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z4qa@*d#L=*f7Dw#4C87!GKNUs{i!V9?h)hh;*aIx5r-e#;S;G%Aqja^1;EGr0$i{p z;_xDuX;?)_-D=BF)xyqRWd>cXc2b|PH6t$1iz|$s?HL@RIAf{+YLcld$(}nqbo_7k zid2An8>(^74nU%irlqtuv}Mby%H;HtBhW`jTmpW;BaWfj;?I*D2Y}bKcSt3Je4kkR zN(BG9to8u^g;NEGn3_tAQ3}2Mh_U__^7) z{;TcoU^M6QZ@b^!ZV@zE=1&`;)9&bBN|VeKCo%k=Fzw-q)}Uni)XU+y(GmecK}J=& JQqnB=zX1yq->(1w literal 0 HcmV?d00001 diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md new file mode 100644 index 000000000..36ef1abfa --- /dev/null +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -0,0 +1,259 @@ +--- +marp: true +--- + +# HDF5.jl: Hierarchical Data Storage for Julia + +Mark Kittisopikul, Simon Byrne, Mustafa Mohamad + +--- + +# To Simon and Mustafa + +I'm using https://marp.app/ +If you use VS Code, I recommend getting the Marp VS Code Extension +https://marketplace.visualstudio.com/items?itemName=marp-team.marp-vscode + +From VS Code you can do Ctrl-Shift-V to view a PDF preview of the slide deck +Also see "Marp: Export slide deck" to save a PDF + +--- + +# What is HDF5? + +HDF5 stands for Hierarchial Data Format version 5 and is maintained by The HDF Group. + +* HDF5 is a file format with an open specification +* HDF5 is a C Library and API +* HDF5 is a data model + +--- + +# When to use HDF5 + +* Store numeric array and attributes in nested groups +* Use it when you want to compactly store lot of binary data +* Do not use it when you have a lot of variable length strings +* Consider other formats when using tables + +--- + +# What is HDF5? - Related Formats + +* NetCDF - Network Common Data Form (Unidata, UCAR) +* MAT - MATLAB data files v7.3+ +* PyTables - Pandas + +--- + +# HDF5 Specification + +![](specification_toc.png) + +https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html + +--- + +# HDF5 Specification: Superblock + +![](superblock.png) + +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 |................| +``` + +![](format_signature.png) + +--- + +# What is HDF5.jl? + +HDF5.jl is a wrapper around the HDF5 C Library. + +It consists of +* A low level interface, a direct mapping to the C API +* A mid level interface, lightweight helpers +* A high level interface, a Julia API + +--- + +# HDF5.jl Early 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 + +--- + +# HDF5.jl Current Developers + +* Mustafa Mohamad is the current lead maintainer +* Mark Kittisopikul has been expanding API coverage, especially with chunking +* Simon Byrne has been working on package organization, filter 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 + +--- + +# How does HDF5.jl compare to h5py? + +* h5py is a Python library that wraps the HDF5 C library. +* h5py uses Cython to build low-level wrappers +* HDF5.jl wraps the C library directly via `@ccall` +* HDF5.jl takes advantages of types and multiple dispatch + +--- + +# Basic HDF5.jl Usage + +```julia +using HDF5 + +# Write a HDF5 file +h5open("mydata.h5", "w") do h5f + h5f["group_A/group_B/array_C"] = rand(1024,1024) + attributes(h5f["group_A"])["access_date"] = "2023_07_21" +end + +# Read a HDF5 file +C = h5open("mydata.h5") do h5f + println(attrs(h5f["group_A"])["access_date"]) + h5f["group_A/group_B/array_C"][:,:] +end +``` + +--- + +# Exploring a HDF5 file with HDF5.jl + +```julia +julia> h5f = h5open("mydata.h5") +🗂️ HDF5.File: (read-only) mydata.h5 +└─ 📂 group_A + ├─ 🏷️ access_date + └─ 📂 group_B + └─ 🔢 array_C + +julia> C = h5f["group_A"]["group_B"]["array_C"][1:16,1:16] +16×16 Matrix{Float64}: +... + +julia> close(h5f) +``` + +--- + +# Structs and HDF5 Types + +```julia +julia> struct Foo + x::Int64 + y::Float64 + end + +julia> HDF5.datatype(Foo) +HDF5.Datatype: H5T_COMPOUND { + H5T_STD_I64LE "x" : 0; + H5T_IEEE_F64LE "y" : 8; + } +``` + +--- + +# Reading and writing structs + +```julia +julia> h5open("mystruct.h5", "w") do h5f + h5f["Foo"] = [Foo(1, 3.0)] + end +1-element Vector{Foo}: + Foo(1, 3.0) + +julia> h5open("mystruct.h5", "r") do h5f + h5f["Foo"][] + end +1-element Vector{NamedTuple{(:x, :y), Tuple{Int64, Float64}}}: + (x = 1, y = 3.0) + +julia> h5open("mystruct.h5", "r") do h5f + read(h5f["Foo"], Foo) + end +1-element Vector{Foo}: + Foo(1, 3.0) +``` + +--- + +# Compression Filter Plugin Packages + +Glue code written in Julia. + +* H5Zblosc.jl - Blosc.jl (Thank you, Steven G. Johnson) +* H5Zzstd.jl - CodecZstd.jl +* H5Zlz4.jl - CodecLZ4.jl +* H5Zbzip2.jl - CodecBzip2.jl +* H5Zbitshuffle.jl + +--- + +# Chunking and Built-in Gzip Compression Usage + +```julia +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 + +```julia +using HDF5, H5Zzstd + +h5open("zstd_chunked.h5", "w", libver_bounds=v"1.12") do h5f + h5ds = create_dataset(h5f, "zstd_data", UInt8, (16,16), + chunk=(4,4), + filters=[ZstdFilter(3)] + ) +end +``` + +Future: Loading CodecZstd.jl will trigger a package extension + +--- + +# Other Related Julia Packages + +* 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 + +--- + +# Parallelization via Message Passing Interface (MPI) + + + 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zZs1-0UY2v67ey44Y}otPtz5qFZobu>9x!2|?