From 001756a410a9868da6fe3dc18434cb1064dad66a Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Mon, 24 Jul 2023 12:19:44 -0400 Subject: [PATCH] Resolve conflict --- .../juliacon_2023_presentation.html | 352 +++++++++++++----- .../juliacon_2023_presentation.md | 4 - .../juliacon_2023_presentation.pdf | Bin 337642 -> 343421 bytes 3 files changed, 267 insertions(+), 89 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.html b/docs/juliacon_2023/juliacon_2023_presentation.html index da53b9886..29a3dfe1f 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.html +++ b/docs/juliacon_2023/juliacon_2023_presentation.html @@ -11,7 +11,7 @@ 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::-webkit-calendar-picker-indicator{filter:invert(50%)}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1){color:var(--h1-color);font-size:1.6em}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1),div#\:\$p>svg>foreignObject>section :is(h2,marp-h2){border-bottom:none}div#\:\$p>svg>foreignObject>section :is(h2,marp-h2){font-size:1.3em}div#\:\$p>svg>foreignObject>section :is(h3,marp-h3){font-size:1.1em}div#\:\$p>svg>foreignObject>section :is(h4,marp-h4){font-size:1.05em}div#\:\$p>svg>foreignObject>section :is(h5,marp-h5){font-size:1em}div#\:\$p>svg>foreignObject>section :is(h6,marp-h6){font-size:.9em}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1) strong,div#\:\$p>svg>foreignObject>section :is(h2,marp-h2) strong,div#\:\$p>svg>foreignObject>section :is(h3,marp-h3) strong,div#\:\$p>svg>foreignObject>section :is(h4,marp-h4) strong,div#\:\$p>svg>foreignObject>section :is(h5,marp-h5) strong,div#\:\$p>svg>foreignObject>section :is(h6,marp-h6) 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:where(.hljs-subst){color:var(--color-prettylights-syntax-storage-modifier-import)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-section){color:var(--color-prettylights-syntax-markup-heading);font-weight:700}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-bullet){color:var(--color-prettylights-syntax-markup-list)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-emphasis){color:var(--color-prettylights-syntax-markup-italic);font-style:italic}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-strong){color:var(--color-prettylights-syntax-markup-bold);font-weight:700}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-addition){background-color:var(--color-prettylights-syntax-markup-inserted-bg);color:var(--color-prettylights-syntax-markup-inserted-text)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-deletion){background-color:var(--color-prettylights-syntax-markup-deleted-bg);color:var(--color-prettylights-syntax-markup-deleted-text)}div#\:\$p>svg>foreignObject>section footer,div#\:\$p>svg>foreignObject>section header{color:var(--header-footer-color);font-size:18px;left:30px;margin:0;position:absolute}div#\:\$p>svg>foreignObject>section header{top:21px}div#\:\$p>svg>foreignObject>section footer{bottom:21px}div#\:\$p>svg>foreignObject>section{--h1-color:#246;--header-footer-color:hsla(0,0%,40%,.75);--heading-strong-color:#48c;--paginate-color:#777;align-items:stretch;display:flex;flex-flow:column nowrap;font-size:29px;height:720px;justify-content:center;padding:78.5px;width:1280px}div#\:\$p>svg>foreignObject>section{--marpit-root-font-size:29px}div#\:\$p>svg>foreignObject>section:where(.invert){--h1-color:#cee7ff;--header-footer-color:hsla(0,0%,60%,.75);--heading-strong-color:#7bf;--paginate-color:#999}div#\:\$p>svg>foreignObject>section>:last-child,div#\:\$p>svg>foreignObject>section[data-footer]>:nth-last-child(2){margin-bottom:0}div#\:\$p>svg>foreignObject>section>:first-child,div#\:\$p>svg>foreignObject>section>header:first-child+*{margin-top:0}div#\:\$p>svg>foreignObject>section:after{bottom:21px;color:var(--paginate-color);font-size:24px;padding:0;position:absolute;right:30px}div#\:\$p>svg>foreignObject>section:after{--marpit-root-font-size:24px}div#\:\$p>svg>foreignObject>section[data-color] :is(h1,marp-h1),div#\:\$p>svg>foreignObject>section[data-color] :is(h2,marp-h2),div#\:\$p>svg>foreignObject>section[data-color] :is(h3,marp-h3),div#\:\$p>svg>foreignObject>section[data-color] :is(h4,marp-h4),div#\:\$p>svg>foreignObject>section[data-color] :is(h5,marp-h5),div#\:\$p>svg>foreignObject>section[data-color] 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HDF5.jl: Hierarchical Data Storage for Julia

