convenience or performance of CPU code. These sacrifices are likely to be minor, however, as the best CPU code takes advantage of similar vectorization as GPU code and, even if vectorization cannot happen in the same manner, the contents and format of basic data arrays do not restrict the actual algorithm in any severe way.

---

Installation Instructions

Here are some basic instructions:

• Download the repository and unpack the source in /your/STORMM/source/dir/
• Set environment variables:

    STORMM_SOURCE  set to /your/STORMM/source/dir/
    STORMM_HOME    set to /your/STORMM/source/dir/
    STORMM_BUILD   set to /your/STORMM/build/dir/
    STORMM_VERBOSE set to COMPACT for concise testing output or FULL for more detailed results if
                   test programs are run manually (this can help to trace errors)

• Have CMake version 3.18 or greater, for CUDA compatibility.
• Make a new directory /your/STORMM/build/dir/ and go to it, then type cmake ${STORMM_SOURCE} -DCMAKE_BUILD_TYPE=RELEASE [ additional cmake variable definitions ] OR
• Execute cmake -b ${STORMM_BUILD} -s ${STORMM_SOURCE} -DCMAKE_BUILD_TYPE=RELEASE [ additional cmake variable definitions ]
• Go to the ${STORMM_BUILD} directory, if you are not already there.
• Type make -j to build with all possible resources.
• Type make test if the compilation is successful to run the test suite

There are several additional definitions that one can apply to the build. Each is provided with the cmake command prefaced with -D ("define"). They include:

• -DCMAKE_BUILD_TYPE with values RELEASE or DEBUG. The DEBUG version will eliminate optimizations present in the RELEASE version and engage debugging flags (that is -g to a C++ or Fortran compiler). We use valgrind a lot when tracing errors in STORMM development, and the debugging flags are essential to getting line numbers in functions that exhibit memory errors or other exceptions.
• -DSTORMM_ENABLE_CUDA with values ON or OFF (default OFF). This will toggle the STORMM installation between CUDA-enabled and CPU-only versions. The CPU-only version implements most features of the CUDA-enabled version, but some of the algorithms and precision models are not as expansive. The intent is that, in the long run, the CPU version will be able to replicate all of what the GPU does using equivalent methods, albeit more slowly.
• -DSTORMM_ENABLE_RDKIT with values ON or OFF (default OFF). This will enable RDKit to communicate with STORMM, but requires a valid RDKit installation and does not yet unlock any real functionality. Some features of STORMM are a stepping stone to information processing with RDKit, and some features of RDKit will enhance the capabilities on the STORMM roadmap.
• -DCUSTOM_GPU_ARCH with many values separated by semicolons (;) with no spaces. Apply whatever architectures will be necessary for the GPUs available. 52 will serve Maxwell GPUs such as the GTX 980, 61 will serve consumer-grade Pascal GPUs such as the GTX 1080 Ti, 75 will serve Turing GPUs such as T4 and the common RTX 2080 Ti, 86 will serve consumer-grade GPUs in the Ampere line (RTX 30xx (Ti), A40), 89 will serve consumer-grade GPUs in the Lovelace line (RTX 40xx (Ti)), and settings such as 60, 70, or 80 will serve X100 series data-center grade GPUs suchas GP100, V100 ("Volta"), and A100. The Hopper line (H100, input value 90) is not well tested and some features were encountering errors. The default behavior, triggered in the absence of -DCUSTOM_GPU_ARCH specification, is to set the native CMAKE_CUDA_ARCHITECTURES variable to 52;60;61;70;75;80;86;89. Trimming the list, particularly with regard to older GPU hardware, will reduce build times. The new variable CUSTOM_GPU_ARCH is provided because the native CMAKE_CUDA_ARCHITECTURES comes with a preset of 52 which will omit a lot of optimizations on more recent cards.
• -DCMAKE_SHOW_PTXAS with values ON or OFF and default OFF. This will cause the compiler to output --ptxas output to the terminal, which can be helpful in gauging kernel performance (in particular, regarding register usage). Setting this to ON will output 3-4 lines for every version of every kernel compiled, which can be a lot to parse as some of the lines are 240+ characters in length.
• -DSTORMM_ENABLE_TEST_COVERAGE which is undefined by default but can be defined with a valid installation of gcov to check the coverage of unit testing code during development.