}WvefFjZj3)b4tGn85vZ-(oi8n4?v zb-t>-j4w`2@U=DTEiSv~sj<$_9W?$P)p2TaRzj)``MVV^_#IoGRoqx)RPmV>+UW6v zU4C(~?BZF$9wCcErsvFO6g4X7E5c$}Ps-JWOp4Erb}1PkG-tJk1Foc4ipH857%D4% z1S(8UJP*&Lw@0JkmfVV|{zE?#^Q zp;t8D6R{GqH%oIDk_`v0zSp zv(&-0M}+Q9;gcTuk1U-8E}n~kl7U6XU(Kf5w*?UUfUscio2%<{p>i9^@a0&G`n`0r zf3Px@DFPA^=gLOAp}u&pgneT)il_dOxn_n}^=X4V)7x4}5^13?jpv2*bYsge2-=7m z8J5S}uxSct3*oW*0H-pYk3&4;0bj zKmCGtPd>#|$UvG$$iuZ)pk}$k4vi2|E!+I7-;_yXv20GMh3PN;q7acXzPmJTe4Z*O zPXM?2cigY=CC4PZ|6{Yb2a}a9mI8ML_nI0>6{R%D(>2ZF31^b@aD|^iVWc9I5 zzO3MiIcN8fK$~%L&d|CxA=aqCOV;2^@b@XsVVP{(3O&&@ep!6C$I9JkfuSod{wx&i z{veooGVXKZPkP4BKU}++Bk)GvB>e06?E9-eEq~_*#m}Q|&5MYJaQ+Fk7cN*m`d>TDm_IX!w!NJ5uhfB- uJe;ZcSHMtLcX}pL#_{j-{|~27S$Hx!s}J^{{v)|Tr>$Y2Ua$7(`F{c1)c8pN literal 0 HcmV?d00001 From d5b03ac563f4ce45267c1486b2a1e3fdadec1f08 Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Fri, 21 Jul 2023 15:23:08 -0700 Subject: [PATCH 02/18] small update --- docs/juliacon_2023/juliacon_2023_presentation.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 36ef1abfa..a587887d9 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -33,14 +33,17 @@ HDF5 stands for Hierarchial Data Format version 5 and is maintained by The HDF G * Store numeric array and attributes in nested groups * Use it when you want to compactly store lot of binary data -* Do not use it when you have a lot of variable length strings -* Consider other formats when using tables + +## When not to use HDF5 +* Arrays of variable-length strings +* Tables of heterogeneous data --- -# What is HDF5? - Related Formats +# Related formats -* NetCDF - Network Common Data Form (Unidata, UCAR) +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 @@ -104,7 +107,7 @@ It consists of * Mustafa Mohamad is the current lead maintainer * Mark Kittisopikul has been expanding API coverage, especially with chunking -* Simon Byrne has been working on package organization, filter interface +* 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 --- @@ -126,7 +129,7 @@ using HDF5 # Write a HDF5 file h5open("mydata.h5", "w") do h5f h5f["group_A/group_B/array_C"] = rand(1024,1024) - attributes(h5f["group_A"])["access_date"] = "2023_07_21" + attrs(h5f["group_A"])["access_date"] = "2023_07_21" end # Read a HDF5 file From 30f4310ccc7fbcd4c8d2d852927e1ac8780136b5 Mon Sep 17 00:00:00 2001 From: Mustafa Mohamad Date: Fri, 21 Jul 2023 18:00:02 -0600 Subject: [PATCH 03/18] Minor updates --- .../juliacon_2023_presentation.md | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index a587887d9..c7c046085 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -8,17 +8,6 @@ Mark Kittisopikul, Simon Byrne, Mustafa Mohamad --- -# To Simon and Mustafa - -I'm using https://marp.app/ -If you use VS Code, I recommend getting the Marp VS Code Extension -https://marketplace.visualstudio.com/items?itemName=marp-team.marp-vscode - -From VS Code you can do Ctrl-Shift-V to view a PDF preview of the slide deck -Also see "Marp: Export slide deck" to save a PDF - ---- - # What is HDF5? HDF5 stands for Hierarchial Data Format version 5 and is maintained by The HDF Group. @@ -100,12 +89,13 @@ It consists of * 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 --- # HDF5.jl Current Developers -* Mustafa Mohamad is the current lead maintainer +* 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 @@ -203,6 +193,8 @@ julia> h5open("mystruct.h5", "r") do h5f # Compression Filter Plugin Packages + + Glue code written in Julia. * H5Zblosc.jl - Blosc.jl (Thank you, Steven G. Johnson) @@ -215,6 +207,7 @@ Glue code written in Julia. # Chunking and Built-in Gzip Compression Usage + ```julia using HDF5 @@ -259,4 +252,4 @@ Future: Loading CodecZstd.jl will trigger a package extension ---- \ No newline at end of file +--- From 9ada3ff9912ec69b33994da246161d0dca9f5763 Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Sat, 22 Jul 2023 20:16:25 -0400 Subject: [PATCH 04/18] Update presentation --- .../juliacon_2023_presentation.html | 279 ++++++++++++++++++ .../juliacon_2023_presentation.md | 91 ++++-- .../juliacon_2023_presentation.pdf | Bin 268060 -> 285424 bytes 3 files changed, 352 insertions(+), 18 deletions(-) create mode 100644 docs/juliacon_2023/juliacon_2023_presentation.html diff --git a/docs/juliacon_2023/juliacon_2023_presentation.html b/docs/juliacon_2023/juliacon_2023_presentation.html new file mode 100644 index 000000000..da53b9886 --- /dev/null +++ b/docs/juliacon_2023/juliacon_2023_presentation.html @@ -0,0 +1,279 @@ +

