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‎.github/workflows/codespell.yml

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# GitHub Action to automate the identification of common misspellings in text files
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# https://github.com/codespell-project/codespell
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# https://github.com/codespell-project/actions-codespell
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name: codespell
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on: [push, pull_request]
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permissions:
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contents: read
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jobs:
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codespell:
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name: Check for spelling errors
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runs-on: ubuntu-latest
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steps:
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- uses: actions/[email protected]
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- uses: codespell-project/actions-codespell@master
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with:
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ignore_words_list: fom,coo,ku,inout
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skip: .git, .github
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‎404.md

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# This page doesn't seem to exist
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## Search all Support Content
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<script async src="https://cse.google.com/cse.js?cx=f671574fa55cc44e6">
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</script>
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<div class="gcse-search"></div>
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<br>
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## Browse Documentation
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You might find what you're looking for in [Documentation](/documentation/index.html).

‎_config.yml

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path: ""
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values:
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layout: default
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url: "https://support.hdfgroup.org"

‎_layouts/default.html

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<!DOCTYPE html>
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<!DOCTYPE html>
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<html lang="{{ site.lang | default: "en-US" }}">
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<head>
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<link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet">
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<li><a href="https://www.hdfgroup.org/solutions/priority-support/">HDF Software Priority Support</a>
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<li><a href="https://www.hdfgroup.org/solutions/consulting/">HDF Consulting</a></li>
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<li><a href="{{site.url_docs}}/archive/support/index.html">Archive</a></li>
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<li><a href="/search/index.html">Search</a></li>
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</ul>
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<br>
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<!--
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{% if site.github.is_project_page %}
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<p>This project is maintained by <a href="{{ site.github.owner_url }}">{{ site.github.owner_name }}</a></p>
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{% endif %}
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<p><small>Hosted on GitHub Pages &mdash; Theme by <a href="https://github.com/orderedlist">orderedlist</a></small></p>
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</footer>
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</footer>
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</div>
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</body>

‎clinic/index.html

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‎clinic/index.org

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: world
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: hello
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: finished at 08:25:33
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- Noticable speedup but not a panacea
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- Noticeable speedup but not a panacea
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#+begin_quote
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Eventually I hope to have a version of =h5pyd= that supports =async= (or maybe
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an entirely new package), that would make it a little easier to use.
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use the contiguous data layout. I believe this option, if available,
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can yield a good read performance.
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#+end_quote
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- Intersting suggestions
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- Interesting suggestions
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- Elena suggested to first create all datasets with =H5D_ALLOC_TIME_LATE=
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- Mark suggested to =H5Pget_meta_block_size= to set aside sufficient
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"group space" (Maybe also =H5Pset_est_link_info=?)
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- A :: Currently, the code will skip entries >64K, because the underlying
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=H5Dcreate= will fail. (The logic and code can be much improved.) It's better
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to first run =archive_checker_64k= and see if there are any size warnings.
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The =h5compactor= and =h5shredder= were sucessfully run on TAR archives with
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The =h5compactor= and =h5shredder= were successfully run on TAR archives with
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tens of millions of small images (<64K).
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** Last week's highlights
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*** Announcements
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#+end_quote
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- [[https://meetingorganizer.copernicus.org/EGU22/EGU22-11201.html][DIARITSup: a framework to supervise live measurements, Digital Twins modelscomputations and predictions for structures monitoring.]]
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#+begin_quote
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DIARITSup is a chain of various softwares following the concept of ”system of
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DIARITSup is a chain of various software following the concept of ”system of
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systems”. It interconnects hardware and software layers dedicated to in-situ
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monitoring of structures or critical components. It embeds data assimilation
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capabilities combined with specific Physical or Statistical models like
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** Tips, tricks, & insights
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*** SWMR (old) and compression "issues" - take 2
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- Free-space managment is disabled in the original SWMR implementation
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- Free-space management is disabled in the original SWMR implementation
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- Can lead to file bloat when compression is enabled & overly aggressive
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flushing
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- **Question:** How does the new SWMR VFD implementation behave?
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- [[https://www.youtube.com/watch?v=WlnUF3LRBj4][Awkward Array: Manipulating JSON-like Data with NumPy-like Idioms]]
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- SciPy 2020 presentation by Jim Pivarski
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- Watch this!
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- How would you repesent something like this in HDF5? (Example from Jim's video)
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- How would you represent something like this in HDF5? (Example from Jim's video)
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#+begin_src python
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import awkward as ak
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array = ak.Array([
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retval = EXIT_FAILURE;
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goto fail_write;
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}
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printf("Write successed.\n");
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printf("Write succeeded.\n");
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if (H5Fflush(file, H5F_SCOPE_GLOBAL) < 0) {
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retval = EXIT_FAILURE;
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The expression below is a [templated] Class datatype in C++, placed in a
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non-contiguous memory location, requiring scatter-gather operators and a
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mechanism to dis-assemble reassemble the components. Becuase of the complexity
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mechanism to dis-assemble reassemble the components. Because of the complexity
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AFAIK there is no automatic support for this sort of operation.
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#+begin_src C++
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- Face-to-face at [[https://www.iter.org/][ITER]] in Saint Paul-lez-Durance, France
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- Reserve your spot before telling your friends! =;-)=
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**** ASCR Workshop January 2022 on the Managment and Storage of Scientific Data
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**** ASCR Workshop January 2022 on the Management and Storage of Scientific Data
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- [[https://www.osti.gov/biblio/1843500-position-papers-ascr-workshop-management-storage-scientific-data][Position Papers]]
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- [[https://www.osti.gov/biblio/1845705][Technical Report]]
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** Tips, tricks, & insights
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*** How do HDF5-UDF work?
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Currently, they are repesented as chunked datasets with a single chunk. That's
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Currently, they are represented as chunked datasets with a single chunk. That's
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why they work fine with existing tools. The UDF itself is executed as part of the
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HDF5 filter pipeline. Its code is stored in the dataset blob data plus metadata
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and managed by the UDF handler.
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I wasn’t able to find how parallelism is exactly implemented. From the above
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behaviour it looks like the file is being locked which then blocks my whole
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programm, especially if the stride is big (more time for the other ranks to run
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into a lock and be idle inbetween). Is that really the case? I write data
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continously, so theoretically there is no need for a lock. Is is possible to
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program, especially if the stride is big (more time for the other ranks to run
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into a lock and be idle in between). Is that really the case? I write data
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continuously, so theoretically there is no need for a lock. Is is possible to
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tell the driver “don’t lock the file”?
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#+end_quote
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- What's a 'stride'? (not a hyperslab stride...)
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#+END_SRC
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- The ouput file produce looks like this:
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- The output file produce looks like this:
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#+BEGIN_EXAMPLE
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- "Partial I/O gets in the way" - sorting fields by name or offset
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- Happens on each write call => overhead
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- Can this be avoided? User might provide a patch...
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**** [[https://forum.hdfgroup.org/t/read-write-specific-coordiantes-in-multi-dimensional-dataset/9137][Read/write specific coordiantes in multi-dimensional dataset?]]
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**** [[https://forum.hdfgroup.org/t/read-write-specific-coordinates-in-multi-dimensional-dataset/9137][Read/write specific coordinates in multi-dimensional dataset?]]
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- Thomas is looking for use cases from =h5py= users
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#+BEGIN_SRC python
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‎documentation/hdf5-docs/Migrating+from+HDF5+1.12+to+HDF5+1.14.md

