march 9, 200910th international lci conference - hdf5 tutorial1 hdf5 advanced topics

March 9, 2009 10th International LCI Conference - HDF5 Tutorial 1

HDF5 Advanced Topics


Outline

• Part I• Overview of HDF5 datatypes

• Part II• Partial I/O in HDF5

• Hyperslab selection• Dataset region references

• Chunking and compression

• Part III• Performance issues (how to do it right)

• Part IV• Performance benefits of HDF5 version 1.8


Part IHDF5 Datatypes

Quick overview of the most difficult topics


HDF5 Datatypes

• HDF5 has a rich set of pre-defined datatypes and supports the creation of an unlimited variety of complex user-defined datatypes.

• Datatype definitions are stored in the HDF5 file with the data.

• Datatype definitions include information such as byte order (endianess), size, and floating point representation to fully describe how the data is stored and to insure portability across platforms.

• Datatype definitions can be shared among objects in an HDF file, providing a powerful and efficient mechanism for describing data.


Example

Array of integers on IA32 platformNative integer is little-endian, 4 bytes

H5T_SDT_I32LE

H5Dwrite

Array of integers on SPARC64 platformNative integer is big-endian, 8 bytes

H5T_NATIVE_INT H5T_NATIVE_INT

H5Dread

Little-endian 4 bytes integer

VAX G-floating

H5Dwrite


Storing Variable Length Data in HDF5


•Data

Time•Data

•Data

•Data

•Data

•Data

•Data

•Data

•Data

Time

HDF5 Fixed and Variable Length Array Storage


Storing Strings in HDF5

• Array of characters (Array datatype or extra dimension in dataset)• Quick access to each character• Extra work to access and interpret each string

• Fixed lengthstring_id = H5Tcopy(H5T_C_S1);H5Tset_size(string_id, size);

• Wasted space in shorter strings• Can be compressed

• Variable lengthstring_id = H5Tcopy(H5T_C_S1);H5Tset_size(string_id, H5T_VARIABLE);

• Overhead as for all VL datatypes• Compression will not be applied to actual data


Storing Variable Length Data in HDF5

• Each element is represented by C structure typedef struct {

size_t length;

void *p;

} hvl_t;

• Base type can be any HDF5 typeH5Tvlen_create(base_type)


•Data

•Data

•Data

•Data

•Data

Example

hvl_t data[LENGTH];

for(i=0; i<LENGTH; i++) { data[i].p=malloc((i+1)*sizeof(unsigned int)); data[i].len=i+1;

}

tvl = H5Tvlen_create (H5T_NATIVE_UINT);

data[0].p

data[4].len


Reading HDF5 Variable Length Array

hvl_t rdata[LENGTH];

/* Create the memory vlen type */

tvl = H5Tvlen_create (H5T_NATIVE_UINT);

ret = H5Dread(dataset,tvl,H5S_ALL,H5S_ALL,

H5P_DEFAULT, rdata);

/* Reclaim the read VL data */

H5Dvlen_reclaim(tvl,H5S_ALL,H5P_DEFAULT,rdata);

On read HDF5 Library allocates memory to read data in, application only needs to allocate array of hvl_t elements (pointers and lengths).


Storing Tables in HDF5 file


Example

a_name (integer)

b_name

(float)

c_name (double)

0 0. 1.0000

1 1. 0.5000

2 4. 0.3333

3 9. 0.2500

4 16. 0.2000

5 25. 0.1667

6 36. 0.1429

7 49. 0.1250

8 64. 0.1111

9 81. 0.1000

Multiple ways to store a table Dataset for each field Dataset with compound datatype If all fields have the same type: 2-dim array 1-dim array of array datatype continued…..Choose to achieve your goal!How much overhead each type of storage will create?Do I always read all fields?Do I need to read some fields more often?Do I want to use compression?Do I want to access some records?


HDF5 Compound Datatypes

• Compound types• Comparable to C structs • Members can be atomic or compound

types • Members can be multidimensional• Can be written/read by a field or set of

fields• Not all data filters can be applied (shuffling,

SZIP)


HDF5 Compound Datatypes

• Which APIs to use?• H5TB APIs

• Create, read, get info and merge tables• Add, delete, and append records• Insert and delete fields• Limited control over table’s properties (i.e. only GZIP

compression, level 6, default allocation time for table, extendible, etc.)