+

HDF5.jl: Hierarchical Data Storage for Julia

+

Mark Kittisopikul, Simon Byrne, Mustafa Mohamad

+
+
+

What is HDF5?

+

HDF5 stands for Hierarchial Data Format version 5 and is maintained by The HDF Group, formerly part of the National Center for Supercomputing Appplications (NCSA).

+
    +
  • HDF5 is a file format with an open specification.
  • +
  • HDF5 is a C Library and API.
  • +
  • HDF5 is a data model.
  • +
+
+
+

When to use HDF5

+
    +
  • Store numeric array and attributes in nested groups.
  • +
  • Use it when you want to compactly store binary data.
  • +
+

When not to use HDF5

+
    +
  • You have arrays of variable-length strings. Used fixed lengths strings instead.
  • +
  • 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
  • +
+
+
+

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

+
    +
  • A low level interface, a direct mapping to the C API
  • +
  • A mid level interface, lightweight helpers
  • +
  • A high level interface, a Julia API
  • +
+
+
+

HDF5.jl Early 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
  • +
+
+
+

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.
  • +
+
+
+

Basic HDF5.jl Usage

+
using HDF5
+
+# Write a HDF5 file
+h5open("mydata.h5", "w") do h5f
+    # Store an array
+    h5f["group_A/group_B/array_C"] = rand(1024,1024)
+    # Store an attribute
+    attrs(h5f["group_A"])["access_date"] = "2023_07_21"
+end
+
+# Read a HDF5 file
+C = h5open("mydata.h5") do h5f
+    # Access an attribute
+    println(attrs(h5f["group_A"])["access_date"])
+    # Load an array and return it as C
+    h5f["group_A/group_B/array_C"][:,:]
+end
+
+
+
+

Exploring a HDF5 file with HDF5.jl

+
julia> h5f = h5open("mydata.h5")
+🗂 HDF5.File: (read-only) mydata.h5
+└─ 📂 group_A
+   ├─ 🏷 access_date
+   └─ 📂 group_B
+      └─ 🔢 array_C
+
+julia> C  = h5f["group_A"]["group_B"]["array_C"][1:16,1:16]
+16×16 Matrix{Float64}:
+...
+
+julia> close(h5f)
+
+
+
+

Structs and HDF5 Types

+
julia> struct Foo
+           x::Int64
+           y::Float64
+       end
+
+julia> HDF5.datatype(Foo)
+HDF5.Datatype: H5T_COMPOUND {
+      H5T_STD_I64LE "x" : 0;
+      H5T_IEEE_F64LE "y" : 8;
+   }
+
+
+
+

Reading and writing structs

+
julia> h5open("mystruct.h5", "w") do h5f
+           h5f["Foo"] = [Foo(1, 3.0)]
+       end
+1-element Vector{Foo}:
+ Foo(1, 3.0)
+
+julia> h5open("mystruct.h5", "r") do h5f
+           h5f["Foo"][]
+       end
+1-element Vector{NamedTuple{(:x, :y), Tuple{Int64, Float64}}}:
+ (x = 1, y = 3.0)
+
+julia> h5open("mystruct.h5", "r") do h5f
+           read(h5f["Foo"], Foo)
+       end
+1-element Vector{Foo}:
+ Foo(1, 3.0)
+
+
+
+

Compression Filter Plugin Packages

+ +

Glue code written in Julia.

+
    +
  • H5Zblosc.jl - Blosc.jl (Thank you, Steven G. Johnson)
  • +
  • H5Zzstd.jl - CodecZstd.jl
  • +
  • H5Zlz4.jl - CodecLZ4.jl
  • +
  • H5Zbzip2.jl - CodecBzip2.jl
  • +
  • H5Zbitshuffle.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
+
+h5open("zstd_chunked.h5", "w", libver_bounds=v"1.12") do h5f
+    h5ds = create_dataset(h5f, "zstd_data", UInt8, (16,16),
+        chunk=(4,4),
+        filters=[ZstdFilter(3)]
+    )
+end
+
+

Future: Loading CodecZstd.jl will trigger a package extension

+
+
+

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
+
+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)
+
+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])
+    write(d, data_B)
+end
+
+
+
+

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. +
  3. h5a_iterate(obj_id::hid_t, idx_type::Cint, order::Cint, n::Ptr{hsize_t}, op::Ptr{Cvoid}, op_data::Any), op is function pointer
  4. +
+

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.