---

Alternate Installation Instructions: Docker

You can also install STORMM using Docker as an alternative method. This allows you to run STORMM in a containerized environment without needing to set up your local environment. Follow the steps below to build and run the Docker container for STORMM.

Prerequisites

• Ensure you have Docker installed on your machine. You can download it from Docker's official website.

Building the Docker Image

1. Download the file named "Dockerfile" under ~/docker in this repository.

If you have a compatible GPU, please download the Dockerfile under STORMM-CONFIG.

If you do not have a compatible GPU, you can run STORMM on CPU only. Please download the Dockerfile under STORMM-CPU-ONLY-CONFIG.

You only need the relevant Dockerfile for this method, and do not need to clone the entire repository.

2. Navigate to the directory with the Dockerfile, and build a container:

   sudo docker build -t stormm-docker .

Running the Docker Container

3. Once the image is built, you can run the container:

   sudo docker run --gpus all -it stormm-docker

   • The --gpus all flag enables GPU access.
   • The -it flag allows you to interact with the container.

Note: Once you exit an active Docker session, the container is stopped. However, it is still in memory.

If docker run is executed, a new container (clone) will be created. Please follow the steps below to reuse the same container, instead of creating a new one.

Managing Docker Containers

Docker provides a way to reuse an existing container without creating a new one each time. Below are several methods to manage your Docker containers effectively:

1. Reuse an Existing Stopped Container

If you want to run a stopped container without creating a new one, you can restart the existing container.

Steps:

• List all containers (including stopped ones):

  sudo docker ps -a

• Find the container ID of the container that you want to reuse, and then start it. Container IDs are the first column, container name is the second ("stormm-docker", from the steps above):

  sudo docker start <container_id>

• Attach your CLI to the running container to interact with it:

  sudo docker exec -it <container_id> /bin/bash

2. [Optional] Using the --name Option

When you create a container with the --name flag, Docker assigns a specific name to the container. You can use that name to start and reuse the container.

Example:

• Create the container with a specific name:

  sudo docker run --gpus all -it --name stormm-container stormm-docker

• When you want to reuse the container, restart it:

  sudo docker start stormm-container

• Attach to it:

  sudo docker exec -it stormm-container /bin/bash

3. [Optional] Automatically Remove Containers After Execution

If you want to run a temporary container and automatically remove it after it finishes, you can use the --rm flag:

sudo docker run --rm --gpus all -it stormm-docker

This won't reuse the same container but ensures no leftover containers after running the command.

4. [Optional] Restart Policy for Automatic Restarts

You can set a restart policy to automatically start containers when they stop:

sudo docker run -dit --restart unless-stopped --name stormm-container stormm-docker

This ensures the container stays running, and you can attach to it as needed.

This Docker setup allows you to easily build and run STORMM in a containerized environment with GPU support, while also managing and maintaining your containers effectively.

5. Removing Unused Containers

To remove containers that are not currently in use, you can follow these steps:

As mentioned above, please use sudo docker ps -a to get the CONTAINER ID for your desired container.

1. Stop any running containers (if needed):

   sudo docker stop <container_name_or_id>

The containers are configured to stop automatically when you exit them, unless the steps under "Restart policy for Automatic Restarts" were followed.

Hence, this command is not required for graceful exits on the default config.

2. Remove a specific stopped container:

   sudo docker rm <container_name_or_id>

Note: While this command is useful in removing old or duplicate containers, if all containers have been removed, STORMM will have to be built in a new container from scratch.

3. Remove all stopped containers at once:

   To clean up your system by removing all stopped containers, you can use:

   sudo docker container prune

This command will prompt you for confirmation before removing all stopped containers, helping you to reclaim space on your system.

However, this would require a new STORMM container to be built from scratch.

