15 KiB
HDF5 Parallel Compression
Introduction
When an HDF5 dataset is created, the application can specify optional data filters to be applied to the dataset (as long as the dataset uses a chunked data layout). These filters may perform compression, shuffling, checksumming/error detection and more on the dataset data. The filters are added to a filter pipeline for the dataset and are automatically applied to the data during dataset writes and reads.
Prior to the HDF5 1.10.2 release, a parallel HDF5 application could read datasets with filters applied to them, but could not write to those datasets in parallel. The datasets would have to first be written in a serial HDF5 application or from a single MPI rank in a parallel HDF5 application. This restriction was in place because:
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Updating the data in filtered datasets requires management of file metadata, such as the dataset's chunk index and file space for data chunks, which must be done collectively in order for MPI ranks to have a consistent view of the file. At the time, HDF5 lacked the collective coordination of this metadata management.
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When multiple MPI ranks are writing independently to the same chunk in a dataset (even if their selected portions of the chunk don't overlap), the whole chunk has to be read, unfiltered, modified, re-filtered and then written back to disk. This read-modify-write style of operation would cause conflicts among the MPI ranks and lead to an inconsistent view of the file.
Introduced in the HDF5 1.10.2 release, the parallel compression feature allows an HDF5 application to write in parallel to datasets with filters applied to them, as long as collective I/O is used. The feature introduces new internal infrastructure that coordinates the collective management of the file metadata between MPI ranks during dataset writes. It also accounts for multiple MPI ranks writing to a chunk by assigning ownership to one of the MPI ranks, at which point the other MPI ranks send their modifications to the owning MPI rank.
The parallel compression feature is always enabled when HDF5
is built with parallel enabled, but the feature may be disabled
if the necessary MPI-3 routines are not available. Therefore,
HDF5 conditionally defines the macro H5_HAVE_PARALLEL_FILTERED_WRITES
which an application can check for to see if the feature is
available.
Examples
Using the parallel compression feature is very similar to using compression in serial HDF5, except that dataset writes must be collective:
hid_t dxpl_id = H5Pcreate(H5P_DATASET_XFER);
H5Pset_dxpl_mpio(dxpl_id, H5FD_MPIO_COLLECTIVE);
H5Dwrite(..., dxpl_id, ...);
The following are two simple examples of using the parallel compression feature:
The former contains simple examples of using the parallel compression feature to write to compressed datasets, while the latter contains an example of how to write to compressed datasets when one or MPI ranks don't have any data to write to a dataset. Remember that the feature requires these writes to use collective I/O, so the MPI ranks which have nothing to contribute must still participate in the collective write call.
Incremental file space allocation support
HDF5's file space allocation time is a dataset creation property that can have significant effects on application performance, especially if the application uses parallel HDF5. In a serial HDF5 application, the default file space allocation time for chunked datasets is "incremental". This means that allocation of space in the HDF5 file for data chunks is deferred until data is first written to those chunks. In parallel HDF5, the file space allocation time was previously always forced to "early", which allocates space in the file for all of a dataset's data chunks at creation time (or during the first open of a dataset if it was created serially). This would ensure that all the necessary file space was allocated so MPI ranks could perform independent I/O operations on a dataset without needing further coordination of file metadata as described previously.
While this strategy has worked in the past, it has some noticeable
drawbacks. For one, the larger the chunked dataset being created,
the more noticeable overhead there will be during dataset creation
as all of the data chunks are being allocated in the HDF5 file.
Further, these data chunks will, by default, be filled
with HDF5's default fill data value, leading to extraordinary
dataset creation overhead and resulting in pre-filling large
portions of a dataset that the application might have been planning
to overwrite anyway. Even worse, there will be more initial overhead
from compressing that fill data before writing it out, only to have
it read back in, unfiltered and modified the first time a chunk is
written to. In the past, it was typically suggested that parallel
HDF5 applications should use H5Pset_fill_time
with a value of H5D_FILL_TIME_NEVER in order to disable writing of
the fill value to dataset chunks, but this isn't ideal if the
application actually wishes to make use of fill values.
