[svn-r780] *** empty log message ***

10 files changed, 647 insertions, 0 deletions

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diff --git a/doc/html/Chunking.html b/doc/html/Chunking.html
new file mode 100644
index 0000000..ccf4f9e
--- /dev/null
+++ b/doc/html/Chunking.html
@@ -0,0 +1,193 @@
+<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
+<html>
+  <head>
+    <title>Dataset Chunking Pitfalls</title>
+  </head>
+
+  <body>
+    <h1>Dataset Chunking Pitfalls</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>
+    </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_split_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).
+
+    <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.
+
+
+
+    <hr>
+    <address><a href="mailto:matzke@llnl.gov">Robb Matzke</a></address>
+<!-- Created: Tue Oct 20 12:38:40 EDT 1998 -->
+<!-- hhmts start -->
+Last modified: Fri Oct 23 10:30:52 EDT 1998
+<!-- hhmts end -->
+  </body>
+</html>