• PyTables http://www.pytables.org• Based on H5TB• Python interface• Indexing capabilities

• HDF5 APIs • H5Tcreate(H5T_COMPOUND), H5Tinsert calls to create a

compound datatype• H5Dcreate, etc.• See H5Tget_member* functions for discovering properties of the

HDF5 compound datatype

http://www.pytables.org/


Creating and Writing Compound Dataset

h5_compound.c example

typedef struct s1_t { int a; float b; double c; } s1_t;

s1_t s1[LENGTH];



/* Create datatype in memory. */

s1_tid = H5Tcreate (H5T_COMPOUND, sizeof(s1_t)); H5Tinsert(s1_tid, "a_name", HOFFSET(s1_t, a), H5T_NATIVE_INT); H5Tinsert(s1_tid, "c_name", HOFFSET(s1_t, c), H5T_NATIVE_DOUBLE); H5Tinsert(s1_tid, "b_name", HOFFSET(s1_t, b), H5T_NATIVE_FLOAT);

Note: • Use HOFFSET macro instead of calculating offset by hand.• Order of H5Tinsert calls is not important if HOFFSET is used.



/* Create dataset and write data */

dataset = H5Dcreate(file, DATASETNAME, s1_tid, space, H5P_DEFAULT, H5P_DEFAULT);status = H5Dwrite(dataset, s1_tid, H5S_ALL, H5S_ALL, H5P_DEFAULT, s1);

Note: • In this example memory and file datatypes are the same.• Type is not packed. • Use H5Tpack to save space in the file.

status = H5Tpack(s1_tid);status = H5Dcreate(file, DATASETNAME, s1_tid, space, H5P_DEFAULT, H5P_DEFAULT);


File Content with h5dump

HDF5 "SDScompound.h5" {GROUP "/" { DATASET "ArrayOfStructures" { DATATYPE { H5T_STD_I32BE "a_name"; H5T_IEEE_F32BE "b_name"; H5T_IEEE_F64BE "c_name"; } DATASPACE { SIMPLE ( 10 ) / ( 10 ) } DATA { { [ 0 ], [ 0 ], [ 1 ] }, { [ 1 ], …


Reading Compound Dataset

/* Create datatype in memory and read data. */

dataset = H5Dopen(file, DATASETNAME, H5P_DEFAULT);s2_tid = H5Dget_type(dataset);mem_tid = H5Tget_native_type (s2_tid);s1 = malloc(H5Tget_size(mem_tid)*number_of_elements); status = H5Dread(dataset, mem_tid, H5S_ALL, H5S_ALL, H5P_DEFAULT, s1);

Note:

• We could construct memory type as we did in writing example.

• For general applications we need to discover the type in the file, find out corresponding memory type, allocate space and do read.


Reading Compound Dataset by Fields

typedef struct s2_t { double c; int a;} s2_t; s2_t s2[LENGTH];…s2_tid = H5Tcreate (H5T_COMPOUND, sizeof(s2_t)); H5Tinsert(s2_tid, "c_name", HOFFSET(s2_t, c), H5T_NATIVE_DOUBLE); H5Tinsert(s2_tid, “a_name", HOFFSET(s2_t, a), H5T_NATIVE_INT);…status = H5Dread(dataset, s2_tid, H5S_ALL, H5S_ALL, H5P_DEFAULT, s2);


New Way of Creating Datatypes

Another way to create a compound datatype

#include H5LTpublic.h…..

s2_tid = H5LTtext_to_dtype( "H5T_COMPOUND {H5T_NATIVE_DOUBLE \"c_name\"; H5T_NATIVE_INT \"a_name\"; }", H5LT_DDL);


Need Help with Datatypes?

Check our support web pages

http://www.hdfgroup.uiuc.edu/UserSupport/examples-by-api/api18-c.html







Part IIWorking with subsets

Collect data one way ….

Array of images (3D)

March 9, 2009 2510th International LCI Conference - HDF5 Tutorial

Stitched image (2D array)

Display data another way …


Data is too big to read….


Need to select and access the same elements of a dataset

Refer to a region…



HDF5 Library Features

• HDF5 Library provides capabilities to• Describe subsets of data and perform write/read

operations on subsets• Hyperslab selections and partial I/O

• Store descriptions of the data subsets in a file• Object references• Region references

• Use efficient storage mechanism to achieve good performance while writing/reading subsets of data

• Chunking, compression


Partial I/O in HDF5


How to Describe a Subset in HDF5?

