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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
<html>
  <head>
    <title>Dataset Chunking</title>
  </head>

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    <h1>Dataset Chunking Issues</h1>

    <h2>Table of Contents</h2>

    <ul>
      <li><a href="#S1">1. Introduction</a>
      <li><a href="#S2">2. Raw Data Chunk Cache</a>
      <li><a href="#S3">3. Cache Efficiency</a>
      <li><a href="#S4">4. Fragmentation</a>
      <li><a href="#S5">5. File Storage Overhead</a>
      <li><a href="#S6">6. Chunk Compression</a>
    </ul>

    <h2><a name="S1">1. Introduction</a></h2>


    <p><em>Chunking</em> refers to a storage layout where a dataset is
      partitioned into fixed-size multi-dimensional chunks.  The
      chunks cover the dataset but the dataset need not be an integral
      number of chunks.  If no data is ever written to a chunk then
      that chunk isn't allocated on disk.  Figure 1 shows a 25x48
      element dataset covered by nine 10x20 chunks and 11 data points
      written to the dataset.  No data was written to the region of
      the dataset covered by three of the chunks so those chunks were
      never allocated in the file -- the other chunks are allocated at 
      independent locations in the file and written in their entirety.

      <center><image src="Chunk_f1.gif"><br><b>Figure 1</b></center>

    <p>The HDF5 library treats chunks as atomic objects -- disk I/O is 
      always in terms of complete chunks<a href="#fn1">(1)</a>. This
      allows data filters to be defined by the application to perform
      tasks such as compression, encryption, checksumming,
      <em>etc</em>. on entire chunks. As shown in Figure 2, if
      <code>H5Dwrite()</code> touches only a few bytes of the chunk,
      the entire chunk is read from the file, the data passes upward
      through the filter pipeline, the few bytes are modified, the
      data passes downward through the filter pipeline, and the entire
      chunk is written back to the file.

      <center><image src="Chunk_f2.gif"><br><b>Figure 2</b></center>

    <h2><a name="S2">2. The Raw Data Chunk Cache</a></h2>

    <p>It's obvious from Figure 2 that calling <code>H5Dwrite()</code> 
      many times from the application would result in poor performance
      even if the data being written all falls within a single chunk.
      A raw data chunk cache layer was added between the top of the
      filter stack and the bottom of the byte modification layer<a
      href="#fn2">(2)</a>. By default, the chunk cache will store 521
      chunks or 1MB of data (whichever is less) but these values can
      be modified with <code>H5Pset_cache()</code>.

    <p>The preemption policy for the cache favors certain chunks and
      tries not to preempt them.

    <ul>
      <li>Chunks that have been accessed frequently in the near past
	are favored.
      <li>A chunk which has just entered the cache is favored.
      <li>A chunk which has been completely read or completely written 
	but not partially read or written is penalized according to
	some application specified weighting between zero and one.
      <li>A chunk which is larger than the maximum cache size is not
	eligible for caching.
    </ul>

    <h2><a name="S3">3. Cache Efficiency</a></h2>

    <p>Now for some real numbers... A 2000x2000 element dataset is
      created and covered by a 20x20 array of chunks (each chunk is 100x100
      elements). The raw data cache is adjusted to hold at most 25 chunks by
      setting the maximum number of bytes to 25 times the chunk size in
      bytes. Then the application creates a square, two-dimensional memory
      buffer and uses it as a window into the dataset, first reading and then
      rewriting in row-major order by moving the window across the dataset
      (the read and write tests both start with a cold cache).

    <p>The measure of efficiency in Figure 3 is the number of bytes requested
      by the application divided by the number of bytes transferred from the
      file.  There are at least a couple ways to get an estimate of the cache
      performance: one way is to turn on <a href="Debugging.html">cache
      debugging</a> and look at the number of cache misses.  A more accurate
      and specific way is to register a data filter whose sole purpose is to
      count the number of bytes that pass through it (that's the method used
      below).

    <center><image src="Chunk_f3.gif"><br><b>Figure 3</b></center>

    <p>The read efficiency is less than one for two reasons: collisions in the
      cache are handled by preempting one of the colliding chunks, and the
      preemption algorithm occasionally preempts a chunk which hasn't been
      referenced for a long time but is about to be referenced in the near
      future.

