Design Overview


This section is a stub. Please open a pull-request to improve it or open an issue with open questions.

This library consists of three conceptual parts:

  • The backend, concerned with elementary low-level I/O operations.

  • The I/O-Queue, acting as a communication mechanism and buffer between the other two parts.

  • The user-facing frontend, enforcing the openPMD standard, keeping a logical state of the data, and synchronizing that state with persistent data by scheduling I/O-Tasks.


One of the main goals of this library is to provide a high-level common interface to synchronize persistent data with a volatile representation in memory. This includes handling data in any number of supported file formats transparently. Therefore, enabling users to handle hierarchical, self-describing file formats while disregarding the actual nitty-gritty details of just those file formats, required the reduction of possible operations reduced to a common set of IOTasks:





    AVAILABLE_CHUNKS, //!< Query chunks that can be loaded in a dataset
    KEEP_SYNCHRONOUS, //!< Keep two items in the object model synchronous with

Every task is designed to be a fully self-contained description of one such atomic operation. By describing a required minimal step of work (without any side-effect), these operations are the foundation of the unified handling mechanism across suitable file formats. The actual low-level exchange of data is implemented in IOHandlers, one per file format (possibly two if handlingi MPI-parallel work is possible and requires different behaviour). The only task of these IOHandlers is to execute one atomic IOTask at a time. Ideally, additional logic is contained to improve performance by keeping track of open file handles, deferring and coalescing parts of work, avoiding redundant operations. It should be noted that while this is desirable, sequential consistency must be guaranteed (see I/O-Queue.)

Note this paragraph is a stub: AbstractParameter and subclasses as typesafe descriptions of task parameters, Writable as unique identification in task, corresponding to node in frontend hierarchy (tree-like structure), subclass of AbstractIOHandler to ensure simple extensibilty, and only two public interface methods (enqueue() and flush()) to hide separate behaviour & state AbstractFilePosition as a format-dependent location inside persistent data (e.g. node-id / path string) should be entirely agnostic to openPMD and just treat transferred data as raw bytes without knowledge


To keep coupling between openPMD logic and actual low-level I/O to a minimum, a sequence of atomic I/O-Tasks is used to transfer data between logical and physical representation. Individual tasks are scheduled by frontend application logic and stored in a data structure that allows for FIFO order processing (in future releases, this order might be relaxed). Tasks are not executed during their creation, but are instead buffered in this queue. Disk accesses can be coalesced and high access latencies can be amortized by performing multiple tasks bunched together. At appropriate points in time, the used backend processes all pending tasks (strict, single-threaded, synchronous FIFO is currently used in all backends, but is not mandatory as long as consistency with that order can be guaranteed).

A typical sequence of tasks that are scheduled during the read of an existing file could look something like this:

2.  READ_ATT     // 'openPMD'
3.  READ_ATT     // 'openPMDextension'
4.  READ_ATT     // 'basePath'
5.  LIST_ATTS    // in '/'
5.1 READ_ATT     // 'meshesPath', if in 5.
5.2 READ_ATT     // 'particlesPath', if in 5.
6.  OPEN_PATH    // 'basePath'
7.  LIST_ATTS    // in 'basePath'
7.X READ_ATT     // every 'att' in 7.
8.  LIST_PATHS   // in 'basePath'
9.X OPEN_PATH    // every 'path' in 8.

Note that (especially for reading), pending tasks might have to be processed between any two steps to guarantee data consistency. That is because action might have to be taken conditionally on read or written values, openPMD conformity checked to fail fast, or a processing of the tasks be requested by the user explicitly.

As such, FIFO-equivalence with the scheduling order must be satisfied. A task that is not located at the head of the queue (i.e. does not have the earliest schedule time of all pending tasks) is not guaranteed to succeed in isolation. Currently, this can only guaranteed by sequentially performing all tasks scheduled prior to it in chronological order. To give two examples where this matters:

  • Reading value chunks from a dataset only works after the dataset has been opened. Due to limitations in some of the backends and the atomic nature of the I/O-tasks in this API (i.e. operations without side effects), datatype and extent of a dataset are only obtained by opening the dataset. For some backends this information is required for chunk reading and thus must be known prior to performing the read.

  • Consecutive chunk writing and reading (to the same dataset) mirrors classical RAW data dependence.

    The two chunks might overlap, in which case the read has to reflect the value changes introduced by the write.

Atomic operations contained in this queue are …


While the other two components are primarily concerned with actual I/O, this one is the glue and constraint logic that lets a user build the in-memory view of the hierarchical file structure. Public interfaces should be limited to this part (exceptions may arise, e.g. format-dependent dataset parameters). Where the other parts contain virtually zero knowledge about openPMD, this one contains all of it and none of the low-level I/O.

Writable (mixin) base class of every front-end class, used to tree structure used in backend

Attributable (mixin) class that allows attaching meta-data to tree nodes (openPMD attributes)

Attribute a variadic datastore for attributes supported across backends

Container serves two purposes
  • python-esque access inside hierarchy groups (foo[“bar”][“baz”])

  • only way for user to construct objects (private constructors), forces them into the correct hierarchy (no dangling objects)

all meta-data access stores in the Attributable part of an object and follows the syntax

Object& setFoo(Foo foo);
Foo foo() const;

(future work: use CRTP)

openPMD frontend classes are designed as handles that user code may copy an arbitrary amount of times, all copies sharing common resources. The internal shared state of such handles is stored as a single shared_ptr. This serves to separate data from implementation, demonstrated by the class hierarchy of Series:

  • SeriesData contains all actual data members of Series, representing the resources shared between instances. Its copy/move constructors/assignment operators are deleted, thus pinning it at one memory location. It has no implementation.

  • Series defines the interface of the class and is used by client code. Its only data member is a shared pointer to its associated instance of SeriesData. (This pointer-based design allows us to avoid a similar template-heavy design based on CRTP.)

  • A non-owning instance of the Series class can be constructed on the fly from a SeriesData object by passing a shared_ptr with no-op destructor when constructing the Series. Care must be taken to not expose such an object to users as the type system does not distinguish between an owning and a non-owning Series object.

  • Frontend classes that define the openPMD group hierarchy follow this same design (except for some few that do not define data members). These classes all inherit from Attributable. Their shared state inherits from AttributableData. A downside of this design is that the shared_ptr pointing to the internal state is redundantly stored for each level in the class hierarchy, static_cast-ed to the corresponding level. All these pointers reference the same internal object.

The class hierarchy of Attributable follows a similar design, with some differences due to its nature as a mixin class that is not instantiated directly:

The Series class is the entry point to the openPMD-api. An instance of this class always represents an entire series (of iterations), regardless of how this series is modeled in storage or transport (e.g. as multiple files, as a stream, as variables containing multiple snapshots of a dataset or as one file containing all iterations at once).