HDF5 Life cycle of data

1. HDF5Life cycle of data HDF and HDF-EOS Workshop X, Landover, MD

2. Outline • “Life cycle” of HDF5 data • I/O operations for datasets with different storage layouts • Compact dataset • Contiguous dataset • Datatype conversion • Partial I/O for contiguous dataset • Chunked dataset • I/O for chunked dataset • Variable length datasets and I/O HDF and HDF-EOS Workshop X, Landover, MD

3. Life cycle of HDF5 data • Life cycle: what does happen to data when it is transferred from application buffer to HDF5 file? Application Data buffer Object API H5Dwrite Library internals Magic box Virtual file I/O Unbuffered I/O File or other “storage” Data in a file HDF and HDF-EOS Workshop X, Landover, MD

4. “Life cycle” of HDF5 data: inside the magic box • Operations on data inside the magic box • Datatype conversion • Scattering - gathering • Data transformation (filters, compression) • Copying to/from internal buffers • Concepts involved • HDF5 metadata, metadata cache • Chunking, chunk cache • Data structures used • B-trees (groups, dataset chunks) • Hash tables • Local and Global heaps (variable length data: link names, strings, etc.) HDF and HDF-EOS Workshop X, Landover, MD

5. “Life cycle” of HDF5 data: inside the magic box • Understanding of what is happening to data inside the magic box will help to write efficient applications • HDF5 library has mechanisms to control behavior inside the magic box • Goals of this and the next talk are to • Introduce the basic concepts and internal data structures and explain how they affect performance and storage sizes • Give some “recipes” for how to improve performance HDF and HDF-EOS Workshop X, Landover, MD

6. Operations on data inside the magic box • Datatype conversion • Examples: • float  integer • LE  BE • 64-bit integer to 16-bit integer (overflow may occur!) • Scattering - gathering • Data is scattered/gathered from/to user’s buffers into internal buffers for datatype conversion and partial I/O • Data transformation (filters, compression) • Checksum on raw data and metadata (in 1.8.0) • Algebraic transform • GZIP and SZIP compressions • User-defined filters • Copying to/from internal buffers HDF and HDF-EOS Workshop X, Landover, MD

7. “Life cycle” of HDF5 data: inside the magic box • HDF5 metadata • Information about HDF5 objects used by the library • Examples: object headers, B-tree nodes for group, B-Tree nodes for chunks, heaps, super-block, etc. • Usually small compared to raw data sizes (KB vs. MB-GB) • Metadata cache • Space allocated to handle pieces of the HDF5 metadata • Allocated by the HDF5 library in application’s memory space • Cache behavior affects overall performance • Will cover in the next talk HDF and HDF-EOS Workshop X, Landover, MD

8. “Life cycle” of HDF5 data: inside the magic box • Chunking mechanism • Chunking – storage layout where a dataset is partitioned in fixed-size multi-dimensional tiles or chunks • Used for extendible datasets and datasets with filters applied (checksum, compression) • HDF5 library treats each chunk as atomic object • Greatly affects performance and file sizes • Chunk cache • Created for each chunked dataset • Default size 1MB HDF and HDF-EOS Workshop X, Landover, MD

9. Metadata Data Dataspace Rank Dimensions 3 Dim_1 = 4 Dim_2 = 5 Dim_3 = 7 Datatype IEEE 32-bit float Attributes Storage info Time = 32.4 Chunked Pressure = 987 Compressed Temp = 56 Writing a dataset HDF and HDF-EOS Workshop X, Landover, MD

10. I/O operations for HDF5 datasets with different storage layouts • Storage layouts • Compact • Contiguous • Chunked • I/O performance depends on • Dataset storage properties • Chunking strategy • Metadata cache performance • Etc. HDF and HDF-EOS Workshop X, Landover, MD

11. Writing a compact dataset Application memory Metadata cache Dataset header …………. Datatype Dataspace …………. Attribute 1 Attribute 2 Data Raw data is stored within the dataset header File HDF and HDF-EOS Workshop X, Landover, MD

12. Writing a contiguous dataset with no datatype conversion Metadata cache Dataset header User buffer (matrix 5x4x7) …………. Datatype Dataspace …………. Attribute 1 Attribute 2 ………… File HDF and HDF-EOS Workshop X, Landover, MD

13. Writing a contiguous dataset with conversion Dataset raw data Metadata cache Dataset header …………. Datatype Dataspace …………. Attribute 1 Conversion buffer 1MB Attribute 2 ………… Application memory File Dataset header Dataset raw data HDF and HDF-EOS Workshop X, Landover, MD

14. Sub-setting of contiguous datasetSeries of adjacent rows Application data in memory N M One I/O operation M rows File Data is contiguous in a file HDF and HDF-EOS Workshop X, Landover, MD

15. Sub-setting of contiguous datasetAdjacent, partial rows Application data in memory N Several small I/O operation M N elements … File Data is scattered in a file in M contiguous blocks HDF and HDF-EOS Workshop X, Landover, MD