    <p>The write test results in lower efficiency for most window
      sizes because HDF5 is unaware that the application is about to
      overwrite the entire dataset and must read in most chunks before 
      modifying parts of them.

    <p>There is a simple way to improve efficiency for this example.
      It turns out that any chunk that has been completely read or
      written is a good candidate for preemption.  If we increase the
      penalty for such chunks from the default 0.75 to the maximum
      1.00 then efficiency improves.

      <center><image src="Chunk_f4.gif"><br><b>Figure 4</b></center>

    <p>The read efficiency is still less than one because of
      collisions in the cache.  The number of collisions can often be
      reduced by increasing the number of slots in the cache.  Figure
      5 shows what happens when the maximum number of slots is
      increased by an order of magnitude from the default (this change 
      has no major effect on memory used by the test since the byte
      limit was not increased for the cache).

      <center><image src="Chunk_f5.gif"><br><b>Figure 5</b></center>

    <p>Although the application eventually overwrites every chunk
      completely the library has know way of knowing this before hand
      since most calls to <code>H5Dwrite()</code> modify only a
      portion of any given chunk. Therefore, the first modification of 
      a chunk will cause the chunk to be read from disk into the chunk 
      buffer through the filter pipeline.  Eventually HDF5 might
      contain a data set transfer property that can turn off this read 
      operation resulting in write efficiency which is equal to read
      efficiency.


    <h2><a name="S4">4. Fragmentation</a></h2>

    <p>Even if the application transfers the entire dataset contents with a
      single call to <code>H5Dread()</code> or <code>H5Dwrite()</code> it's
      possible the request will be broken into smaller, more manageable
      pieces by the library.  This is almost certainly true if the data
      transfer includes a type conversion.

      <center><image src="Chunk_f6.gif"><br><b>Figure 6</b></center>

    <p>By default the strip size is 1MB but it can be changed by calling
      <code>H5Pset_buffer()</code>.


    <h2><a name="S5">5. File Storage Overhead</a></h2>

    <p>The chunks of the dataset are allocated at independent
      locations throughout the HDF5 file and a B-tree maps chunk
      N-dimensional addresses to file addresses. The more chunks that
      are allocated for a dataset the larger the B-tree. Large B-trees
      have two disadvantages:

    <ul>
      <li>The file storage overhead is higher and more disk I/O is
	required to traverse the tree from root to leaves.
      <li>The increased number of B-tree nodes will result in higher
	contention for the meta data cache.
    </ul>

    <p>There are three ways to reduce the number of B-tree nodes.  The
      obvious way is to reduce the number of chunks by choosing a larger chunk
      size (doubling the chunk size will cut the number of B-tree nodes in
      half).  Another method is to adjust the split ratios for the B-tree by
      calling <code>H5Pset_btree_ratios()</code>, but this method typically
      results in only a slight improvement over the default settings.
      Finally, the out-degree of each node can be increased by calling
      <code>H5Pset_istore_k()</code> (increasing the out degree actually
      increases file overhead while decreasing the number of nodes).


    <h2><a name="S6">6. Chunk Compression</a></h2>

    <p>Dataset chunks can be compressed through the use of filters.
     See the chapter &ldquo;<a href="Filters.html">Filters in HDF5</a>.&rdquo;

    <p>Reading and rewriting compressed chunked data can result in holes 
     in an HDF5 file.  In time, enough such holes can increase the 
     file size enough to impair application or library performance 
     when working with that file.  See
     &ldquo;<a href="Performance.html#Freespace">Freespace Management</a>&rdquo;
     in the chapter
     &ldquo;<a href="Performance.html">Performance Analysis and Issues</a>.&rdquo;


<hr>

    <p><a name="fn1">Footnote 1:</a> Parallel versions of the library
      can access individual bytes of a chunk when the underlying file
      uses MPI-IO.

    <p><a name="fn2">Footnote 2:</a> The raw data chunk cache was
      added before the second alpha release.

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