-

Mark Kittisopikul, Simon Byrne, Mustafa Mohamad

+

Mark Kittisopikul (HHMI), Simon Byrne (Caltech), Mustafa Mohamad (UCalgary)

What is HDF5?

@@ -34,42 +34,23 @@

When not to use HDF5

  • You have tables of heterogeneous data. Consider using columnar layouts. Other formats are more optimized for tables.
    • -
    +

    Related formats

    HDF5 is used as a base for other formats

    • NetCDF - Network Common Data Form v4 (Unidata, UCAR)
    • MAT - MATLAB data files v7.3+
    • PyTables - Pandas
      • +
    • JLD/JLD2 - Julia Data Format

    HDF5 Specification

    The HDF5 specification is open and freely available.

    -

    +

    https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html

    -
    -

    HDF5 Specification: Superblock

    -

    HDF5 structures are variably sized and use Bob Jenkin's Lookup3 checksum for metadata integrity.

    -

    -

    https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html#Superblock

    -
    -
    -

    A HDF5 Hex Dump

    -
    00000000  89 48 44 46 0d 0a 1a 0a  03 08 08 00 00 00 00 00  |.HDF............|
-00000010  00 00 00 00 ff ff ff ff  ff ff ff ff 82 08 01 00  |................|
-00000020  00 00 00 00 30 00 00 00  00 00 00 00 92 3c c0 2c  |....0........<.,|
-00000030  4f 48 44 52 02 20 a3 5c  ae 64 a3 5c ae 64 a3 5c  |OHDR. .\.d.\.d.\|
-00000040  ae 64 a3 5c ae 64 78 02  12 00 00 00 00 ff ff ff  |.d.\.dx.........|
-00000050  ff ff ff ff ff ff ff ff  ff ff ff ff ff 0a 02 00  |................|
-00000060  01 00 00 06 14 00 00 01  00 09 7a 61 72 72 73 68  |..........zarrsh|
-00000070  61 72 64 c3 00 00 00 00  00 00 00 00 40 00 00 00  |ard.........@...|
-00000080  00 00 00 00 00 00 00 00  00 00 00 00 00 00 00 00  |................|
-
    -

    -
    -
    +

    What is HDF5.jl?

    HDF5.jl is a wrapper around the HDF5 C Library.

    It consists of

    @@ -79,40 +60,51 @@

    What is HDF5.jl?

  • A high level interface, a Julia API
    • -
    -

    HDF5.jl Early Contributors

    +
    +

    Related Julia Packages

    +
      +
    • HDF5_jll.jl, C Library from HDF Group (dependency of HDF5.jl)
      • +
    • MAT.jl, MATLAB files (depends on HDF5.jl)
      • +
    • JLD.jl, Julia Data Format (depends on HDF5.jl)
      • +
    • JLD2.jl, Julia Data Format 2: pure Julia implementation of a subset of HDF5
      • +
    • NetCDF.jl & NCDatasets.jl: wrappers for the NetCDF C library, which incorporates HDF5
      • +
    +
    +
    +