16. Sub-setting of contiguous datasetExtreme case: writing a column Application data in memory N Several small I/O operation M 1 element … Data is scattered in a file in M different locations HDF and HDF-EOS Workshop X, Landover, MD

17. Sub-setting of contiguous datasetData sieve buffer Application data in memory Data is gathered in a sieve buffer in memory 64K memcopy N M 1 element … File Data is scattered in a file in M contiguous blocks HDF and HDF-EOS Workshop X, Landover, MD

18. Performance tuning for contiguous dataset • Datatype conversion • Avoid for better performance • Use H5Pset_buffer function to customize conversion buffer size • Partial I/O • Write/read in big contiguous blocks (at least the size of a block on FS) • Use H5Pset_sieve_buf_size to improve performance for complex subsetting HDF and HDF-EOS Workshop X, Landover, MD

19. Possible tuning work • Datatype conversion • Use of multiple threads for datatype conversion • Partial I/O • OS vector I/O • Asynchronous I/O HDF and HDF-EOS Workshop X, Landover, MD

20. Writing chunked dataset Dimension sizes X x Y x Z Dataset is partitioned into fixed-size multi-dimensional chunks of sizes X/4 x Y/2 x Z HDF and HDF-EOS Workshop X, Landover, MD

21. Extending chunked dataset in any dimension • Data can be added in any dimensions • Compression is applied to each chunk • Datatype conversion is applied to each chunk HDF and HDF-EOS Workshop X, Landover, MD

22. Writing chunked dataset Chunked dataset Chunk cache A C C B Filter pipeline B A C File ………….. • Each chunk is written as a contiguous blob • Chunks may be scattered all over the file • Compression is performed when chunk is evicted from the chunk cache • Other filters when data goes through filter pipeline (e.g. encryption) HDF and HDF-EOS Workshop X, Landover, MD

23. Writing chunked dataset Metadata cache Dataset_1 header ………… ……… Chunk cache Default size is 1MB Dataset_N header Chunking B-tree nodes ………… • Size of chunk cache is set for file • Each chunked dataset has its own chunk cache • Chunk may be too big to fit into cache • Memory may grow if application keeps opening datasets Application memory HDF and HDF-EOS Workshop X, Landover, MD

24. Partial I/O for chunked dataset • Build list of chunks and loop through the list • For each chunk: • Bring chunk into memory • Map selection in memory to selection in file • Gather elements into conversion buffer and • perform conversion • Scatter elements back to the chunk • Apply filters (compression) when chunk is • flushed from chunk cache • For each element 3 memcopy performed 1 2 3 4 HDF and HDF-EOS Workshop X, Landover, MD

25. Partial I/O for chunked dataset Application buffer 3 Chunk memcopy Elements participated in I/O are gathered into corresponding chunk Application memory HDF and HDF-EOS Workshop X, Landover, MD

26. Partial I/O for chunked dataset Chunk cache Gather data Conversion buffer 3 Scatter data Application memory On eviction from cache chunk is compressed and is written to the file Chunk File HDF and HDF-EOS Workshop X, Landover, MD

27. Variable length datasets and I/O • Examples of variable-length data • String A[0] “the first string we want to write” ………………………………… A[N-1] “the N-th string we want to write” • Each element is a record of variable-length A[0] (1,1,0,0,0,5,6,7,8,9) length of the first record is 10 A[1] (0,0,110,2005) ……………………….. A[N] (1,2,3,4,5,6,7,8,9,10,11,12,….,M) length of the N+1 record is M HDF and HDF-EOS Workshop X, Landover, MD

28. Variable length datasets and I/O • Variable length description in HDF5 application typedef struct { size_t length; void *p; }hvl_t; • Base type can be any HDF5 type H5Tvlen_create(base_type) • ~ 20 bytesoverhead for each element • Raw data cannot be compressed HDF and HDF-EOS Workshop X, Landover, MD

29. Variable length datasets and I/O Raw data Global heap Global heap Application buffer Elements inapplication buffer point to global heaps where actual data is stored HDF and HDF-EOS Workshop X, Landover, MD

30. Writing chunked VL datasets Application memory Metadata cache B-tree nodes Chunk cache Dataset header ………… Global heap ……… Raw data Chunk cache Conversion buffer Filter pipeline VL chunked dataset with selected region File HDF and HDF-EOS Workshop X, Landover, MD

31. VL chunked dataset in a file Chunking B-tree File Dataset header Raw data Dataset chunks HDF and HDF-EOS Workshop X, Landover, MD

32. Variable length datasets and I/O • Hints • Avoid closing/opening a file while writing VL datasets • global heap information is lost • global heaps may have unused space • Avoid writing VL datasets interchangeably • data from different datasets will is written to the same heap • If maximum length of the record is known, use fixed-length records and compression HDF and HDF-EOS Workshop X, Landover, MD

33. Thank you! Questions ? HDF and HDF-EOS Workshop X, Landover, MD

34. Acknowledgement This report is based upon work supported in part by a Cooperative Agreement with NASA under NASA NNG05GC60A. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration. HDF and HDF-EOS Workshop X, Landover, MD