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The virtual file layer has changed in HDF5 1.14.0. Existing virtual file drivers (VFDs) will have to be updated to work with this version of the library.
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## Virtual Object Layer (VOL) Changes
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The virtual object layer has changed significantly in HDF5 1.14.0 and the 1.12 VOL API is now considered deprecated and unsupported. Existing virtual object layer connectors shoul be updated to work with this version of the library.
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The virtual object layer has changed significantly in HDF5 1.14.0 and the 1.12 VOL API is now considered deprecated and unsupported. Existing virtual object layer connectors should be updated to work with this version of the library.

‎documentation/hdf5-docs/hdf5_topics/DebugH5App.md

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| hl | No | Local heaps |
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| i | Yes | Interface abstraction |
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| mf | No | File memory management |
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| mm | Yes | Library memory managment |
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| mm | Yes | Library memory management |
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| o | No | Object headers and messages |
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| p | Yes | Property lists |
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| s | Yes | Data spaces |

‎documentation/hdf5-docs/release_specifics/Migrating_from_HDF5_1.12_to_HDF5_1.14.md

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The virtual file layer has changed in HDF5 1.14.0. Existing virtual file drivers (VFDs) will have to be updated to work with this version of the library.
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## Virtual Object Layer (VOL) Changes
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The virtual object layer has changed significantly in HDF5 1.14.0 and the 1.12 VOL API is now considered deprecated and unsupported. Existing virtual object layer connectors shoul be updated to work with this version of the library.
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The virtual object layer has changed significantly in HDF5 1.14.0 and the 1.12 VOL API is now considered deprecated and unsupported. Existing virtual object layer connectors should be updated to work with this version of the library.

‎documentation/hdf5-docs/release_specifics/hdf5_1_14.md

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### [New Features in HDF5 Release 1.14](new_features_1_14.md)
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* [H5Sencode / H5Sdecode Format Change - RFC](https://docs.hdfgroup.org/hdf5/rfc/H5Sencode_format.docx.pdf)
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* [Update to References](https://docs.hdfgroup.org/hdf5/rfc/RFC_Update_to_HDF5_References.pdf)
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* [Update to Selections](https://docs.hdfgroup.org/hdf5/rfc/selection_io_RFC_210610.pdf)
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* [Virtual Object Layer](https://docs.hdfgroup.org/hdf5/develop/_v_o_l__connector.html)
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* [16 bit floating point and Complex number datatypes](https://github.com/HDFGroup/hdf5doc/blob/master/RFCs/HDF5_Library/Float16/RFC__Adding_support_for_16_bit_floating_point_and_Complex_number_datatypes_to_HDF5.pdf)
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* [Asynchronous I/O operations](asyn_ops_wHDF5_VOL_connectors.html)
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* [Subfiling VFD](https://support.hdfgroup.org/releases/hdf5/documentation/rfc/RFC_VFD_subfiling_200424.pdf)
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* [Onion VFD](https://support.hdfgroup.org/releases/hdf5/documentation/rfc/Onion_VFD_RFC_211122.pdf)
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* [Multi Dataset I/O](https://support.hdfgroup.org/releases/hdf5/documentation/rfc/H5HPC_MultiDset_RW_IO_RFC.pdf)
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### [Software Changes from Release to Release for HDF5 1.14](sw_changes_1.14.md)

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