    HDF5.jl Early and Recent Contributors

    • There are many contributors
    • Konrad Hisen initiated Julia's support for HDF5
    • Tim Holy and Simon Kornblith were the initial primary authors
    • Tom Short, Blake Johnson, Isaih Norton, Elliot Saba, Steven Johnson, Mike Nolta, Jameson Nash
    • Justin Willmert improved many aspects C to Julia API interface
      • +
    • Other recent contributors: t-bltg, Henrik Ranocha, Nathan Zimmerberg, Joshua Lampert, Tamas Gal, David MacMahon, Juan Ignacio Polanco, Michale Schlottke-Lakemper, linwaytin, Dmitri Iouchtchenko, Lorenzo Van Munoz, Jared Wahlstrand, Julian Samaroo, machakann, James Hester, Ralph Kube, Kristoffer Carlsson
    -
    +

    HDF5.jl Current Developers

    • Mustafa Mohamad, Mark Kittisopikul, and Simon Byrne are the current maintainers
    • Mark Kittisopikul has been expanding API coverage, especially with chunking
    • Simon Byrne has been working on package organization, filter interface, virtual datasets, and parallelization
      • -
    • Other recent contributors: t-bltg, Henrik Ranocha, Nathan Zimmerberg, Joshua Lampert, Tamas Gal, David MacMahon, Juan Ignacio Polanco, Michale Schlottke-Lakemper, linwaytin, Dmitri Iouchtchenko, Lorenzo Van Munoz, Jared Wahlstrand, Julian Samaroo, machakann, James Hester, Ralph Kube, Kristoffer Carlsson
    -
    +

    What advantages does Julia bring to HDF5.jl?

    +
      -
    • HDF5.jl wraps the C library directly in Julia via @ccall. +
    • HDF5.jl dynamically create types to match the stored HDF5 types.
      • +
    • HDF5.jl can use Julia's reflection capabilities to create corresponding HDF5 types.
      • +
    • HDF5.jl is easily extensible using multiple dispatch.
      • +
    • HDF5.jl can create callbacks for C for efficient iteration.
      • +
    • HDF5.jl wraps the C library directly in Julia via @ccall.
      • -
    • HDF5.jl dynamically create types to match the stored HDF5 types.
      • -
    • HDF5.jl can use Julia's reflection capabilities to create corresponding HDF5 types.
      • -
    • HDF5.jl is easily extensible using multiple dispatch.
      • -
    • HDF5.jl can create callbacks for C for efficient iteration.
    -
    +

    Basic HDF5.jl Usage

    using HDF5
 
@@ -133,7 +125,7 @@ 
    Basic HDF5.jl Usage
    
 end
    -
    +

    Exploring a HDF5 file with HDF5.jl

    julia> h5f = h5open("mydata.h5")
 🗂 HDF5.File: (read-only) mydata.h5
@@ -149,7 +141,7 @@ 
    Exploring a HDF5 file with HDF5.jl
    -
    +

    Structs and HDF5 Types

    julia> struct Foo
            x::Int64
@@ -163,7 +155,7 @@ 
    Structs and HDF5 Types
    
    }
    -
    +

    Reading and writing structs

    julia> h5open("mystruct.h5", "w") do h5f
            h5f["Foo"] = [Foo(1, 3.0)]
@@ -184,6 +176,20 @@ 
    Reading and writing structs
    
  Foo(1, 3.0)
    +
    +

    Chunking and Built-in Gzip Compression Usage

    +

    In HDF5.jl version 0.16 we introduced a new general filter keyword allowing for the definition of filter pipelines.

    +
    using HDF5
+
+h5open("simple_chunked.h5", "w", libver_bounds=v"1.12") do h5f
+    h5ds = create_dataset(h5f, "gzipped_data", UInt8, (16,16),
+        chunk=(4,4),
+        filters=[HDF5.Filters.Deflate()],
+        alloc_time = :early
+    )
+end
+
    +

    Compression Filter Plugin Packages

    @@ -195,21 +201,9 @@

    Compression Filter Plugin PackagesH5Zbzip2.jl - CodecBzip2.jl
  • H5Zbitshuffle.jl
    • +

    Future: Let's figure out how to share these with JLD2.jl!