+
+
+

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

+
    +
  • 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
  • +
+
+
+

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

Simon Byrne, please elaborate

\ 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 c7c046085..05a293ce9 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -10,22 +10,22 @@ Mark Kittisopikul, Simon Byrne, Mustafa Mohamad # What is HDF5? -HDF5 stands for Hierarchial Data Format version 5 and is maintained by The HDF Group. +HDF5 stands for Hierarchial Data Format version 5 and is maintained by The HDF Group, formerly part of the National Center for Supercomputing Appplications (NCSA). -* HDF5 is a file format with an open specification -* HDF5 is a C Library and API -* HDF5 is a data model +* HDF5 is a file format with an open specification. +* HDF5 is a C Library and API. +* HDF5 is a data model. --- # When to use HDF5 -* Store numeric array and attributes in nested groups -* Use it when you want to compactly store lot of binary data +* Store numeric array and attributes in nested groups. +* Use it when you want to compactly store binary data. ## When not to use HDF5 -* Arrays of variable-length strings -* Tables of heterogeneous data +* You have arrays of variable-length strings. Used fixed lengths strings instead. +* You have tables of heterogeneous data. Consider using columnar layouts. Other formats are more optimized for tables. --- @@ -40,6 +40,8 @@ HDF5 is used as a base for other formats # HDF5 Specification +The HDF5 specification is open and freely available. + ![](specification_toc.png) https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html @@ -48,6 +50,8 @@ 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. + ![](superblock.png) https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html#Superblock @@ -102,12 +106,14 @@ It consists of --- -# How does HDF5.jl compare to h5py? +# What advantages does Julia bring to HDF5.jl? -* h5py is a Python library that wraps the HDF5 C library. -* h5py uses Cython to build low-level wrappers -* HDF5.jl wraps the C library directly via `@ccall` -* HDF5.jl takes advantages of types and multiple dispatch +* HDF5.jl wraps the C library directly in Julia via `@ccall`. + * This is partially automated via Clang.jl and https://github.com/mkitti/LibHDF5.jl . +* 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. --- @@ -118,13 +124,17 @@ using HDF5 # Write a HDF5 file h5open("mydata.h5", "w") do h5f + # Store an array h5f["group_A/group_B/array_C"] = rand(1024,1024) + # Store an attribute attrs(h5f["group_A"])["access_date"] = "2023_07_21" end # Read a HDF5 file C = h5open("mydata.h5") do h5f + # Access an attribute println(attrs(h5f["group_A"])["access_date"]) + # Load an array and return it as C h5f["group_A/group_B/array_C"][:,:] end ``` @@ -239,12 +249,47 @@ Future: Loading CodecZstd.jl will trigger a package extension --- -# Other Related Julia Packages +# Using External Native Plugin Filters -* 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 +The HDF5 C library has a filter plugin mechanism. Plugins are shared libraries located in `/usr/local/hdf5/lib/plugin` or as specified by `$HDF5_PLUGIN_DIR`. + +``` +using HDF5.Filters + +bitshuf = ExternalFilter(32008, Cuint[0, 0]) +bitshuf_comp = ExternalFilter(32008, Cuint[0, 2]) + +data_A = rand(0:31, 1024) +data_B = rand(32:63, 1024) + +filename, _ = mktemp() +h5open(filename, "w") do h5f + # Indexing style + h5f["ex_data_A", chunk=(32,), filters=bitshuf] = data_A + # Procedural style + d, dt = create_dataset(h5f, "ex_data_B", data_B, chunk=(32,), filters=[bitshuf_comp]) + write(d, data_B) +end +``` +--- + +# Iteration + +For accessing data has two kinds of interfaces for accessing enumerated data: +1. `h5a_get_name_by_idx(loc_id, obj_name, index_type, order, idx, name, size, lapl_id)` +2. `h5a_iterate(obj_id::hid_t, idx_type::Cint, order::Cint, n::Ptr{hsize_t}, op::Ptr{Cvoid}, op_data::Any)`, `op` is function pointer + +The `_by_idx` calls are easy to use via a simple `for` loop but are very inefficient for iterating over many items. + +The `_iterate` calls require a C callback, `op`, and can be challenging to use but are efficient. + +--- + +# Multithreading + +* The HDF5 C library is not directly compatible with multithreading for parallel I/O. The preferred parallelization is via MPI. +* There is a `H5_HAVE_THREADSAFE` compile time option that uses a recursive lock. +* In HDF5.jl we have applied a `ReentrantLock` on all API calls. --- @@ -253,3 +298,13 @@ Future: Loading CodecZstd.jl will trigger a package extension --- + +# Other Related Julia Packages + +* HDF5_jll.jl, C Library from HDF Group +* MAT.jl, MATLAB files +* JLD.jl, Julia Data Format +* JLD2.jl, Julia Data Format 2, Pure Julia implementation of a subset of HDF5 + +--- + diff --git a/docs/juliacon_2023/juliacon_2023_presentation.pdf b/docs/juliacon_2023/juliacon_2023_presentation.pdf index e97ac0ad42648d8a202ad5515afeb06507d716d8..cec64259ff14eb2df10a4cc2735cca503def4103 100644 GIT binary patch delta 99653 zcmZ6y18}B6(=MFk$;P&A+uYc;ZJW;%+sP)`*tTukwl>Me+6~Ws?|aT)_5D@Tb5GTD z%{BL^dwROFYf)}nP!sup9B@KHaBl9d7A6jG-q|ghTee#wC;@9)zugKhJFdUU=Qrfc zM~LVle1KYEF;uNzzvh!Db?oTT@nsoN?ulk*r!zB~qKBVu;f>ec*55UPZm&AtcAg@^ 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z!gqchlM$7nX9u%Sr^{!p>KAwEm0@RtRmB%MN@IJ(ErbJ|{;3l=LfQ8>H|jY6@6~#2 zQHQ6?;i#qhMUMJI;S(8wdr?ePk%JIH=*w%#X0m_HY{G&3)CHlS@bdD3`H*R8rIe-r E4+Z9U1ONa4 From 74451031f790712f5a19aa80c6537043075673b4 Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Sun, 23 Jul 2023 14:20:26 -0400 Subject: [PATCH 05/18] Move images into images folder, adjust sizes --- .../{ => images}/format_signature.png | Bin .../{ => images}/specification_toc.png | Bin .../juliacon_2023/{ => images}/superblock.png | Bin .../juliacon_2023_presentation.md | 47 ++++++++++-------- 4 files changed, 27 insertions(+), 20 deletions(-) rename docs/juliacon_2023/{ => images}/format_signature.png (100%) rename docs/juliacon_2023/{ => images}/specification_toc.png (100%) rename docs/juliacon_2023/{ => images}/superblock.png (100%) diff --git a/docs/juliacon_2023/format_signature.png b/docs/juliacon_2023/images/format_signature.png similarity index 100% rename from docs/juliacon_2023/format_signature.png rename to docs/juliacon_2023/images/format_signature.png diff --git a/docs/juliacon_2023/specification_toc.png b/docs/juliacon_2023/images/specification_toc.png similarity index 100% rename from docs/juliacon_2023/specification_toc.png rename to docs/juliacon_2023/images/specification_toc.png diff --git a/docs/juliacon_2023/superblock.png b/docs/juliacon_2023/images/superblock.png similarity index 100% rename from docs/juliacon_2023/superblock.png rename to docs/juliacon_2023/images/superblock.png diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 05a293ce9..4166f8814 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -42,7 +42,7 @@ HDF5 is used as a base for other formats The HDF5 specification is open and freely available. -![](specification_toc.png) +![width:600px](images/specification_toc.png) https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html @@ -52,7 +52,7 @@ https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html HDF5 structures are variably sized and use Bob Jenkin's Lookup3 checksum for metadata integrity. -![](superblock.png) +![width:800px](images/superblock.png) https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html#Superblock @@ -72,7 +72,7 @@ https://docs.hdfgroup.org/hdf5/v1_14/_f_m_t3.html#Superblock 00000080 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 |................