---

Code Standards

• This code base will follow the C++17 standard, in particular for enum classes, member initializers, brace initialization for POD aggregates, deleted and defaulted functions, nullptr, constructor delegation, and key Standard Library algorithms. C++17 is comprehensible and compatible to NVIDIA's compilers. It subsumes the major C++11 increment in C++, will be insulated from deprecation for years to come, and remains a safe choice for hybrid programming.
• Developers should strive for a code feel much like C, with basic C++ features like templates, private or protected struct or class member variables with public getter functions, and often passing by reference rather than by pointer (in C++, type & rather than * in C). The code base should be familiar to a developer who understand functions, loops, structs, and pointers. Such a developer will need to learn (struct or class) member functions, enumerator class types, initialization of objects, templates, and function overloading. These concepts can be learned independently and may reinforce one another. The transition from intermediate C or python coding to writing C++ with STORMM is intended to be a steady learning curve.
• Iterators, ranged for loops, and unique pointers are among the features of modern C++ deemed to be further from the original C language than most programmers should have to go, and therefore seldom appear in the STORMM libraries themselves. Programmers writing backend or small programs that link to the STORMM libraries may use these features at their discretion.
• All dynamic memory intended to operate in a hybrid CPU / GPU environment should be allocated with Hybrid objects from the STORMM library. This class functions with many of the features of the STL vector container and interfaces with it for easy conversion to any function requiring that type.

---

Coding Conventions

• We're not doing this with a formatter! The goal is to get as much code on one screen as possible in a clear and legible fashion. Code formatters tend to insert excessive line breaks where they are not necessary or make it harder to skip lines where it would be nice to separate groups of statements. They also work against the concept of column uniformity in assignment operators, which can be helpful within a scope or a tightly connected group of statements.
• Max line width: 99 characters (100th must be a line break). This may be violated if a kernel launch cannot fit within the space, but in most cases names should be adjusted to avoid wrapping past the 100 character limit.
• Indent new scopes by two spaces.
• When continuing arithmetic statements on new lines, end each line with an arithmetic operator, which stands out as an indication that something must follow. Indent the next line as far as the assignment operator, i.e.:

    t  = x + y +
         z + w;
    t += (k * p) +
         (x / y) + 4;

• When writing arithmetic statements, add parentheses and group operations according to the order of operations. See the example above. Use spaces between each arithmetic operator and its operands, but do not add extra spaces between variables and left or right parentheses.
• When making arithmetic statements within index assignments, or with very short NON-STRUCT variable names, it is permissible to show the order of operations by cutting the spaces away from basic arithmetic operators.
• When continuing lines within parentheses, brackets, or braces, indent to the interior of the most recent open delimiter.
• Align assignment operators for a tightly connected series of statements, as shown in the code above.
• Use of auto-typing (type inference) is discouraged, to make the code as inviting and clear as possible for new developers.
• When writing conditional statements that branch the code path when a logical expression logic_expr is false, use (!logic_expr) when performance is critical. Use (logic_expr == false) in most other cases: this implies an extra comparison, whereas ! is merely arithmetic, but the (logic_expr == false) syntax is more legible when the meaning of the code is most important.
• When passing by reference or declaring a reference to an object, attach the & to the left side of the object name, rather than to the right side of the object name, i.e. int foo(MyClassType &mc) not int foo(MyClassType& mc) or int foo(MyClassType & mc). The middle choice can let the reference symbol get lost, while the final choice can be confused with the bitwise AND operator.
• When returning a reference from a function, attach the reference to the right side of the returned type, not to the left side of the function name.
• When printing particular formats of numbers, i.e. 0.0F or 7LL, use capital letters in particular to prevent lowercase "l" from being confused with one (1).

---

Abstraction and Dependencies

• Class inheritance, while powerful, has not yet been implemented for STORMM objects. The most useful and appropriate places would likely be among the various coordinate objects, but even there the practice would be of limited benefit as each of the differet coordinate objects are somewhat orthogonal. In general, STORMM development does not prohibit inheritance but does not require it, either.
• For including libraries, follow this order:
  • C++ Standard Libraries (#include <vector>), in alphabetical order
  • #include "copyright.h" (the copyright statement is found in the base source directory)
  • STORMM libraries (#include "Math/vector_ops.h"), in alphabetical order
• C++ implementation (.cpp) files should generally include their own corresponding header files. Any classes, functions, or other objects present in a header file should be traceable by some #include statement in that header file. Any classes, funcions, or other objects present in an implementation file should be traceable by additional #include statements in the implementation file itself, or in the relevant library header file.
• Place documentation for function usage in header (.h) files and implementation-relevant documentation in implementation (.cpp or .tpp) files. Place the implementations of template functions in template implementation (.tpp) files, not in the header file where the templated function or class template is first declared. The .tpp file should be included at the end of the corresponding header file, outside of any namespace scopes but within the header file's customary #include guards. Template files should re-declare the relevant namespace scopes to wrap their implementations, but not contain additional #include guards.