• Before writing and reading a subset of data one has to describe it to the HDF5 Library.

• HDF5 APIs and documentation refer to a subset as a “selection” or “hyperslab selection”.

• If specified, HDF5 Library will perform I/O on a selection only and not on all elements of a dataset.


Types of Selections in HDF5

• Two types of selections• Hyperslab selection

• Regular hyperslab• Simple hyperslab• Result of set operations on hyperslabs (union,

difference, …)

• Point selection

• Hyperslab selection is especially important for doing parallel I/O in HDF5 (See Parallel HDF5 Tutorial)


Regular Hyperslab

Collection of regularly spaced equal size blocks


Simple Hyperslab

Contiguous subset or sub-array


Hyperslab Selection

Result of union operation on three simple hyperslabs


Hyperslab Description

• Start - starting location of a hyperslab (1,1)• Stride - number of elements that separate each

block (3,2)• Count - number of blocks (2,6)• Block - block size (2,1)• Everything is “measured” in number of elements


Simple Hyperslab Description

• Two ways to describe a simple hyperslab• As several blocks

• Stride – (1,1)• Count – (2,6)• Block – (2,1)

• As one block• Stride – (1,1)• Count – (1,1)• Block – (4,6)

No performance penalty for one way or another


H5Sselect_hyperslab Function

space_id Identifier of dataspace

op Selection operatorH5S_SELECT_SET or H5S_SELECT_OR

start Array with starting coordinates of hyperslab stride Array specifying which positions along a dimension to select count Array specifying how many blocks to select from the

dataspace, in each dimension block Array specifying size of element block

(NULL indicates a block size of a single element in

a dimension)


Reading/Writing Selections

Programming model for reading from a dataset in

a file1. Open a dataset.

2. Get file dataspace handle of the dataset and specify subset to read from.a. H5Dget_space returns file dataspace handle

a. File dataspace describes array stored in a file (number of dimensions and their sizes).

b. H5Sselect_hyperslab selects elements of the array that participate in I/O operation.

3. Allocate data buffer of an appropriate shape and size


Reading/Writing Selections

Programming model (continued)4. Create a memory dataspace and specify subset to write

to.1. Memory dataspace describes data buffer (its rank and

dimension sizes).

2. Use H5Screate_simple function to create memory dataspace.

3. Use H5Sselect_hyperslab to select elements of the data buffer that participate in I/O operation.

5. Issue H5Dread or H5Dwrite to move the data between file and memory buffer.

6. Close file dataspace and memory dataspace when done.


Example : Reading Two Rows

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1

Data in a file4x6 matrix

Buffer in memory1-dim array of length 14


Example: Reading Two Rows

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

start = {1,0}count = {2,6}block = {1,1}stride = {1,1}

filespace = H5Dget_space (dataset);H5Sselect_hyperslab (filespace, H5S_SELECT_SET, start, NULL, count, NULL)



-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1

start[1] = {1}count[1] = {12}dim[1] = {14}

memspace = H5Screate_simple(1, dim, NULL);H5Sselect_hyperslab (memspace, H5S_SELECT_SET, start, NULL, count, NULL)



1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

-1 7 8 9 10 11 12 13 14 15 16 17 18 -1

H5Dread (…, …, memspace, filespace, …, …);


Things to Remember

• Number of elements selected in a file and in a memory buffer must be the same • H5Sget_select_npoints returns number of

selected elements in a hyperslab selection

• HDF5 partial I/O is tuned to move data between selections that have the same dimensionality; avoid choosing subsets that have different ranks (as in example above)

• Allocate a buffer of an appropriate size when reading data; use H5Tget_native_type and H5Tget_size to get the correct size of the data element in memory.


