Copied tnl-benchmark-spmv files and spmv.h from BLAS to SpMV. Deleted min/max size and stepFactor. Not working yet, backup purposes.

Refactoring for execution policies

Closes #49, #46, and #11

See merge request !42

Closes #49

They are not suitable for more than 2 devices/execution types; their design
breaks the Open-Closed Principle. Instead, a type template "Self" was
created, which allows to change any template parameter.

Serialization in TNL::File: File::save and File::load are specialized by Allocator instead of Device

The usage of algorithms such as MemoryOperations or Reduction is not
bound to a particular container. On the other hand, ArrayIO,
ArrayAssignment, VectorAssignment and StaticArrayAssignment are just
implementation details for the containers - moved into
TNL/Containers/detail/

Also moved ParallelFor, StaticFor, StaticVectorFor, TemplateStaticFor
into TNL/Algorithms/

This will be necessary to avoid code bloat with more than 2 devices
(execution types).

- cudaMemcpy is slower than our ParallelFor kernel for CUDA
- use std::copy and std::equal instead of memcpy and memcmp, but only as
  sequential fallbacks
- use parallel algorithms for containsValue and containsOnlyValue (again
  with sequential fallbacks)

It has nothing to do with devices.

Also set the buffer size to 1 MiB, because larger buffer size slows down
memory copies significantly (e.g. MeshTest would take about 10x longer).

Addresses #26

Moved synchronization of smart pointers from Devices::Cuda into TNL::Pointers namespace as free functions

synchronizeDevice() was renamed to synchronizeSmartPointersOnDevice()
for clarity - there are many similarly named functions in CUDA (e.g.
cudaDeviceSynchronize()).