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New ITensor Storage and Code Design

Miles Stoudenmire—March 25, 2016

Background

A long-term goal of ITensor is supporting not just "regular" dense tensors and quantum number conserving tensors (a special case of block-sparsity), but a much richer variety of sparse tensors. We also want to leave the door open for more ambitious future designs, such as tensors with storage distributed across multiple machines.

ITensor storage prior to version 2.0 was just a contiguous array of real numbers, which is insufficient for handling arbitrary sparse tensor types. Though we were able to cleverly repurpose this storage to implement diagonal-sparse ITensors, this was just a workaround until we could find a better solution.

For ITensor 2.0 we considered a few standard solutions, but most had serious drawbacks. Two examples:

The doTask system: "dynamic overloading"

The design we went with is fairly novel for C++, although it is second nature in a language such as Julia which eschews class methods and supports multiple dispatch. In a nutshell, our design works as follows:

  1. An ITensor is basically just a shared pointer to an opaque "box" type which can hold any of the pre-registered storage types. The pointer of an ITensor T can be accessed by calling T.store().
  2. Storage types (Dense, Diag, Combiner, etc.) are different types, each with their own unique layout and class methods. There are no requirements for storage types: they do not have to inherit from any base class. The only necessary step to use a new storage type is to register it in the ITensor storage system.
  3. Methods for manipulating tensor storage are free functions, all overloads of a function named doTask(...). These methods are distinguished by their first argument, which is a lightweight type called the "task object". For example, to compute the norm of a storage object, at the ITensor (interface) level one calls:
 Real
 norm(ITensor T)
 {
 auto nrm = doTask(Norm(),T.store());
 return nrm;
 }

Calling doTask(Norm(),T.store()) searches for a function with the signature doTask(Norm,StorageType S). For example, to implement this task type for the Dense storage type, one defines the following function:

 Real
 doTask(Norm,Dense const& D)
 {
 //... code to compute norm of D ...
 return nrm;
 }

Importantly, leaving a doTask overload undefined for a particular storage type is not a compile-time error. An undefined method only causes an error if an attempt is made to call it at run time. This design allows one to focus on writing only the code that is needed to define the meaningful behavior of each type, with minimal boilerplate or glue code.

Other advantages and features of the doTask system:


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