| ``` -![](format_signature.png) +![](images/format_signature.png) --- @@ -201,23 +201,9 @@ julia> h5open("mystruct.h5", "r") do h5f --- -# Compression Filter Plugin Packages - - - -Glue code written in Julia. - -* H5Zblosc.jl - Blosc.jl (Thank you, Steven G. Johnson) -* H5Zzstd.jl - CodecZstd.jl -* H5Zlz4.jl - CodecLZ4.jl -* H5Zbzip2.jl - CodecBzip2.jl -* H5Zbitshuffle.jl - ---- - # 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. ```julia using HDF5 @@ -232,6 +218,22 @@ end --- +# Compression Filter Plugin Packages + + + +Glue code written in Julia. + +* H5Zblosc.jl - Blosc.jl (Thank you, Steven G. Johnson) +* H5Zzstd.jl - CodecZstd.jl +* H5Zlz4.jl - CodecLZ4.jl +* H5Zbzip2.jl - CodecBzip2.jl +* H5Zbitshuffle.jl + +Future: Let's figure out how to share these with JLD2.jl! + +--- + # Chunking and Filter Plugin Usage ```julia @@ -245,7 +247,7 @@ h5open("zstd_chunked.h5", "w", libver_bounds=v"1.12") do h5f end ``` -Future: Loading CodecZstd.jl will trigger a package extension +TODO: Use a package extension loading mechanism when CodecZstd.jl is present. --- @@ -283,6 +285,12 @@ The `_by_idx` calls are easy to use via a simple `for` loop but are very ineffic The `_iterate` calls require a C callback, `op`, and can be challenging to use but are efficient. +--- + +# New with HDF5 1.12.3 and 1.14.0: Efficient Chunk Based Iteration + + + --- # Multithreading @@ -307,4 +315,3 @@ The `_iterate` calls require a C callback, `op`, and can be challenging to use b * JLD2.jl, Julia Data Format 2, Pure Julia implementation of a subset of HDF5 --- - From 7e7bc51aef65b4229b3f4532092425a810b96dac Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Mon, 24 Jul 2023 00:29:32 -0400 Subject: [PATCH 06/18] add MPI pieces --- .../juliacon_2023_presentation.md | 70 ++++++++++++++++++- 1 file changed, 69 insertions(+), 1 deletion(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 4166f8814..02f787abd 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -298,12 +298,80 @@ The `_iterate` calls require a C callback, `op`, and can be challenging to use b * 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. --- # Parallelization via Message Passing Interface (MPI) - +- Message Passing Interface (MPI) is an interface for single-program, multiple-data (SPMD) parallelism. + - Launch multiple processes running the same program + ```sh + mpiexec -n 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 + +---- + +## Configuring HDF5 with MPI (in upcoming 0.17 release) + +- Now works with default MPI & HDF5 JLLs +- On HPC clusters, will typically want to use the system-provided MPI library + - Integrate with resource manager, make use of specialized network hardware, GPU-aware interfaces + +### Option 1: use MPItrampoline +Requires building a wrapper library around your MPI library. + ```julia + MPIPreferences.use_jll_binary("MPItrampoline_jll") + ``` + - HDF5.jl should work directly. + +---- +### Option 2: use system binary directly +Requires system-provided MPI + HDF5 libraries. + +```julia +using MPIPreferences +MPIPreferences.use_system_binary() +``` +Need to set corresponding preferences for HDF5 +```julia +using Preferences, HDF5 +set_preferences!(HDF5, + "libhdf5" => "/path/to/your/libhdf5.so", + "libhdf5_hl" => "/path/to/your/libhdf5_hl.so", + force = true) +``` + +--- +## Using MPI + HDF5 + +Load and initialize MPI +```julia +using MPI, HDF5 +MPI.Init() +``` + +Pass MPI communicator to `h5open`, e.g. +```julia +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). + --- From 7cdbf3d32b85ae36c5fef780be735bf74a22eeb4 Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Mon, 24 Jul 2023 11:00:51 -0400 Subject: [PATCH 07/18] Add summary, chunk details --- .../juliacon_2023_presentation.md | 36 ++++++++++++++++--- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 4166f8814..ed086fb90 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -255,7 +255,7 @@ TODO: Use a package extension loading mechanism when CodecZstd.jl is present. 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`. -``` +```julia using HDF5.Filters bitshuf = ExternalFilter(32008, Cuint[0, 0]) @@ -285,15 +285,25 @@ The `_by_idx` calls are easy to use via a simple `for` loop but are very ineffic 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. + --- # 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 Chunks | H5Dchunk_iter | H5Dget_chunk_info | Ratio | +|---|---|---|---| +| 64 | 2e-4 s | 5e-4 s | 2.4 | +| 256 | 7e-4 s | 5e-3 s | 6 | +| 1024 | 3e-3 s | 5e-2 s | 16 | +| 4096 | 1e-2 s | 7e-1 s | 57 | +| 16384 | 6e-2 s | 1e2 s | 208 | --- -# Multithreading +# 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. @@ -301,6 +311,14 @@ The `_iterate` calls require a C callback, `op`, and can be challenging to use b --- +# 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? + +--- + # Parallelization via Message Passing Interface (MPI) @@ -309,9 +327,17 @@ The `_iterate` calls require a C callback, `op`, and can be challenging to use b # Other Related Julia Packages -* HDF5_jll.jl, C Library from HDF Group -* MAT.jl, MATLAB files -* JLD.jl, Julia Data Format +* 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, a wrapper for the NetCDF C library, which incorporates HDF5 --- + +# 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 From c58a7467967436183657384398cf5dc93a0674cd Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Mon, 24 Jul 2023 11:17:55 -0400 Subject: [PATCH 08/18] move related --- .../juliacon_2023_presentation.md | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 33496942b..4520a8286 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -35,6 +35,7 @@ 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 --- @@ -87,6 +88,16 @@ It consists of --- +# 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, a wrapper for the NetCDF C library, which incorporates HDF5 + +--- + # HDF5.jl Early Contributors * There are many contributors @@ -390,17 +401,6 @@ Usage otherwise same as normal: - if collective, use `dxpl_mpio=:collective` option with `create_dataset`/`open_dataset` - some limitations (e.g no datasets with variable-length strings). - ---- - -# Other 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, a wrapper for the NetCDF C library, which incorporates HDF5 - --- # Summary From 0648f26681da57d4169d34ac8909058b6b60f283 Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Mon, 24 Jul 2023 11:23:21 -0400 Subject: [PATCH 09/18] add some more --- docs/juliacon_2023/juliacon_2023_presentation.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 4520a8286..145120c05 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -4,7 +4,7 @@ marp: true # HDF5.jl: Hierarchical Data Storage for Julia -Mark Kittisopikul, Simon Byrne, Mustafa Mohamad +Mark Kittisopikul (HHMI), Simon Byrne (Caltech), Mustafa Mohamad (UCalgary) --- @@ -93,8 +93,8 @@ It consists of * 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, a wrapper for the NetCDF C library, which incorporates HDF5 +* 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 --- From f5b0b05f429693433a0c76eb0bf832080410ebbd Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Mon, 24 Jul 2023 11:27:19 -0400 Subject: [PATCH 10/18] add comments --- docs/juliacon_2023/juliacon_2023_presentation.