HDF5 Region References and Selections

Need to select and access the same elements of a dataset

Saving Selected Region in a File



Reference Datatype

• Reference to an HDF5 object• Pointer to a group or a dataset in a file

• Predefined datatype H5T_STD_REG_OBJ describe object references

• Reference to a dataset region (or to selection)• Pointer to the dataspace selection

• Predefined datatype H5T_STD_REF_DSETREG to describe regions


Reference to Dataset Region

REF_REG.h5

Root

Region ReferencesMatrix

1 1 2 3 3 4 5 5 61 2 2 3 4 4 5 6 6


Reference to Dataset Region

Example

dsetr_id = H5Dcreate(file_id, “REGION REFERENCES”, H5T_STD_REF_DSETREG, …);

H5Sselect_hyperslab(space_id, H5S_SELECT_SET, start, NULL, …);H5Rcreate(&ref[0], file_id, “MATRIX”,H5R_DATASET_REGION, space_id);

H5Dwrite(dsetr_id, H5T_STD_REF_DSETREG, H5S_ALL, H5S_ALL, H5P_DEFAULT,ref);


Reference to Dataset RegionHDF5 "REF_REG.h5" {GROUP "/" { DATASET "MATRIX" { …… } DATASET "REGION_REFERENCES" { DATATYPE H5T_REFERENCE DATASPACE SIMPLE { ( 2 ) / ( 2 ) } DATA { (0): DATASET /MATRIX {(0,3)-(1,5)}, (1): DATASET /MATRIX {(0,0), (1,6), (0,8)} } }}}


Chunking in HDF5


HDF5 Chunking

• Dataset data is divided into equally sized blocks (chunks).• Each chunk is stored separately as a contiguous block in

HDF5 file.

Application memory

Metadata cacheDataset headerDataset header

………….Datatype

Dataspace………….Attributes

…

File

Dataset data

A DC BheaderChunkindex

Chunkindex

A B C D


HDF5 Chunking

• Chunking is needed for• Enabling compression and other filters

• Extendible datasets


HDF5 Chunking

• If used appropriately chunking improves partial I/O for big datasets

Only two chunks are involved in I/O


HDF5 Chunking

• Chunk has the same rank as a dataset• Chunk’s dimensions do not need to be factors of

dataset’s dimensions


Creating Chunked Dataset

1. Create a dataset creation property list.2. Set property list to use chunked storage layout.3. Create dataset with the above property list.

dcpl_id = H5Pcreate(H5P_DATASET_CREATE); rank = 2; ch_dims[0] = 100; ch_dims[1] = 100; H5Pset_chunk(dcpl_id, rank, ch_dims); dset_id = H5Dcreate (…, dcpl_id); H5Pclose(dcpl_id);


Writing or Reading Chunked Dataset

1. Chunking mechanism is transparent to application.

2. Use the same set of operation as for contiguous dataset, for example,

H5Dopen(…);

H5Sselect_hyperslab (…);

H5Dread(…);

3. Selections do not need to coincide precisely with the chunks boundaries.


HDF5 Filters

• HDF5 filters modify data during I/O operations• Available filters:

1. Checksum (H5Pset_fletcher32)2. Shuffling filter (H5Pset_shuffle)3. Data transformation (in 1.8.*)4. Compression

• Scale + offset (in 1.8.*)• N-bit (in 1.8.*)• GZIP (deflate), SZIP (H5Pset_deflate, H5Pset_szip)• User-defined filters (BZIP2)

• Example of a user-defined compression filter can be found http://www.hdfgroup.uiuc.edu/papers/papers/bzip2/

http://www.hdfgroup.uiuc.edu/papers/papers/bzip2/


Creating Compressed Dataset

1. Create a dataset creation property list2. Set property list to use chunked storage layout3. Set property list to use filters4. Create dataset with the above property list

crp_id = H5Pcreate(H5P_DATASET_CREATE); rank = 2; ch_dims[0] = 100; ch_dims[1] = 100; H5Pset_chunk(crp_id, rank, ch_dims); H5Pset_deflate(crp_id, 9); dset_id = H5Dcreate (…, crp_id); H5Pclose(crp_id);


Writing Compressed Dataset

C BA

…………..

Default chunk cache size is 1MB. Filters including compression are applied when chunk is evicted from cache.Chunks in the file may have different sizes

AB C

C

File

Chunk cache (per dataset)Chunked dataset

Filter pipeline


Chunking Basics to Remember

• Chunking creates storage overhead in the file.• Performance is affected by

• Chunking and compression parameters • Chunking cache size (H5Pset_cache call)

• Some hints for getting better performance• Use chunk size not smaller than block size (4k) on

a file system.• Use compression method appropriate for your

data.• Avoid using selections that do not coincide with

the chunking boundaries.


