Template Numerical Library  version develop:3271efbb
Core concepts

TNL is based on the following core concepts:

  1. Allocators
    • Allocator handles memory allocation and deallocation.
    • TNL allocators are fully compatible with the standard C++ concept
    • Multiple allocators can correspond to the same "memory space".
  2. Devices (TODO: rename to Executor or something like that)
    • Device is responsible for the execution of algorithms in a specific way.
    • Algorithms can be specialized by the Device template parameter.
  3. Communicators
    • Communicators represent the main abstraction for distributed computations, where multiple programs (or instances of the same program) have to communicate with each other.
    • At present, there are only two communicators: MpiCommunicator (uses MPI) and NoDistrCommunicator (dummy communicator without any distribution support).
  4. Algorithms
    • Basic (container-free) algorithms specialized by Device/Executor.
    • ParallelFor, Reduction, MultiReduction, ArrayOperations, ...
  5. Containers
    • Classes for general data structures. (TODO: alternatively use "Dense" and "Sparse", because a dense matrix can be an extended alias for 2D array)
    • Array, Vector (also VectorOperations), NDArray, ...
  6. Views
    • Views wrap only a raw pointer to data and some metadata (such as the array size), they do not do allocation and deallocation of the data. Hence, views have a fixed size which cannot be changed.
    • Views have a copy-constructor which does a shallow copy. As a result, views can be passed-by-value to CUDA kernels or captured-by-value by device lambda functions.
    • Views have a copy-assignment operator which does a deep copy.
    • Views have all other methods present in the relevant container (data structure).

TODO: formalize the concepts involving lambda functions (e.g. in Reduction)