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 33496942b..721cdc761 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -60,6 +60,8 @@ 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 |................| @@ -108,6 +110,8 @@ It consists of # What advantages does Julia bring to HDF5.jl? + + * HDF5.jl wraps the C library directly in Julia via `@ccall`. * This is partially automated via Clang.jl and https://github.com/mkitti/LibHDF5.jl . * HDF5.jl dynamically create types to match the stored HDF5 types. @@ -277,6 +281,8 @@ end # 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. `h5a_iterate(obj_id::hid_t, idx_type::Cint, order::Cint, n::Ptr{hsize_t}, op::Ptr{Cvoid}, op_data::Any)`, `op` is function pointer From 36da5d4a0a0bb9fcbe2ed794e4b4709ef851ed7e Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Mon, 24 Jul 2023 11:43:31 -0400 Subject: [PATCH 11/18] move prefs to end --- .../juliacon_2023_presentation.md | 58 +++++++------------ 1 file changed, 22 insertions(+), 36 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index f821b7dda..e4f497f72 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -337,51 +337,19 @@ Where are the compressed chunks and can we decompress them in parallel? --- -# Parallelization via Message Passing Interface (MPI) +# Parallelization via MPI - Message Passing Interface (MPI) is an interface for single-program, multiple-data (SPMD) parallelism. - Launch multiple processes running the same program - ```sh - mpiexec -n program ... - ``` + ```sh + mpiexec -n 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 ----- - -## Configuring HDF5 with MPI (in upcoming 0.17 release) - -- Now works with default MPI & HDF5 JLLs -- On HPC clusters, will typically want to use the system-provided MPI library - - Integrate with resource manager, make use of specialized network hardware, GPU-aware interfaces - -### Option 1: use MPItrampoline -Requires building a wrapper library around your MPI library. - ```julia - MPIPreferences.use_jll_binary("MPItrampoline_jll") - ``` - - HDF5.jl should work directly. - ----- -### Option 2: use system binary directly -Requires system-provided MPI + HDF5 libraries. - -```julia -using MPIPreferences -MPIPreferences.use_system_binary() -``` -Need to set corresponding preferences for HDF5 -```julia -using Preferences, HDF5 -set_preferences!(HDF5, - "libhdf5" => "/path/to/your/libhdf5.so", - "libhdf5_hl" => "/path/to/your/libhdf5_hl.so", - force = true) -``` - --- ## Using MPI + HDF5 @@ -409,6 +377,24 @@ Usage otherwise same as normal: --- +# 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 +```julia +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. From e0d07a75b1dec46114997a076069748edab6d75d Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Mon, 24 Jul 2023 12:12:32 -0400 Subject: [PATCH 12/18] add virtual dataset --- .../juliacon_2023_presentation.md | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index e4f497f72..3da5271e9 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -335,6 +335,28 @@ Where are the compressed chunks and can we decompress them in parallel? * 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. + +```julia +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 From ade8cef8a7d389abc7948b1e0ca45bd14dec9291 Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Mon, 24 Jul 2023 12:16:19 -0400 Subject: [PATCH 13/18] Reorganize slides, move some to end --- .../juliacon_2023_presentation.md | 126 ++++++++++-------- .../juliacon_2023_presentation.pdf | Bin 285424 -> 337642 bytes 2 files changed, 68 insertions(+), 58 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index f821b7dda..06e04150d 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -49,36 +49,6 @@ 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. - -![width:800px](images/superblock.png) - -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 |................| -``` - -![](images/format_signature.png) - ---- - # What is HDF5.jl? HDF5.jl is a wrapper around the HDF5 C Library. @@ -100,13 +70,14 @@ It consists of --- -# HDF5.jl Early Contributors +# 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 --- @@ -115,7 +86,6 @@ It consists of * 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 --- @@ -123,12 +93,12 @@ It consists of -* HDF5.jl wraps the C library directly in Julia via `@ccall`. - * This is partially automated via Clang.jl and https://github.com/mkitti/LibHDF5.jl . * 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`. + * This is partially automated via Clang.jl and https://github.com/mkitti/LibHDF5.jl . --- @@ -290,22 +260,6 @@ end ``` --- -# 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. `h5a_iterate(obj_id::hid_t, idx_type::Cint, order::Cint, n::Ptr{hsize_t}, op::Ptr{Cvoid}, op_data::Any)`, `op` is function pointer - -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. - ---- - # 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? @@ -329,14 +283,6 @@ Where are the compressed chunks and can we decompress them in parallel? --- -# 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? - ---- - # Parallelization via Message Passing Interface (MPI) - Message Passing Interface (MPI) is an interface for single-program, multiple-data (SPMD) parallelism. @@ -415,3 +361,67 @@ Usage otherwise same as normal: * 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. + +![width:800px](images/superblock.png) + +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 |................| +``` + +![](images/format_signature.png) + +--- + +# 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. `h5a_iterate(obj_id::hid_t, idx_type::Cint, order::Cint, n::Ptr{hsize_t}, op::Ptr{Cvoid}, op_data::Any)`, `op` is function pointer + +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. + +--- + +# 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? diff --git 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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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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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zKG_AyvB<)6$H}q5D=o_v6JJtd!9wn>pd-6QNNu{MQrq+9CWK&h`DAwgdSpNuKVAyHCF HDogz@>K&n* From ade56462f9d23e9eb988491a8a722d6d4b684cfb Mon Sep 17 00:00:00 2001 From: Simon Byrne Date: Mon, 24 Jul 2023 12:19:34 -0400 Subject: [PATCH 15/18] mention Erik --- docs/juliacon_2023/juliacon_2023_presentation.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 25668c6e7..c48951fd2 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -87,6 +87,9 @@ It consists of * Mark Kittisopikul has been expanding API coverage, especially with chunking * Simon Byrne has been working on package organization, filter interface, virtual datasets, and parallelization +## Special mention +- Erik Schnetter for building HDF5 in Yggdrasil + --- # What advantages does Julia bring to HDF5.jl? From 60e89ab484f4a32fc8add5d2e2d4069e79abf8e6 Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Mon, 24 Jul 2023 12:29:20 -0400 Subject: [PATCH 16/18] Sync changes --- .../juliacon_2023_presentation.html | 40 ++++++++---------- .../juliacon_2023_presentation.md | 8 +--- .../juliacon_2023_presentation.pdf | Bin 343421 -> 344018 bytes 3 files changed, 19 insertions(+), 29 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.html b/docs/juliacon_2023/juliacon_2023_presentation.html index 29a3dfe1f..3ed980c93 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.html +++ b/docs/juliacon_2023/juliacon_2023_presentation.html @@ -88,6 +88,10 @@