---

Naming Conventions

• Camel case (thisIsCamelCase) for function names
• Lowercase lettering with underscore separators (these_are_underscores) for variable names
• Uppercase lettering with underscore separators (THIS_IS_A_MACRO) for macro functions and macro definitions
• Capitalization of first letters (ThisCapitalizesAllFirstletters) for class names
• Use all uppercase letters for enumerations, with underscore separators (THIS_IS_AN_ENUMERATION). Avoid #define'd constants, preferring constexpr, but when necessary maintain the enumeration case convention for #define'd constants.
• Keep variable names descriptive, but not excessively verbose. Names of major classes and critical variables should contain complete words, avoiding abbreviations except for obvious diminiutions like "Information" -> "Info" or "Matrix" -> "Mat".
• The length of the variable name should follow from the extent of the scope. Short variable names are permissible in very local contexts, longer ones necessary for expansive scopes.
• File names should be in all lowercase with underscore separators, similar to variable names.
• Library files get .cpp extensions, header files .h extensions. Do not use separate header files for data structures and functions within a library--if circular referencing occurs, make a new library and separate out the problematic data structures.

---

Function Declarations

• Pass by class objects by const reference to C++ functions, unless they will undergo changes in the function--then, pass them by pointer to indicate that they may change as the function is executed (see the discussion on pointer usage below). Pass abstracts (structs of const pointers and scalars) by value to CUDA or HIP kernels. Depending on the situation, passing an abstract by value to a C++ function (to avoid an extra layer of de-referencing) is permissible.
• Place function descriptions in the .cpp library file, ahead of the function declaration, not in the header.
• Declare const-ness of elementary type variables in the library file but not in the header file, i.e. const double x in the .cpp but double x in the .h. Exceptions to this rule include template header files, where there may be no corresponding code in a .cpp file.
• Place doxygen \brief comments for classes and structs in the .h file above their declarations. Place \brief comments for functions or extern / global class objects above their code in the .cpp library file.
• Declare overloaded functions using comments such as

    /// \brief General description of function in all guises
    ///
    /// Overloaded:
    ///   - First version
    ///   - Second version
    ///
    /// \param (Describe all parameters to any of the function's overloaded versions)

• Overloaded functions should cascade from instance to instance so long as it does not create ineffciency in production calculations. Conserve code and avoid replication, but not at the expense of performance. Use the "buck stops here" overloaded function in the most performance- critical sections of algorithms for versatility as well as performance.

---

Struct Declarations

• Place descriptions for structs in the .h header file where the struct is declared.
• Most structs will have private member variables that are accessible only by getter and setter functions. In such cases the developer will often define their own constructor. For convenience, such as with a small struct which serves only to interlace related data in an array or pass by value to another function, structs may be "unguarded" meaning that they have all public member variables. Such structs should also have no member functions--they are, for all practical purposes, structs in the traditional C sense, but this is of course a convention, not a compiler-enforced rule.
• Template structs take template type name T, or T1, T2, T3... as necessary. When multiple templated types are present, tuples of the base type should be named with the size of the tuple, e.g. <typename T, typename T4> when T could be int or float. More elaborate names are encouraged when the types serve different purposes and are not expected to comprise similar number formats, e.g. <typename Tcoord, typename Tcalc> for distinguishing the data types used to express coordinates and the data type used to perform most arithmetic.
• Use initializer list mechanics where possible, but for structs that require detailed reading of a text file or other complex manipulation of data, write an argumentless constructor that builds a blank struct, then use that to fulfill the initializer lists of any other constructors which take actual arguments (i.e. a file name or other data struct). Use the function bodies of the subsequent constructors to resize arrays and other member variables as necessary and fill out the contents of the struct.
• Use cascading initalization, including the above strategy, as much as possible. Assume that struct initialization is not a performance issue, and write code such that it is an overhead cost but not a factor in calculation production.

