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<center><h1>HDF5 Raw I/O Flow Notes</h1></center>
<center><h3>Quincey Koziol<br>
koziol@ncsa.uiuc.edu<br>
August 20, 2003
</h3></center>
<ol class="upperroman">
<li><h3><u>Document's Audience:</u></h3>
<ul>
<li>Current H5 library designers and knowledgeable external developers.</li>
</ul>
</li><li><h3><u>Background Reading:</u></h3>
</li><li><h3><u>Introduction:</u></h3>
<dl>
<dt><strong>What is this document about?</strong></dt>
<dd>This document attempts to supplement the flow charts describing
the flow of control for raw data I/O in the library.
</dd> <br>
</dl>
</li><li><h3><u>Figures:</u></h3>
<p>The following figures provide the main information:</p>
<table>
<tr><td><img src="IOFlow.gif" alt="High-Level View of Writing Raw Data" style="height:50%;"></td></tr>
<tr><td><img src="IOFlow2.gif" alt="Perform Serial or Parallel I/O" style="height:50%;"></td></tr>
<tr><td><img src="IOFlow3.gif" alt="Gather/Convert/Scatter" style="height:50%;"></td></tr>
</table>
</li><li><h3><u>Notes From Accompanying Figures:</u></h3>
<p>This section provides notes to augment the information in the accompanying
figures.
</p>
<ol>
<li><b>Validate Parameters</b> - Resolve any H5S_ALL parameters
for dataspace selections to actual dataspaces, allocate
conversion buffers, etc.
</li>
<li><b>Space Allocated in File?</b> - Space may not have been allocated
in the file to store the dataset data, if "late allocation" was chosen
for the allocation time when the dataset was created.
</li>
<li><b>Allocate & Fill Space</b> - These operations allocate both contiguous
and chunked dataset's space in the file. The chunked dataset space
allocation iterates through all the chunks in the file and allocates
both the B-tree information and the raw data in the file. Because of
the way filters work, fill-values are written out for chunked datasets
as they are allocated, instead of as a separate step.
In parallel
I/O, the chunked dataset allocation can potentially be time-consuming,
since all the raw data in the dataset is allocated from one process.
</li>
<li><b>Datatype Conversion Needed?</b> - This currently is the deciding
factor between doing "direct I/O" (in serial or parallel) and needing
to perform gather/convert/scatter operations. I believe that MPI
is capable of performing a limited range of type conversions and if so,
we should add support to detect when they can be used. This will
allow more I/O operations to be performed collectively.
</li>
<li><b>Collective I/O Requested/Allowed?</b> - A user has to both request
that collective I/O occur and also their I/O operation must meet the
requirements that the library sets for supporting collective parallel
I/O:
<ul>
<li>The dataspace must be scalar or simple (which is a no-op really,
since we don't support "complex" dataspaces in the library
currently).
</li>
<li>The selection must be regular. "all" selections
and hyperslab selections that were
made with only one call to H5Sselect_hyperslab() (i.e. not a
hyperslab selection that has been aggregated over multiple
selection calls) are regular. Supporting point and
irregular hyperslab selections are on the "to do" list.
</li>
<li>The dataset must be stored contiguously on disk (as shown in the
figure also). Supporting chunked dataset storage is also
on the "to do" list.
</li>
</ul>
</li>
<li><b>Build "chunk map"</b> - This step still has some scalability issues
as it creates a data structure that is proportional to the number of
chunks which will be written to, which could potentially be very large.
Building the "chunk map" information incrementally is on the "to do"
list also.
</li>
<li><b>Perform Chunked I/O</b> - As the figure shows, there is no support
for collective parallel I/O on chunked datasets currently. As noted
earlier, this is on the "to do" list.
</li>
<li><b>Perform "Direct" Serial I/O</b> - "Direct" serial I/O writes data
from the application's buffer, without any intervening buffer or memory
copies. For maximum efficiency and performance, the elements in the
selections should be adjoining.
</li>
<li><b>Perform Collective Parallel I/O</b> - This step also writes data
directly from an application buffer, but additionally uses collective
MPI I/O operations to combine the data from each process in the parallel
application in an efficient manner.
</li>
</ol>
</li></ol>
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