    HDF5.jl Current Developers

  • Mark Kittisopikul has been expanding API coverage, especially with chunking
  • Simon Byrne has been working on package organization, filter interface, virtual datasets, and parallelization
  • +

    Special mention

    +
      +
    • Erik Schnetter for building HDF5 in Yggdrasil
    • +

    What advantages does Julia bring to HDF5.jl?

    @@ -295,15 +299,7 @@

    The HDF5 C

    -
    -

    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

    • @@ -331,7 +327,7 @@

      Virtual datasets

      )
    -
    +

    Parallelization via MPI

    • Message Passing Interface (MPI) is an interface for single-program, multiple-data (SPMD) parallelism. @@ -348,7 +344,7 @@

      Parallelization via MPI

    • MPI.jl provides Julia bindings
    -
    +

    Using MPI + HDF5

    Load and initialize MPI

    using MPI, HDF5
    @@ -362,7 +358,7 @@ 

    Using MPI + HDF5

  • 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.
    • @@ -375,7 +371,7 @@

      Using MPI + HDF5

    • some limitations (e.g no datasets with variable-length strings).
    -
    +

    Configuring HDF5 (in upcoming 0.17 release)

    • @@ -397,7 +393,7 @@

      Configuring HDF5 (in upcoming force = true)

    -
    +

    Summary

    • HDF5 is a format, C library, and data model for storing hierarchical information.
    • @@ -406,23 +402,23 @@

      Summary

    • 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  |................|
    @@ -436,7 +432,7 @@ 

    A HDF5 Hex Dump

    -
    +

    Iteration

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

    @@ -448,8 +444,8 @@

    Iteration

    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.

    -
    -

    Concurrency with Direct I/O

    +
    +

    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.
    • @@ -457,5 +453,5 @@

      Concurrency with Direct I/O

    reorder

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

    Move to the end

    move to end

    give examples ?

    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 c48951fd2..97d768293 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -57,6 +57,7 @@ It consists of * A low level interface, a direct mapping to the C API * A mid level interface, lightweight helpers * A high level interface, a Julia API + --- @@ -286,13 +287,6 @@ Where are the compressed chunks and can we decompress them in parallel? --- -# 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 diff --git 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zN&l-CNED3pE-CXJ%0lq1Z8888jXDb+ARPdj*$kE~8LQoSX>_0d?tl9N*(*b;Pq}n= zyU6#>^};lxo&q~$pe!hAB~i+3%yTEw$U5P_et|q8M=FL~lqLNgxh_YlN5y;RLSf`! zv%R%M*&E|7q~di_b7Yo0=>=qlJa`}PUj|ZSgFLAga!sC;@&q|D?bxX-Ps)tslm~BH z0>c?-ySqLWRqFcyjnXL)c18xL8S)`5CvgRFc6D{*x{F4G?Zasb6X~+(ab0ZE(rnHo z`>JprF0jJFAoXy?r47HT5V)Lvl?}fHk0oI3VhL@!v4r7oHbg?O4RJHchKS#_ncQnB za|xUuLD%hj>9PfSE-y01*6wt+r4dHTF3hi9-)U5jsYTT9;&hJ`6jbhS#J!}xfc&UH RN=YUHz5 Date: Wed, 26 Jul 2023 11:45:10 -0400 Subject: [PATCH 17/18] Pre-JuliaCon version --- .../juliacon_2023_presentation.html | 9 ++++++++- .../juliacon_2023_presentation.md | 9 +++++++++ .../juliacon_2023_presentation.pdf | Bin 344018 -> 342532 bytes 3 files changed, 17 insertions(+), 1 deletion(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.html b/docs/juliacon_2023/juliacon_2023_presentation.html index 3ed980c93..0337c2289 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.html +++ b/docs/juliacon_2023/juliacon_2023_presentation.html @@ -371,7 +371,7 @@

    Using MPI + HDF5

  • some limitations (e.g no datasets with variable-length strings).
  • -
    +

    Configuring HDF5 (in upcoming 0.17 release)

    • @@ -392,6 +392,13 @@

      Configuring HDF5 (in upcoming "libhdf5_hl" => "/path/to/your/libhdf5_hl.so", force = true) +

      Applications

      +
        +
      • CliMa
      • +
      • Plots.jl backend
      • +
      • JLD.jl (dependency) and JLD2.jl (interop reference)
      • +
      • Checkpointing.jl
      • +