---

Template Conventions

One of the most significant features that distinguishes C++ from the original C language is the existence of templates, objects or functions which are (within limits) agnostic to the data type that they will operate upon. Because they step into the realm of modern C++ and a new world of abstraction and inference, templates make an important "boundary" case of what can be sanctioned within the (arbitrary, self-imposed) STORMM coding conventions. The critical aspect of successful template design concerns type inference, a depthy and general aspect of C++ which made laudable improvements in C++11. When a template is used in the code, the compiler essentially creates an overloaded variant of the template function as stipulated either by an explicit type declaration, i.e. double t = foo<double>(... argument list ...) or by infering the exact overloaded type from a function argument of the template type, i.e. a primitive such as

    template <typename T> T foo(const std::vector<T> &list) {
      ... function content ...
    }

In the above case, the return type of foo() cannot be dictated by the type of variable that receives the output. However, it can be inferred from the argument fed to foo():

    std::vector<long long int> phone_numbers(1000, 0);
    ... load phone_numbers with data... 
    float local_var = foo(phone_numbers);

The template leads to an overloaded instantiation of the function that takes a vector of ints, and therefore returns an int, regardless of the type of local_var. This can be very powerful, but it can also lead to some mistakes that will break code later on. Let's say that foo() was designed to accept a vector of int4 objects:

    struct int4 {
      int x;
      int y;
      int z;
      int w;
    };

If there were any code inside of foo() that operated on components of the input data such as x, y, or w, this would negate the use of foo() on any data type that does not have those components. Similarly, if foo() contained an operation like += that was well defined for standard ints but not for int4s, its use would be diminished. All of these errors would be silent until the templated function was given a type to work on, at which point it would either compile or not. With this understanding:

• Write templates such that they can be used with inputs of scalar data, or vectors of scalar types, with type inference by the compiler rather than explicit declaration.
• For templated functions that take no input arguments, explicit declaration of the intended type is required as a function modifier, i.e. foo<int>(). Write these functions to be able to work with many types, without operations that would restrict the input type, or with names that suggest the types that the function is intended to work with.
• Understand that templates cascade when one function call another, and that this behavior cannot be protected by an if (conditions) { } statement in the code. Any code that appears in a templated function must be able to work with any type that might be presented to the template.
• For templates that are only intended to work with a finite number of types, use functions with traps to throw runtime errors when developers attempt to instantiate the template with the wrong type. This feature will be superceded by code that throws compile-time errors in the future.

---

References and Pointers

In C, it is common to pass arrays and other large objects by pointer. This saves the program from having to create a copy of the argument in question solely for the purposes of that function, at the expense of de-referencing the pointer at every access of the object or one of its attributes. This also requires frequent use of the -> operator, i.e. x->y for member variable y in struct x, if x has been passed by pointer (foo(*x)). In C++, it is more common to pass by reference (&x). There are some subtle differences between references and pointers, but from the standpoint of performance references and pointers are equivalent (the program still needs to de-reference the reference). Passing by reference also removes the need to write -> for accessing object attributes and also &x in the function call (it can be confusing, as to pass by reference there will be a &x in the function declaration). One can write x.y inside a function passed argument &x (although the de-referencing will still occur), and call foo(x). It is not best just to pass arguments by reference, however: whenever passing by reference, STORMM passes by const reference, restricting the function from changing the data referenced by the variable (the only thing that a function can produce is its return value). While some developers strive to have every argument be const (including arguments passed by value), there are situations where it is far preferable to have a function do two things, usually modifying some array while accumulating a result. In these cases, the best practice is to return the result and pass the variable which will be modified through the process by pointer. That way, in the function call, developers will need to write & when passing the argument, sending a clear signal that it, too, will be modified over the course of the call. For example:

(... Declare BuildingDesign and ConstructionProject objects ...)

//-------------------------------------------------------------------------------------------------
int squareFootage(const BuildingDesign &blueprint, std::vector<ConstructionProject> *buildings,
                  const std::vector<int> &addresses) {
  int sq_ft = 0;
  for (size_t i = 0; i < addresses->size(); i++) {
    buildings->data()[addresses[i]].floor_count = blueprint.floors;
    buildings->data()[addresses[i]].entry_dimensions = blueprint.door_width;
    buildings->data()[addresses[i]].length = blueprint.length;
    buildings->data()[addresses[i]].width = blueprint.width;
    sq_ft += (blueprint.floors * blueprint.length * blueprint.width) - (blueprint.door_width * 2);
  }
  
  return sq_ft;
}

//-------------------------------------------------------------------------------------------------
int main() {
  BuildingDesign home_blueprint1;
  BuildingDesign home_blueprint2;
  std::vector<ConstructionProject> my_town;
  std::vector<int> home_addresses1;
  std::vector<int> home_addresses2;
  (... Fill in the details of home_blueprint1, home_blueprint2, and home_addresses(1,2) ...)
  
