Assumptions and Semantics

In this section, we provide assumptions we make about the behavior of applications that use UnifyFS and about the file system semantics of UnifyFS.

System Requirements

The system requirements to run UnifyFS are:

  • Compute nodes must be equipped with local storage device(s) that UnifyFS can use for storing file data, e.g., SSD or RAM.
  • The system must support the ability for UnifyFS user-level server processes to run concurrently with user application processes on compute nodes.

Application Behavior

UnifyFS is specifically designed to support the bulk synchronous I/O pattern that is typical in HPC applications, e.g., checkpoint/restart or output dumps. In bulk synchronous I/O, I/O operations occur in separate write and read phases, and files are not read and written simultaneously. For example, files are written during checkpointing (a write phase) and read during recovery/restart (a read phase). Additionally, parallel writes and reads to shared files occur systematically, where processes access computable, regular offsets of files, e.g., in strided or segmented access patterns, with ordering of potential conflicting updates enforced by inter-process communication. This behavior is in contrast to other I/O patterns that may perform random, small writes and reads or overlapping writes without synchronization.

UnifyFS offers the best performance for applications that exhibit the bulk synchronous I/O pattern. While UnifyFS does support deviations from this pattern, the performance might be slower and the user may have to take additional steps to ensure correct execution of the application with UnifyFS. For more information on this topic, refer to the section on :ref:`commit consistency semantics in UnifyFS <commit_consistency_label>`_.

Consistency Model

The UnifyFS file system does not support strict POSIX consistency semantics. (Please see Chen et al., HPDC 2021 for more details on different file system consistency semantics models.) Instead, UnifyFS supports two different consistency models: commit consistency semantics when a file is actively being modified; and lamination semantics when the file is no longer being modified by the application. These two consistency models provide opportunities for UnifyFS to provide better performance for the I/O operations of HPC applications.

Commit Consistency Semantics in UnifyFS

Commit consistency semantics require explicit “commit” operations to be performed before updates to a file are globally visible. We chose commit consistency semantics for UnifyFS because it is sufficient for correct execution of typical HPC applications that adhere to the bulk synchronous I/O pattern, and it enables UnifyFS to provide better performance than with strict POSIX semantics. For example, because we assume that applications using UnifyFS will not execute concurrent modifications to the same file offset, UnifyFS does not have to employ locking to ensure sequential access to file regions. This assumption allows us to cache file modifications locally which greatly improves the write performance of UnifyFS.

The use of synchronization operations are required for applications that exhibit I/O accesses that deviate from the bulk synchronous I/O pattern. There are two types of synchronization that are required for correct execution of parallel I/O on UnifyFS: local synchronization and inter-process synchronization. Here, local synchronization refers to synchronization operations performed locally by a process to ensure that its updates to a file are visible to other processes. For example, a process may update a region of a file and then execute fflush() so that a different process can read the updated file contents. Inter-process synchronization refers to synchronization operations that are performed to enforce ordering of conflicting I/O operations from multiple processes. These inter-process synchronizations occur outside of normal file I/O operations and typically involve inter-process communication, e.g., with MPI. For example, if two processes need to update the same file region and it is important to the outcome of the program that the updates occur in a particular order, then the program needs to enforce this ordering with an operation like an MPI_Barrier() to be sure that the first process has completed its updates before the next process begins its updates.

There are several methods by which applications can adhere to the synchronization requirements of UnifyFS.

  • Using MPI-IO. The (MPI-IO) interface requirements are a good match for the consistency model of UnifyFS. Specifically, the MPI-IO interface requires explicit synchronization in order for updates made by processes to be globally visible. If an application utilizes the MPI-IO interface correctly, it will adhere to the requirements of UnifyFS.
  • Using (HDF5) and other parallel I/O libraries. Most parallel I/O libraries hide the synchronization requirements of file systems from their users. For example, HDF5 implements the synchronization required by the MPI-IO interface so users of HDF5 do not need to perform any synchronization operations explicitly in their codes.
  • With explicit synchronization. If an application does not use a compliant parallel I/O library or if the developer wishes to perform explicit synchronization, local synchronization can be achieved through adding explicit “flush” operations with calls to fflush(), close(), or fsync() in the application source code, or by supplying the client.write_sync configuration parameter to UnifyFS on startup, which will cause an implicit “flush” operation after every write (note: use of the client.write_sync mode can significantly slow down write performance). In this case, inter-process synchronization is still required for applications that perform conflicting updates to files.