Example

Creates a compressed 1000x20 integer dataset in a file

%h5dump –p –H zip.h5

HDF5 "zip.h5" {GROUP "/" { GROUP "Data" { DATASET "Compressed_Data" { DATATYPE H5T_STD_I32BE DATASPACE SIMPLE { ( 1000, 20 )……… STORAGE_LAYOUT { CHUNKED ( 20, 20 ) SIZE 5316 }


Example (continued)

FILTERS { COMPRESSION DEFLATE { LEVEL 6 } } FILLVALUE { FILL_TIME H5D_FILL_TIME_IFSET VALUE 0 } ALLOCATION_TIME { H5D_ALLOC_TIME_INCR } } }}}


Example (bigger chunk)

Creates a compressed integer dataset 1000x20 in afile; better compression ratio is achieved.

h5dump –p –H zip.h5

HDF5 "zip.h5" {GROUP "/" { GROUP "Data" { DATASET "Compressed_Data" { DATATYPE H5T_STD_I32BE DATASPACE SIMPLE { ( 1000, 20 )……… STORAGE_LAYOUT { CHUNKED ( 200, 20 ) SIZE 2936 }


Part IIIPerformance Issues(How to Do it Right)


Performance of Serial I/O Operations

• Next slides show the performance effects of using different access patterns and storage layouts.

• We use three test cases which consist of writing a selection to an array of characters.

• Data is stored in a row-major order.• Tests were executed on THG Linux x86_64 box

using h5perf_serial and HDF5 version 1.8.0


Serial Benchmarking Tool

• Benchmarking tool, h5perf_serial, publicly released with HDF5 1.8.1

• Features inlcude:• Support for POSIX and HDF5 I/O calls.• Support for datasets and buffers with multiple

dimensions.• Entire dataset access using a single or several I/O

operations.• Selection of contiguous and chunked storage for HDF5

operations.


Contiguous Storage (Case 1)

• Rectangular dataset of size 48K x 48K, with write selections of 512 x 48K.

• HDF5 storage layout is contiguous.• Good I/O pattern for POSIX and

HDF5 because each selection is contiguous.

• POSIX: 5.19 MB/s• HDF5: 5.36 MB/s

1

2

3

4

1 2 3 4


Contiguous Storage (Case 2)

• Rectangular dataset of 48K x 48K, with write selections of 48K x 512.

• HDF5 storage layout is contiguous.

• Bad I/O pattern for POSIX and HDF5 because each selection is noncontiguous.


1 2 3 4

1 2 3 4 1 2 3 4 …….


Chunked Storage

• Rectangular dataset of 48K x 48K, with write selections of 48K x 512.

• HDF5 storage layout is chunked. Chunks and selections sizes are equal.

• Bad I/O case for POSIX because selections are noncontiguous.

• Good I/O case for HDF5 since selections are contiguous due to chunking layout settings.


1 2 3 4

1 2 3 4

1 2 3 4 1 2 3 4 …….

POSIX

HDF5


Conclusions

• Access patterns with small I/O operations incur high latency and overhead costs many times.

• Chunked storage may improve I/O performance by affecting the contiguity of the data selection.

Writing Chunked Dataset

• 1000x100x100 dataset• 4 byte integers

• Random values 0-99

• 50x100x100 chunks (20 total)• Chunk size: 2 MB

• Write the entire dataset using 1x100x100 slices• Slices are written sequentially


Test Setup

• 20 Chunks

• 1000 slices• Chunk size is 2MB


Test Setup (continued)

• Tests performed with 1 MB and 5MB chunk cache size• Cache size set with H5Pset_cache function

H5Pget_cache (fapl, NULL, &rdcc_nelmts,

&rdcc_nbytes, &rdcc_w0);

H5Pset_cache (fapl, 0, rdcc_nelmts,

5*1024*1024, rdcc_w0);

• Tests performed with no compression and with gzip (deflate) compression


Effect of Chunk Cache Size on Write

Cache size I/O operations Total data written

File size

1 MB (default) 1002 75.54 MB 38.15 MB

5 MB 22 38.16 MB 38.15 MB

No compression

Gzip compression

Cache size I/O operations Total data written

File size

1 MB (default) 1982 335.42 MB(322.34 MB read)

13.08 MB

5 MB 22 13.08 MB 13.08 MB



• With the 1 MB cache size, a chunk will not fit into the cache• All writes to the dataset must be immediately

written to disk• With compression, the entire chunk must be read

and rewritten every time a part of the chunk is written to

• Data must also be decompressed and recompressed each time

• Non sequential writes could result in a larger file• Without compression, the entire chunk must be

written when it is first written to the file• If the selection were not contiguous on disk, it could

require as much as 1 I/O operation for each elementMarch 9, 2009 7710th International LCI Conference - HDF5 Tutorial