With improvements made to the parallel compression feature for the HDF5 1.13.1 release, "incremental" file space allocation is now the default for datasets created in parallel only if they have filters applied to them. "Early" file space allocation is still supported for these datasets if desired and is still forced for datasets created in parallel that do not have filters applied to them. This change should significantly reduce the overhead of creating filtered datasets in parallel HDF5 applications and should be helpful to applications that wish to use a fill value for these datasets. It should also help significantly reduce the size of the HDF5 file, as file space for the data chunks is allocated as needed rather than all at once.
Performance Considerations
Since getting good performance out of HDF5's parallel compression feature involves several factors, the following is a list of performance considerations (generally from most to least important) and best practices to take into account when trying to get the optimal performance out of the parallel compression feature.
Begin with a good chunking strategy
Starting with a good chunking strategy will generally have the largest impact on overall application performance. The different chunking parameters can be difficult to fine-tune, but it is essential to start with a well-performing chunking layout before adding compression and parallel I/O into the mix. Compression itself adds overhead and may have side effects that necessitate further adjustment of the chunking parameters and HDF5 application settings. Consider that the chosen chunk size becomes a very important factor when compression is involved, as data chunks have to be completely read and re-written to perform partial writes to the chunk.
Improving I/O performance with HDF5 compressed datasets is a useful reference for more information on getting good performance when using a chunked dataset layout.
Avoid chunk sharing
Since the parallel compression feature has to assign ownership of data chunks to a single MPI rank in order to avoid the previously described read-modify-write issue, an HDF5 application may need to take care when determining how a dataset will be divided up among the MPI ranks writing to it. Each dataset data chunk that is written to by more than 1 MPI rank will incur extra MPI overhead as one of the ranks takes ownership and the other ranks send it their data and information about where in the chunk that data belongs. While not always possible to do, an HDF5 application will get the best performance out of parallel compression if it can avoid writing in a way that causes more than 1 MPI rank to write to any given data chunk in a dataset.
Collective metadata operations
The parallel compression feature typically works with a significant amount of metadata related to the management of the data chunks in datasets. In initial performance results gathered from various HPC machines, it was found that the parallel compression feature did not scale well at around 8k MPI ranks and beyond. On further investigation, it became obvious that the bottleneck was due to heavy filesystem pressure from the metadata management for dataset data chunks as they changed size (as a result of data compression) and moved around in the HDF5 file.
Enabling collective metadata operations in the HDF5 application (as in the below snippet) showed significant improvement in performance and scalability and is generally always recommended unless application performance shows negative benefits by doing so.
...
hid_t fapl_id = H5Pcreate(H5P_FILE_ACCESS);
H5Pset_fapl_mpio(fapl_id, MPI_COMM_WORLD, MPI_INFO_NULL);
H5Pset_all_coll_metadata_ops(fapl_id, 1);
H5Pset_coll_metadata_write(fapl_id, 1);
hid_t file_id = H5Fcreate("file.h5", H5F_ACC_TRUNC, H5P_DEFAULT, fapl_id);
...
Align chunks in the file
The natural layout of an HDF5 file may cause dataset data chunks to end up at addresses in the file that do not align well with the underlying file system, possibly leading to poor performance. As an example, Lustre performance is generally good when writes are aligned with the chosen stripe size. The HDF5 application can use H5Pset_alignment to have a bit more control over where objects in the HDF5 file end up. However, do note that setting the alignment of objects generally wastes space in the file and has the potential to dramatically increase its resulting size, so caution should be used when choosing the alignment parameters.
H5Pset_alignment has two parameters that control the alignment of objects in the HDF5 file, the "threshold" value and the alignment value. The threshold value specifies that any object greater than or equal in size to that value will be aligned in the file at addresses which are multiples of the chosen alignment value. While the value 0 can be specified for the threshold to make every object in the file be aligned according to the alignment value, this isn't generally recommended, as it will likely waste an excessive amount of space in the file.