During a write phase, a process can deviate from the bulk synchronous I/O pattern and read any byte in a file, including remote data that has been written by processes executing on remote compute nodes in the job. However, the performance will differ based on which process wrote the data that is being read:

  • If the bytes being read were written by the same process that is reading the bytes, UnifyFS offers the fastest performance and no synchronization operations are needed. This kind of access is typical in some I/O libraries, e.g., HDF5, where file metadata may be updated and read by the same process. (Note: to obtain the performance benefit for this case, one must set the client.local_extents configuration parameter.)
  • If the bytes being read were written by a process executing on the same compute node as the reading process, UnifyFS can offer slightly slower performance than the first case and the application must introduce synchronization operations to ensure that the most recent data is read.
  • If the bytes being read were written by a process executing on a different compute node than the reading process, then the performance is slower than the first two cases and the application must introduce synchronization operations to ensure that the most recent data is read.

In summary, reading the local data (which has been written by processes executing on the same compute node) will always be faster than reading remote data.

Note that, as we discuss above, commit semantics also require inter-process synchronization for potentially conflicting write accesses. If an application does not enforce sequential ordering of file modifications during a write phase, e.g., with MPI synchronization, and multiple processes write concurrently to the same file offset or to an overlapping region, the result is undefined and may reflect the result of a mixture of the processes’ operations to that offset or region.

Lamination Consistency Semantics in UnifyFS

The other consistency model that UnifyFS employs is called “lamination semantics” which is intended to be applied once a file is done being modified at the end of a write phase of an application. After a file is laminated, it becomes permanently read-only and its data is accessible across all the compute nodes in the job without further synchronization. Once a file is laminated, it cannot be further modified, except for being renamed or deleted.

A typical use case for lamination is for checkpoint/restart. An application can laminate checkpoint files after they have been successfully written so that they can be read by any process on any compute node in the job in a restart operation. To laminate a file, an application can simply call chmod() to remove all the write bits, after its write phase is completed. When write bits of a file are removed, UnifyFS will laminate the file. A typical checkpoint write operation with UnifyFS will look like:

fd = open("checkpoint1.chk", O_WRONLY)
write(fd, <checkpoint data>, <len>)
close(fd)
chmod("checkpoint1.chk", 0444)

We plan for future versions of UnifyFS to support different methods for laminating files, such as laminating all files on close() or laminating via an explicit API call.

We define the laminated consistency model to enable certain optimizations while supporting the typical requirements of bulk synchronous I/O. Recall that for bulk synchronous I/O patterns, reads and writes typically occur in distinct phases. This means that for the majority of the time, processes do not need to read arbitrary bytes of a file until the write phase is completed, which in practice is when the file is done being modified and closed and can be safely made read-only with lamination. For applications in which processes do not need to access file data modified by other processes before lamination, UnifyFS can optimize write performance by buffering all metadata and file data for processes locally, instead of performing costly exchanges of metadata and file data between compute nodes on every write. Also, since file contents cannot change after lamination, aggressive caching may be used during the read phase with minimal locking.

File System Behavior

The following summarize the behavior of UnifyFS under our consistency model.

Failure behavior:

  • In the event of a compute node failure, all file data from the processes running on the failed compute node will be lost.
  • In the event of the failure of a UnifyFS server process, all file data from the processes assigned to that server process (typically on the same compute node) will be lost.
  • In the event of application process failures when the UnifyFS server processes remain running, the file data can retrieved by the local UnifyFS server or a remote UnifyFS server.
  • The UnifyFS team plans to improve the reliability of UnifyFS in the event of failures using redundancy scheme implementations available from the VeloC project as part of a future release.

Behavior before lamination (write phase):

  • open/close: A process may open/close a file multiple times.
  • write: A process may write to any part of a file. If two processes write to the same location concurrently (i.e., without inter-process synchronization to enforce ordering), the result is undefined.
  • read: A process may read bytes it has written. Reading other bytes is invalid without explicit synchronization operations.
  • rename: A process may rename a file.
  • truncate: A process may truncate a file.
  • unlink: A process may delete a file.

Behavior after lamination (read phase):

  • open/close: A process may open/close a file multiple times.
  • write: All writes are invalid.
  • read: A process may read any byte in the file.
  • rename: A process may rename a file.
  • truncate: Truncation is invalid (considered to be a write operation).
  • unlink: A process may delete a file.

The additional behavior of UnifyFS can be summarized as follows.

  • UnifyFS exists on node local storage only and is not automatically persisted to stable storage like a parallel file system (PFS). When the data needs to be persisted to an external file system, users can use unifyfs utility with its data staging options.
  • UnifyFS also can be coupled with SymphonyFS, high level I/O libraries, or a checkpoint library like SCR or VeloC to move data to the PFS periodically.
  • UnifyFS can be used with checkpointing libraries like SCR or VeloC, or with I/O libraries like HDF5 to support shared files on burst buffers.
  • The UnifyFS file system will be empty at job start. A user job must populate the file system manually or by using unifyfs utility.
  • UnifyFS creates a shared file system namespace across all compute nodes in a job, even if an application process is not running on all compute nodes.
  • UnifyFS survives across multiple application runs within a job.
  • UnifyFS transparently intercepts system level I/O calls of applications and I/O libraries.