• With the 5 MB cache size, the chunk is written only after it is full• Drastically reduces the number of I/O operations

• Reduces the amount of data that must be written (and read)

• Reduces processing time, especially with the compression filter


Conclusion

• It is important to make sure that a chunk will fit into the raw data chunk cache

• If you will be writing to multiple chunks at once, you should increase the cache size even more• Try to design chunk dimensions to minimize the

number you will be writing to at once


Reading Chunked Dataset

• Read the same dataset, again by slices, but the slices cross through all the chunks

• 2 orientations for read plane• Plane includes fastest changing dimension

• Plane does not include fastest changing dimension

• Measure total read operations, and total size read• Chunk sizes of 50x100x100, and 10x100x100• 1 MB cache


• Chunks

• Read slices• Vertical and horizontal

Test Setup


Results

• Read slice includes fastest changing dimension

Chunk size Compression I/O operations Total data read

50 Yes 2010 1307 MB

10 Yes 10012 1308 MB

50 No 100010 38 MB

10 No 10012 3814 MB


Results (continued)

• Read slice does not include fastest changing dimension

Chunk size Compression I/O operations Total data read

50 Yes 2010 1307 MB

10 Yes 10012 1308 MB

50 No 10000010 38 MB

10 No 10012 3814 MB


Effect of Cache Size on Read

• When compression is enabled, the library must always read each entire chunk once for each call to H5Dread.

• When compression is disabled, the library’s behavior depends on the cache size relative to the chunk size.• If the chunk fits in cache, the library reads each

entire chunk once for each call to H5Dread• If the chunk does not fit in cache, the library reads

only the data that is selected• More read operations, especially if the read plane

does not include the fastest changing dimension• Less total data read


Conclusion

• In this case cache size does not matter when reading if compression is enabled.

• Without compression, a larger cache may not be beneficial, unless the cache is large enough to hold all of the chunks.• The optimum cache size depends on the exact

shape of the data, as well as the hardware.


Hints for Chunk Settings

• Chunk dimensions should align as closely as possible with hyperslab dimensions for read/write

• Chunk cache size (rdcc_nbytes) should be large enough to hold all the chunks in the selection• If this is not possible, it may be best to disable chunk

caching altogether (set rdcc_nbytes to 0)

• rdcc_nelmts should be a prime number that is at least 10 to 100 times the number of chunks that can fit into rdcc_nbytes

• rdcc_w0 should be set to 1 if chunks that have been fully read/written will never be read/written again



Part IVPerformance Benefits of

HDF5 version 1.8

What Did We Do in HDF5 1.8?

• Extended File Format Specification • Reviewed group implementations• Introduced new link object• Revamped metadata cache implementation• Improved handling of datasets and datatypes• Introduced shared object header message• Extended error handling• Enhanced backward/forward APIs and file format

compatibility


What Did We Do in HDF5 1.8?

And much more good stuff to make HDF5


Better and Faster


HDF5 File Format Extension



• Why: • Address deficiencies of the original file format

• Address space overhead in an HDF5 file

• Enable new features

• What: • New routine that instructs the HDF5 library to

create all objects using the latest version of the HDF5 file format (cmp. with the earliest version when object became available, for example, array datatype)



Example

/* Use the latest version of a file format for each object created in a file */

fapl_id = H5Pcreate(H5P_FILE_ACCESS);H5Pset_libver_bounds(fapl_id, H5F_LIBVER_LATEST, H5F_LIBVER_LATEST);fid = H5Fcreate(…,…,…,fapl_id);orfid = H5Fopen(…,…,fapl_id);


Group Revisions


Better Large Group Storage

• Why: • Faster, more scalable storage and access for large

groups

• What: • New format and method for storing groups with

many links


Informal Benchmark

• Create a file and a group in a file• Create up to 10^6 groups with one dataset in

each group• Compare files sizes and performance of HDF5

1.8.1 using the latest group format with the performance of HDF5 1.8.1 (default, old format) and 1.6.7

• Note: Default 1.8.1 and 1.6.7 became very slow after 700000 groups

Time to Open and Read a Dataset


File Size



Questions?

march 9, 200910th international lci conference - hdf5 tutorial1 hdf5 advanced topics

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