    -

    Chunking and Built-in Gzip Compression Usage

    -
    using HDF5
-
-h5open("simple_chunked.h5", "w", libver_bounds=v"1.12") do h5f
-    h5ds = create_dataset(h5f, "gzipped_data", UInt8, (16,16),
-        chunk=(4,4),
-        filters=[HDF5.Filters.Deflate()],
-        alloc_time = :early
-    )
-end
-
    -
    -

    Chunking and Filter Plugin Usage

    using HDF5, H5Zzstd
 
@@ -220,31 +214,231 @@ 
    Chunking and Filter Plugin Usage
    
     )
 end
    -

    Future: Loading CodecZstd.jl will trigger a package extension

    +

    TODO: Use a package extension loading mechanism when CodecZstd.jl is present.

    -
    +

    Using External Native Plugin Filters

    The HDF5 C library has a filter plugin mechanism. Plugins are shared libraries located in /usr/local/hdf5/lib/plugin or as specified by $HDF5_PLUGIN_DIR.

    -
    using HDF5.Filters
+
    using HDF5.Filters
 
-bitshuf = ExternalFilter(32008, Cuint[0, 0])
-bitshuf_comp = ExternalFilter(32008, Cuint[0, 2])
+bitshuf = ExternalFilter(32008, Cuint[0, 0])
+bitshuf_comp = ExternalFilter(32008, Cuint[0, 2])
 
-data_A = rand(0:31, 1024)
-data_B = rand(32:63, 1024)
+data_A = rand(0:31, 1024)
+data_B = rand(32:63, 1024)
 
 filename, _ = mktemp()
-h5open(filename, "w") do h5f
-    # Indexing style
-    h5f["ex_data_A", chunk=(32,), filters=bitshuf] = data_A
-    # Procedural style
-    d, dt = create_dataset(h5f, "ex_data_B", data_B, chunk=(32,), filters=[bitshuf_comp])
+h5open(filename, "w") do h5f
+    # Indexing style
+    h5f["ex_data_A", chunk=(32,), filters=bitshuf] = data_A
+    # Procedural style
+    d, dt = create_dataset(h5f, "ex_data_B", data_B, chunk=(32,), filters=[bitshuf_comp])
     write(d, data_B)
-end
+end
+
    
+
    +
    +

    New with HDF5 1.12.3 and 1.14.0: Efficient Chunk Based Iteration

    +

    Where are the compressed chunks and can we decompress them in parallel?

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    N ChunksH5Dchunk_iterH5Dget_chunk_infoRatio
    642e-4 s5e-4 s2.4
    2567e-4 s5e-3 s6
    10243e-3 s5e-2 s16
    40961e-2 s7e-1 s57
    163846e-2 s1e2 s208
    +
    +
    +

    The HDF5 C API does not allow for multithreaded concurrency

    +
      +
    • The HDF5 C library is not directly compatible with multithreading for parallel I/O. The preferred parallelization is via MPI.
      • +
    • There is a H5_HAVE_THREADSAFE compile time option that uses a recursive lock.
      • +
    • In HDF5.jl we have applied a ReentrantLock on all API calls. +
        +
      • It is now safe to use HDF5.jl with multithreading, but you may not see much of an improvement.
        • +
      +
      • +
    +
    +
    +

    Concurrency with Direct I/O

    +
      +
    • The HDF5 C library provides byte offsets for continguous and chunked datasets
      • +
    • Currently, HDF5.jl allows contiguous datasets to be memory mapped into arrays allowing for multithreaded reads.
      • +
    • With efficient chunk iteration, could we perform parallel decompression in HDF5.jl by reading compressed chunks directly?
      • +
    +
    +
    +