    Summary

    diff --git a/docs/juliacon_2023/juliacon_2023_presentation.md b/docs/juliacon_2023/juliacon_2023_presentation.md index 97d768293..16c1c1306 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -368,6 +368,15 @@ set_preferences!(HDF5, --- +# Applications + +* CliMa +* Plots.jl backend +* JLD.jl (dependency) and JLD2.jl (interop reference) +* Checkpointing.jl + +--- + # Summary * HDF5 is a format, C library, and data model for storing hierarchical information. diff --git a/docs/juliacon_2023/juliacon_2023_presentation.pdf b/docs/juliacon_2023/juliacon_2023_presentation.pdf index 998858d0f888eb74febba9caf42682d9817a692d..93876ae8582f208cdf08adc256b8b9821385f0cc 100644 GIT binary patch delta 96032 zcmY(pV{l;48#Ef*wl*8vwv&yyv2F7t8)sv0Y}>Z&WMkWQ?*4!8d#mpKaGt4|sp_ff z({*OL=gfU!1l?oA@&L?m0s?T(E>5O~ws0O9jjC%__{=E2Th%kpxwks5pvGbLxyS)c zIIp0#=u`zW_g}f+EIZ>f)A$m-X7vs&4m1ADuAm>iIvXr|3;LA#zrFH(q`{GaY=>d2 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ff6abce755f39435da928cb23c2dbbb5b7565b8b Mon Sep 17 00:00:00 2001 From: Mark Kittisopikul Date: Wed, 26 Jul 2023 11:57:14 -0400 Subject: [PATCH 18/18] Fix typos in names, thanks Joshua Lampert! --- .../juliacon_2023_presentation.html | 22 ++++++++++-------- .../juliacon_2023_presentation.md | 2 +- .../juliacon_2023_presentation.pdf | Bin 342532 -> 343884 bytes 3 files changed, 13 insertions(+), 11 deletions(-) diff --git a/docs/juliacon_2023/juliacon_2023_presentation.html b/docs/juliacon_2023/juliacon_2023_presentation.html index 0337c2289..78c987653 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.html +++ b/docs/juliacon_2023/juliacon_2023_presentation.html @@ -78,7 +78,7 @@

    HDF5.jl Early and Recent Contribut
  • 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
  • +
  • Other recent contributors: t-bltg, Hendrik Ranocha, Nathan Zimmerberg, Joshua Lampert, Tamas Gal, David MacMahon, Juan Ignacio Polanco, Michael Schlottke-Lakemper, linwaytin, Dmitri Iouchtchenko, Lorenzo Van Munoz, Jared Wahlstrand, Julian Samaroo, machakann, James Hester, Ralph Kube, Kristoffer Carlsson
  • @@ -371,7 +371,7 @@

    Using MPI + HDF5

  • some limitations (e.g no datasets with variable-length strings).
  • -
    +

    Configuring HDF5 (in upcoming 0.17 release)

    • @@ -392,6 +392,8 @@

      Configuring HDF5 (in upcoming "libhdf5_hl" => "/path/to/your/libhdf5_hl.so", force = true) +

    +

    Applications

    • CliMa
    • @@ -400,7 +402,7 @@

      Applications

    • Checkpointing.jl
    -
    +

    Summary

    • HDF5 is a format, C library, and data model for storing hierarchical information.
    • @@ -409,23 +411,23 @@

      Summary

    • 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  |................|
    @@ -439,7 +441,7 @@ 

    A HDF5 Hex Dump

    -
    +

    Iteration

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

    @@ -451,7 +453,7 @@

    Iteration

    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.

    -
    +

    Concurrency with Direct I/O

    • The HDF5 C library provides byte offsets for continguous and chunked datasets
    • @@ -460,5 +462,5 @@

      Concurrency with Direct I/O

    give examples ?

    reorder

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

    Move to the end

    move to end

    give examples ?

    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 16c1c1306..a9c4531c7 100644 --- a/docs/juliacon_2023/juliacon_2023_presentation.md +++ b/docs/juliacon_2023/juliacon_2023_presentation.md @@ -78,7 +78,7 @@ It consists of * 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 +* Other recent contributors: t-bltg, Hendrik Ranocha, Nathan Zimmerberg, Joshua Lampert, Tamas Gal, David MacMahon, Juan Ignacio Polanco, Michael Schlottke-Lakemper, linwaytin, Dmitri Iouchtchenko, Lorenzo Van Munoz, Jared Wahlstrand, Julian Samaroo, machakann, James Hester, Ralph Kube, Kristoffer Carlsson --- diff --git a/docs/juliacon_2023/juliacon_2023_presentation.pdf b/docs/juliacon_2023/juliacon_2023_presentation.pdf index 93876ae8582f208cdf08adc256b8b9821385f0cc..f71c29f42c85aaa46e0babe3612590687c6ad12e 100644 GIT binary patch delta 88612 zcmY&;Q40hnO;_+Xrz9Zd{uVB9mCR5l`T8R2|#YHpqKVq6-mq5P+8&IW|ELB9N&I}}!h zT7D3duf|r#*e-=FAZA2I<~b88-3xU9^s7(Xh&lazK0evLvfz720m4bUdfoE++if|f z6o`~dH9cvdt&Tq3ZdXS~7Ft`0UhldGK;)6a0355_5nJUSjO@P zov>=!EC;W2HNq{=F3(k;)r=R@hOL|8rFeXCeM->wg#xN-aLAIe$X{#+4~=a09S$Xg zsC9FY^>ih6)u~NsV@CacWuR>!64`zV+(2c(`&FyG>%gTzexXrushB}8%`weJJEMcl z07fc$To6|EN9ulLqm( zo5s4HOgD{VUA*I!z-gj~Ta9M9hAqM{Gn)GqUC_D>H}r-OWTlr%8{}*=%qN5Z z0_%lnO6KoBc?s6Bhu9?>WSR-wFXol8zs@GD@djnTX|sK|IUdjx_4$0;K79LL2@%M)a?!txX>`Z-;TbN7`lDCH@jjdP5^`IenQ>*=|gK2{Qxy{ zN`0tj=C4lj`=6+HpKskTMtTHCJU-a`I(fCoWCC*9f&K0JM(9up(QOV6I*n2 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