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authorRobb Matzke <matzke@llnl.gov>1998-10-23 15:44:52 (GMT)
committerRobb Matzke <matzke@llnl.gov>1998-10-23 15:44:52 (GMT)
commit9bba487ca4dc89140684da78cd541aba4403df53 (patch)
tree0baaf93789a3e8572f1ddacc498bed2425660c82
parente1d1bf607f2128d0066b6f4a50c61d91e8732db7 (diff)
downloadhdf5-9bba487ca4dc89140684da78cd541aba4403df53.zip
hdf5-9bba487ca4dc89140684da78cd541aba4403df53.tar.gz
hdf5-9bba487ca4dc89140684da78cd541aba4403df53.tar.bz2
[svn-r780] *** empty log message ***
-rw-r--r--doc/html/Chunk_f1.gifbin0 -> 3664 bytes
-rw-r--r--doc/html/Chunk_f1.obj252
-rw-r--r--doc/html/Chunk_f2.gifbin0 -> 3986 bytes
-rw-r--r--doc/html/Chunk_f2.obj95
-rw-r--r--doc/html/Chunk_f3.gifbin0 -> 6815 bytes
-rw-r--r--doc/html/Chunk_f4.gifbin0 -> 5772 bytes
-rw-r--r--doc/html/Chunk_f5.gifbin0 -> 5455 bytes
-rw-r--r--doc/html/Chunk_f6.gifbin0 -> 4949 bytes
-rw-r--r--doc/html/Chunk_f6.obj107
-rw-r--r--doc/html/Chunking.html193
10 files changed, 647 insertions, 0 deletions
diff --git a/doc/html/Chunk_f1.gif b/doc/html/Chunk_f1.gif
new file mode 100644
index 0000000..d73201a
--- /dev/null
+++ b/doc/html/Chunk_f1.gif
Binary files differ
diff --git a/doc/html/Chunk_f1.obj b/doc/html/Chunk_f1.obj
new file mode 100644
index 0000000..004204a
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diff --git a/doc/html/Chunk_f2.gif b/doc/html/Chunk_f2.gif
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diff --git a/doc/html/Chunk_f2.obj b/doc/html/Chunk_f2.obj
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diff --git a/doc/html/Chunk_f3.gif b/doc/html/Chunk_f3.gif
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Binary files differ
diff --git a/doc/html/Chunk_f4.gif b/doc/html/Chunk_f4.gif
new file mode 100644
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Binary files differ
diff --git a/doc/html/Chunk_f5.gif b/doc/html/Chunk_f5.gif
new file mode 100644
index 0000000..3b12174
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Binary files differ
diff --git a/doc/html/Chunk_f6.gif b/doc/html/Chunk_f6.gif
new file mode 100644
index 0000000..616946d
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diff --git a/doc/html/Chunking.html b/doc/html/Chunking.html
new file mode 100644
index 0000000..ccf4f9e
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+++ b/doc/html/Chunking.html
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+<!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>