    Virtual datasets

    +
      +
    • +

      Maps multiple datasets into a single dataset

      +
        +
      • Can be same or different files
        • +
      • Supports patterns for sequentially numbered files/datasets
        • +
      +
      • +
    • +

      e.g. consider a dataset made up of 100×10 blocks, across 4 files

      +
        +
      • data00.h5, data01.h5, etc.
        • +
      +
      • +
    +
    space = dataspace((100,40))
+create_dataset(h5f, "dataset", datatype, space;
+    virtual=[HDF5.VirtualMapping(
+        HDF5.hyperslab(space, (1:100, HDF5.BlockRange(1:10; count = -1))), # block pattern
+        "./data0%b.h5",       # filenames (%b block pattern)
+        "data",               # path to source dataset in file
+        dataspace((100,10))   # view into source dataset
+    )]
+)
+
    +
    +
    +

    Parallelization via MPI

    +
      +
    • Message Passing Interface (MPI) is an interface for single-program, multiple-data (SPMD) parallelism. +
        +
      • Launch multiple processes running the same program
         mpiexec -n <nprocs> program ...
+
        +
        • +
      • Programs determine what they should do based on their identifier (rank).
        • +
      • Each process determines what communication operations it should do (messages)
        • +
      • Multiple implementations (Open MPI, MPICH, vendor-specific)
        • +
      • Widely used in HPC for large-scale distributed parallelism.
        • +
      +
      • +
    • MPI.jl provides Julia bindings
      • +
    +
    +
    +

    Using MPI + HDF5

    +

    Load and initialize MPI

    +
    using MPI, HDF5
+MPI.Init()
+
    +

    Pass MPI communicator to h5open, e.g.

    +
    h5 = h5open("data.h5", "w", MPI.COMM_WORLD)
+
    +
      +
    • Needs to be collective (all processes at the same time), with the same arguments.
      • +
    • File needs to be on accessible from all processes (e.g. on a shared file system if distributed).
      • +
    +
    +
    +

    Usage otherwise same as normal:

    +
      +
    • metadata operatrions(create_dataset, writing attributes) should be done collectively, with the same arguments.
      • +
    • reading/writing data can be independently per-process. +
        +
      • try to align chunks with processes
        • +
      • if collective, use dxpl_mpio=:collective option with create_dataset/open_dataset
        • +
      +
      • +
    • some limitations (e.g no datasets with variable-length strings).
      • +
    +
    +
    +

    Configuring HDF5 (in upcoming 0.17 release)

    +
      +
    • +

      May want to use specific HDF5 library

      +
        +
      • interoperability with other languages (e.g. h5py)
        • +
      • linked against custom MPI binary
        • +
      • specific hardware features (burst buffers)
        • +
      +
      • +
    • +

      Preferences.jl to specify custom HDF5 binary

      +
      • +
    +
    using Preferences, HDF5
+set_preferences!(HDF5,
+        "libhdf5" => "/path/to/your/libhdf5.so",
+        "libhdf5_hl" => "/path/to/your/libhdf5_hl.so",
+        force = true)
    -
    +
    +

    Summary

    +
      +
    • HDF5 is a format, C library, and data model for storing hierarchical information.
      • +
    • HDF5.jl is a wrapper providing high and low level access to the HDF5 library.
      • +
    • HDF5.jl now allows for multithreaded capability through locks and may expand capabilities beyond that of HDF5 C library
      • +
    • HDF5.jl works with MPI.jl to allow for distributed multiprocessing
      • +
    +
    +
    +

    Questions?

    +
    +
    +

    Extra Slides and Advanced Topics

    +
      +
    • HDF5 Specification: Superblock and Hex Dump
      • +
    • Iteration
      • +
    +
    +
    +

    HDF5 Specification: Superblock

    +

    HDF5 structures are variably sized and use Bob Jenkin's Lookup3 checksum for metadata integrity.