In the example below, the chosen dataset chunk size is provided for the threshold value and 1MiB is specified for the alignment value. Assuming that 1MiB is an optimal alignment value (e.g., assuming that it matches well with the Lustre stripe size), this should cause dataset data chunks to be well-aligned and generally give good write performance.
hid_t fapl_id = H5Pcreate(H5P_FILE_ACCESS);
H5Pset_fapl_mpio(fapl_id, MPI_COMM_WORLD, MPI_INFO_NULL);
/* Assuming Lustre stripe size is 1MiB, align data chunks
in the file to address multiples of 1MiB. */
H5Pset_alignment(fapl_id, dataset_chunk_size, 1048576);
hid_t file_id = H5Fcreate("file.h5", H5F_ACC_TRUNC, H5P_DEFAULT, fapl_id);
File free space managers
As data chunks in a dataset get written to and compressed, they can change in size and be relocated in the HDF5 file. Since parallel compression usually involves many data chunks in a file, this can create significant amounts of free space in the file over its lifetime and eventually cause performance issues.
An HDF5 application can use H5Pset_file_space_strategy
with a value of H5F_FSPACE_STRATEGY_PAGE to enable the paged
aggregation feature, which can accumulate metadata and raw
data for dataset data chunks into well-aligned, configurably
sized "pages" for better performance. However, note that using
the paged aggregation feature will cause any setting from
H5Pset_alignment
to be ignored. While an application should be able to get
comparable performance effects by setting the size of these pages to be equal to the value that
would have been set for H5Pset_alignment,
this may not necessarily be the case and should be studied.
Note that H5Pset_file_space_strategy
has a persist parameter. This determines whether or not the
file free space manager should include extra metadata in the
HDF5 file about free space sections in the file. If this
parameter is false, any free space in the HDF5 file will
become unusable once the HDF5 file is closed. For parallel
compression, it's generally recommended that persist be set
to true, as this will keep better track of file free space
for data chunks between accesses to the HDF5 file.
hid_t fcpl_id = H5Pcreate(H5P_FILE_CREATE);
/* Use persistent free space manager with paged aggregation */
H5Pset_file_space_strategy(fcpl_id, H5F_FSPACE_STRATEGY_PAGE, 1, 1);
/* Assuming Lustre stripe size is 1MiB, set page size to that */
H5Pset_file_space_page_size(fcpl_id, 1048576);
...
hid_t file_id = H5Fcreate("file.h5", H5F_ACC_TRUNC, fcpl_id, fapl_id);
Low-level collective vs. independent I/O
While the parallel compression feature requires that the HDF5 application set and maintain collective I/O at the application interface level (via H5Pset_dxpl_mpio), it does not require that the actual MPI I/O that occurs at the lowest layers of HDF5 be collective; independent I/O may perform better depending on the application I/O patterns and parallel file system performance, among other factors. The application may use H5Pset_dxpl_mpio_collective_opt to control this setting and see which I/O method provides the best performance.
hid_t dxpl_id = H5Pcreate(H5P_DATASET_XFER);
H5Pset_dxpl_mpio(dxpl_id, H5FD_MPIO_COLLECTIVE);
H5Pset_dxpl_mpio_collective_opt(dxpl_id, H5FD_MPIO_INDIVIDUAL_IO); /* Try independent I/O */
H5Dwrite(..., dxpl_id, ...);
Runtime HDF5 Library version
An HDF5 application can use the H5Pset_libver_bounds routine to set the upper and lower bounds on library versions to use when creating HDF5 objects. For parallel compression specifically, setting the library version to the latest available version can allow access to better/more efficient chunk indexing types and data encoding methods. For example:
...
hid_t fapl_id = H5Pcreate(H5P_FILE_ACCESS);
H5Pset_libver_bounds(fapl_id, H5F_LIBVER_LATEST, H5F_LIBVER_LATEST);
hid_t file_id = H5Fcreate("file.h5", H5F_ACC_TRUNC, H5P_DEFAULT, fapl_id);
...