  // The function call below will accumulate the total residential space while assigning specific
  // addresses to be a particular type of home.
  int total_residential_space  = squareFootage(home_blueprint1, &my_town, home_addresses1);
  total_residential_space     += squareFootage(home_blueprint2, &my_town, home_addresses2);
  return total_residential_space;
}

The program above passes blueprint objects and the arrays of home addresses by const reference, while passing the array of actual buildings in the town by pointer. In the main function, it is obvious that my_town is being modified as the details of particular buildings are filled out by calls to a function that also accumulates the square footage of buildings made with a certain blueprint. It would also be feasible to write the squareFootage() function with a non-const reference for its second argument, but then the calls in main would give no indication that my_town was being modified by each call: const and non-const references look the same when calling a function, but passing by pointer guards against stealth data modifications.

Do not be afraid of pointers! C++ is a superset of C, and therefore can make use of them. It is not desirable to use pointers to dynamically allocate memory, however: that is for containers and smart pointers, the most common being std::vector. For array access, pointers are often (but not always) less desirable than containers due to the fact that a pointer to a block of memory is just that: it carries no information about what the bounds are, and while it is possible to reach a segmentation fault by accessing a non-existent element of a std::vector it is much easier to go into the code and install a bounds check if that happens. In optimized code, accessing an element of a std::vector by the [] operator is as fast as accessing an element of an array through a pointer--the compiler knows what the program is meant to do, so pointers seldom have a performance advantage unless they help to skip over an extra layer of function calls and de-referencing (there are cases of that, and STORMM libraries frequently provide small structs full of const pointers to expedite access to data, especially in GPU-based arrays). However, for the specific purpose of letting a function modify one of its input arguments, pointers are the preferred route.

---

Class Abstracts and the C++ >> C >> HPC Transition Model

STORMM is a collection of C++ classes and CUDA (and perhaps in the future HIP or OpenCL) Kernels which communicate by stripping the data down to a lowest common denominator of C. Each class built in STORMM should emit one or more "abstracts" which will collect critical constants, sizing information for arrays, and raw pointers to the underlying information contained in the class. Each abstract is a struct and as such should contain only a basic constructor taking values for each member variable as a formal argument, copy and move constructors, and perhaps a manually defined destructor. This represents a C++ to C transition: it is possible to overrun the limits of various arrays when uing an abstract if the stated bounds are not respected, as there are no accessor functions with their own internal bounds checks as would be expected in a C++ class. Abstracts should contain suffixes like Reader, Writer, Kit, or Plan depending on what seems most appropriate given the nature of the object and the mutability of data mapped by the abstract. In most cases, even Writer abstracts will contain some const members such as array sizing constants, and thus will not be able to have copy and move assignment operators. In most cases the abstracts will be obtained from class member functions named data() but more descriptive names may be used to produce specialized abstracts tailored to a subset of the underlying class object's contents.

---

Templated CUDA Kernels and Templated Kernel Arguments

A thought experiment can establish that it is not feasible to call a templated CUDA kernel from a C++ file (.cpp) unless the HPC compiler is also compiling the .cpp implementations, i.e. is the only compiler in town. This is not the way STORMM is intended to function, given the standalone CPU functionality that most of the code base aims to provide for prototyping and basic testing. This does not, however, mean that a templated class instantiated by a C++ compiler cannot be delivered to a CUDA (or HIP, or OpenCL) implementation without writing separate branches for every intended data type of the template, an approach which would be prohibitive in the case of multiple independent templated data types. The preferred approach in STORMM is to create abstracts of the templated class with the templated data pointers cast to void*. This cannot be done with scalar constants included in the abstract, but it is seldom a problem. Abstracts of this sort are typically obtained by member functions named templateFreeData(). The void-casted abstracts are then converted back to their original types by templated free functions named restoreType, overloaded by the different abstracts they take for formal arguments. Because both C++ and HPC compilers will build objects out of the various header files, each will develop their own template instantiations for a given object while the void*-casted form is useful as the intermediary.