    +

    +

    https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html#Superblock

    +
    +
    +

    A HDF5 Hex Dump

    +
    00000000  89 48 44 46 0d 0a 1a 0a  03 08 08 00 00 00 00 00  |.HDF............|
+00000010  00 00 00 00 ff ff ff ff  ff ff ff ff 82 08 01 00  |................|
+00000020  00 00 00 00 30 00 00 00  00 00 00 00 92 3c c0 2c  |....0........<.,|
+00000030  4f 48 44 52 02 20 a3 5c  ae 64 a3 5c ae 64 a3 5c  |OHDR. .\.d.\.d.\|
+00000040  ae 64 a3 5c ae 64 78 02  12 00 00 00 00 ff ff ff  |.d.\.dx.........|
+00000050  ff ff ff ff ff ff ff ff  ff ff ff ff ff 0a 02 00  |................|
+00000060  01 00 00 06 14 00 00 01  00 09 7a 61 72 72 73 68  |..........zarrsh|
+00000070  61 72 64 c3 00 00 00 00  00 00 00 00 40 00 00 00  |ard.........@...|
+00000080  00 00 00 00 00 00 00 00  00 00 00 00 00 00 00 00  |................|
+
    +

    +
    +

    Iteration

    +

    For accessing data has two kinds of interfaces for accessing enumerated data:

    1. h5a_get_name_by_idx(loc_id, obj_name, index_type, order, idx, name, size, lapl_id)
      2. @@ -252,28 +446,16 @@

      Iteration

    The _by_idx calls are easy to use via a simple for loop but are very inefficient for iterating over many items.

    The _iterate calls require a C callback, op, and can be challenging to use but are efficient.

    +

    Based on h5a_iterate we have created a new attrs API replacing the former attributes API.

    -
    -

    Multithreading

    -
      -
    • The HDF5 C library is not directly compatible with multithreading for parallel I/O. The preferred parallelization is via MPI.
      • -
    • There is a H5_HAVE_THREADSAFE compile time option that uses a recursive lock.
      • -
    • In HDF5.jl we have applied a ReentrantLock on all API calls.
      • -
    -
    -
    -

    Parallelization via Message Passing Interface (MPI)

    -
    -
    -

    Other Related Julia Packages

    +
    +

    Concurrency with Direct I/O

      -
    • HDF5_jll.jl, C Library from HDF Group
      • -
    • MAT.jl, MATLAB files
      • -
    • JLD.jl, Julia Data Format
      • -
    • JLD2.jl, Julia Data Format 2, Pure Julia implementation of a subset of HDF5
      • +
    • The HDF5 C library provides byte offsets for continguous and chunked datasets
      • +
    • Currently, HDF5.jl allows contiguous datasets to be memory mapped into arrays allowing for multithreaded reads.
      • +
    • With efficient chunk iteration, could we perform parallel decompression in HDF5.jl by reading compressed chunks directly?
    -

    During the talk explain what these do and when/why you would want to use them

    Simon Byrne, please elaborate

    reorder

    During the talk explain what these do and when/why you would want to use them

    Move to the end

    move to end

    \ No newline at end of file diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index b7b20bc8c..25668c6e7 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -283,9 +283,6 @@ Where are the compressed chunks and can we decompress them in parallel? --- -<<<<<<< HEAD -# Parallelization via Message Passing Interface (MPI) -======= # Concurrency with Direct I/O * The HDF5 C library provides byte offsets for continguous and chunked datasets @@ -317,7 +314,6 @@ create_dataset(h5f, "dataset", datatype, space; --- # Parallelization via MPI ->>>>>>> e0d07a75b1dec46114997a076069748edab6d75d - Message Passing Interface (MPI) is an interface for single-program, multiple-data (SPMD) parallelism. - Launch multiple processes running the same program diff --git a/docs/juliacon_2023/juliacon_2023_presentation.pdf b/docs/juliacon_2023/juliacon_2023_presentation.pdf index 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