diff --git a/.gitignore b/.gitignore index dae5c097..2163d406 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ Manifest-v*.toml /docs/build/ /.benchmarkci *.h5 +slurm_log/ diff --git a/Project.toml b/Project.toml index fe4412d7..259a4e6a 100644 --- a/Project.toml +++ b/Project.toml @@ -5,7 +5,9 @@ version = "1.3.0" [deps] ChunkSplitters = "ae650224-84b6-46f8-82ea-d812ca08434e" +ExactOptimalTransport = "24df6009-d856-477c-ac5c-91f668376b31" HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" +HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195" OpenMPI_jll = "fe0851c0-eecd-5654-98d4-656369965a5c" @@ -19,7 +21,9 @@ YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" [compat] ChunkSplitters = "3.1" +ExactOptimalTransport = "0.2.5" HDF5 = "0.14, 0.15, 0.16, 0.17" +HiGHS = "1.19.0" LinearAlgebra = "<0.0.1, 1" MPI = "0.20.22" OpenMPI_jll = "4" diff --git a/extra/Manifest.toml b/extra/Manifest.toml index 962affa6..33bf5744 100644 --- a/extra/Manifest.toml +++ b/extra/Manifest.toml @@ -1,203 +1,261 @@ # This file is machine-generated - editing it directly is not advised -[[Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "f1b523983a58802c4695851926203b36e28f09db" -uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.3.0" +[[AbstractPlutoDingetjes]] +deps = ["Pkg"] +git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a" +uuid = "6e696c72-6542-2067-7265-42206c756150" +version = "1.3.2" + +[[AliasTables]] +deps = ["PtrArrays", "Random"] +git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff" +uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8" +version = "1.1.3" + +[[ArgTools]] +uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" +version = "1.1.2" [[Artifacts]] -deps = ["Pkg"] -git-tree-sha1 = "c30985d8821e0cd73870b17b0ed0ce6dc44cb744" uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" -version = "1.3.0" +version = "1.11.0" [[Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" +version = "1.11.0" + +[[BitFlags]] +git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d" +uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" +version = "0.1.9" [[Blosc]] deps = ["Blosc_jll"] -git-tree-sha1 = "84cf7d0f8fd46ca6f1b3e0305b4b4a37afe50fd6" +git-tree-sha1 = "310b77648d38c223d947ff3f50f511d08690b8d5" uuid = "a74b3585-a348-5f62-a45c-50e91977d574" -version = "0.7.0" +version = "0.7.3" [[Blosc_jll]] -deps = ["Libdl", "Lz4_jll", "Pkg", "Zlib_jll", "Zstd_jll"] -git-tree-sha1 = "aa9ef39b54a168c3df1b2911e7797e4feee50fbe" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Lz4_jll", "Zlib_jll", "Zstd_jll"] +git-tree-sha1 = "535c80f1c0847a4c967ea945fca21becc9de1522" uuid = "0b7ba130-8d10-5ba8-a3d6-c5182647fed9" -version = "1.14.3+1" +version = "1.21.7+0" [[Bzip2_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "c3598e525718abcc440f69cc6d5f60dda0a1b61e" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1b96ea4a01afe0ea4090c5c8039690672dd13f2e" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" -version = "1.0.6+5" +version = "1.0.9+0" [[Cairo_jll]] -deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "e2f47f6d8337369411569fd45ae5753ca10394c6" +deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] +git-tree-sha1 = "fde3bf89aead2e723284a8ff9cdf5b551ed700e8" uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" -version = "1.16.0+6" +version = "1.18.5+0" + +[[CodecZlib]] +deps = ["TranscodingStreams", "Zlib_jll"] +git-tree-sha1 = "962834c22b66e32aa10f7611c08c8ca4e20749a9" +uuid = "944b1d66-785c-5afd-91f1-9de20f533193" +version = "0.7.8" [[ColorSchemes]] -deps = ["ColorTypes", "Colors", "FixedPointNumbers", "Random", "StaticArrays"] -git-tree-sha1 = "c8fd01e4b736013bc61b704871d20503b33ea402" +deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] +git-tree-sha1 = "a656525c8b46aa6a1c76891552ed5381bb32ae7b" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" -version = "3.12.1" +version = "3.30.0" [[ColorTypes]] deps = ["FixedPointNumbers", "Random"] -git-tree-sha1 = "024fe24d83e4a5bf5fc80501a314ce0d1aa35597" +git-tree-sha1 = "67e11ee83a43eb71ddc950302c53bf33f0690dfe" uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" +version = "0.12.1" +weakdeps = ["StyledStrings"] + + [ColorTypes.extensions] + StyledStringsExt = "StyledStrings" + +[[ColorVectorSpace]] +deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "Requires", "Statistics", "TensorCore"] +git-tree-sha1 = "8b3b6f87ce8f65a2b4f857528fd8d70086cd72b1" +uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4" version = "0.11.0" + [ColorVectorSpace.extensions] + SpecialFunctionsExt = "SpecialFunctions" + + [ColorVectorSpace.weakdeps] + SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b" + [[Colors]] deps = ["ColorTypes", "FixedPointNumbers", "Reexport"] -git-tree-sha1 = "417b0ed7b8b838aa6ca0a87aadf1bb9eb111ce40" +git-tree-sha1 = "37ea44092930b1811e666c3bc38065d7d87fcc74" uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" -version = "0.12.8" +version = "0.13.1" [[Compat]] deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"] -git-tree-sha1 = "e4e2b39db08f967cc1360951f01e8a75ec441cab" +git-tree-sha1 = "d476eaeddfcdf0de15a67a948331c69a585495fa" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "3.30.0" +version = "3.47.0" [[CompilerSupportLibraries_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "8e695f735fca77e9708e795eda62afdb869cbb70" +deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "0.3.4+0" +version = "1.1.1+0" -[[ConstructionBase]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "1dc43957fb9a1574fa1b7a449e101bd1fd3a9fb7" -uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.2.1" +[[ConcurrentUtilities]] +deps = ["Serialization", "Sockets"] +git-tree-sha1 = "d9d26935a0bcffc87d2613ce14c527c99fc543fd" +uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" +version = "2.5.0" [[Contour]] -deps = ["StaticArrays"] -git-tree-sha1 = "9f02045d934dc030edad45944ea80dbd1f0ebea7" +git-tree-sha1 = "439e35b0b36e2e5881738abc8857bd92ad6ff9a8" uuid = "d38c429a-6771-53c6-b99e-75d170b6e991" -version = "0.5.7" +version = "0.6.3" [[DataAPI]] -git-tree-sha1 = "dfb3b7e89e395be1e25c2ad6d7690dc29cc53b1d" +git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" -version = "1.6.0" +version = "1.16.0" [[DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "4437b64df1e0adccc3e5d1adbc3ac741095e4677" +git-tree-sha1 = "4e1fe97fdaed23e9dc21d4d664bea76b65fc50a0" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.9" - -[[DataValueInterfaces]] -git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" -uuid = "e2d170a0-9d28-54be-80f0-106bbe20a464" -version = "1.0.0" +version = "0.18.22" [[Dates]] deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" +version = "1.11.0" + +[[Dbus_jll]] +deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl"] +git-tree-sha1 = "473e9afc9cf30814eb67ffa5f2db7df82c3ad9fd" +uuid = "ee1fde0b-3d02-5ea6-8484-8dfef6360eab" +version = "1.16.2+0" [[DelimitedFiles]] deps = ["Mmap"] +git-tree-sha1 = "9e2f36d3c96a820c678f2f1f1782582fcf685bae" uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" +version = "1.9.1" [[Distributed]] deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" +version = "1.11.0" -[[EarCut_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "92d8f9f208637e8d2d28c664051a00569c01493d" -uuid = "5ae413db-bbd1-5e63-b57d-d24a61df00f5" -version = "2.1.5+1" +[[DocStringExtensions]] +git-tree-sha1 = "7442a5dfe1ebb773c29cc2962a8980f47221d76c" +uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" +version = "0.9.5" + +[[Downloads]] +deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"] +uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6" +version = "1.6.0" + +[[EpollShim_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "8a4be429317c42cfae6a7fc03c31bad1970c310d" +uuid = "2702e6a9-849d-5ed8-8c21-79e8b8f9ee43" +version = "0.0.20230411+1" + +[[ExceptionUnwrapping]] +deps = ["Test"] +git-tree-sha1 = "d36f682e590a83d63d1c7dbd287573764682d12a" +uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" +version = "0.1.11" [[Expat_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "1402e52fcda25064f51c77a9655ce8680b76acf0" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "d55dffd9ae73ff72f1c0482454dcf2ec6c6c4a63" uuid = "2e619515-83b5-522b-bb60-26c02a35a201" -version = "2.2.7+6" +version = "2.6.5+0" [[FFMPEG]] -deps = ["FFMPEG_jll", "x264_jll"] -git-tree-sha1 = "9a73ffdc375be61b0e4516d83d880b265366fe1f" +deps = ["FFMPEG_jll"] +git-tree-sha1 = "53ebe7511fa11d33bec688a9178fac4e49eeee00" uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a" -version = "0.4.0" +version = "0.4.2" [[FFMPEG_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "LibVPX_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "Pkg", "Zlib_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] -git-tree-sha1 = "3cc57ad0a213808473eafef4845a74766242e05f" +deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Pkg", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] +git-tree-sha1 = "74faea50c1d007c85837327f6775bea60b5492dd" uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5" -version = "4.3.1+4" +version = "4.4.2+2" + +[[FileWatching]] +uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" +version = "1.11.0" [[FixedPointNumbers]] deps = ["Statistics"] -git-tree-sha1 = "335bfdceacc84c5cdf16aadc768aa5ddfc5383cc" +git-tree-sha1 = "05882d6995ae5c12bb5f36dd2ed3f61c98cbb172" uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" -version = "0.8.4" +version = "0.8.5" [[Fontconfig_jll]] -deps = ["Artifacts", "Bzip2_jll", "Expat_jll", "FreeType2_jll", "JLLWrappers", "Libdl", "Libuuid_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "35895cf184ceaab11fd778b4590144034a167a2f" +deps = ["Artifacts", "Bzip2_jll", "Expat_jll", "FreeType2_jll", "JLLWrappers", "Libdl", "Libuuid_jll", "Zlib_jll"] +git-tree-sha1 = "301b5d5d731a0654825f1f2e906990f7141a106b" uuid = "a3f928ae-7b40-5064-980b-68af3947d34b" -version = "2.13.1+14" +version = "2.16.0+0" -[[Formatting]] -deps = ["Printf"] -git-tree-sha1 = "8339d61043228fdd3eb658d86c926cb282ae72a8" -uuid = "59287772-0a20-5a39-b81b-1366585eb4c0" -version = "0.4.2" +[[Format]] +git-tree-sha1 = "9c68794ef81b08086aeb32eeaf33531668d5f5fc" +uuid = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" +version = "1.3.7" [[FreeType2_jll]] -deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "cbd58c9deb1d304f5a245a0b7eb841a2560cfec6" +deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "2c5512e11c791d1baed2049c5652441b28fc6a31" uuid = "d7e528f0-a631-5988-bf34-fe36492bcfd7" -version = "2.10.1+5" +version = "2.13.4+0" [[FriBidi_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "0d20aed5b14dd4c9a2453c1b601d08e1149679cc" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "7a214fdac5ed5f59a22c2d9a885a16da1c74bbc7" uuid = "559328eb-81f9-559d-9380-de523a88c83c" -version = "1.0.5+6" +version = "1.0.17+0" [[GLFW_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] -git-tree-sha1 = "a199aefead29c3c2638c3571a9993b564109d45a" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll", "libdecor_jll", "xkbcommon_jll"] +git-tree-sha1 = "fcb0584ff34e25155876418979d4c8971243bb89" uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89" -version = "3.3.4+0" +version = "3.4.0+2" [[GR]] -deps = ["Base64", "DelimitedFiles", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Printf", "Random", "Serialization", "Sockets", "Test", "UUIDs"] -git-tree-sha1 = "011458b83178ac913dc4eb73b229af45bdde5d83" +deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "p7zip_jll"] +git-tree-sha1 = "ddda044ca260ee324c5fc07edb6d7cf3f0b9c350" uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -version = "0.57.4" +version = "0.73.5" [[GR_jll]] -deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Pkg", "Qt5Base_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "90acee5c38f4933342fa9a3bbc483119d20e7033" +deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"] +git-tree-sha1 = "278e5e0f820178e8a26df3184fcb2280717c79b1" uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" -version = "0.57.2+0" - -[[GeometryBasics]] -deps = ["EarCut_jll", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"] -git-tree-sha1 = "4136b8a5668341e58398bb472754bff4ba0456ff" -uuid = "5c1252a2-5f33-56bf-86c9-59e7332b4326" -version = "0.3.12" +version = "0.73.5+0" -[[Gettext_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"] -git-tree-sha1 = "8c14294a079216000a0bdca5ec5a447f073ddc9d" -uuid = "78b55507-aeef-58d4-861c-77aaff3498b1" -version = "0.20.1+7" +[[GettextRuntime_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll"] +git-tree-sha1 = "45288942190db7c5f760f59c04495064eedf9340" +uuid = "b0724c58-0f36-5564-988d-3bb0596ebc4a" +version = "0.22.4+0" [[Glib_jll]] -deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "04690cc5008b38ecbdfede949220bc7d9ba26397" +deps = ["Artifacts", "GettextRuntime_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE2_jll", "Zlib_jll"] +git-tree-sha1 = "35fbd0cefb04a516104b8e183ce0df11b70a3f1a" uuid = "7746bdde-850d-59dc-9ae8-88ece973131d" -version = "2.59.0+4" +version = "2.84.3+0" + +[[Graphite2_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "8a6dbda1fd736d60cc477d99f2e7a042acfa46e8" +uuid = "3b182d85-2403-5c21-9c21-1e1f0cc25472" +version = "1.3.15+0" [[Grisu]] git-tree-sha1 = "53bb909d1151e57e2484c3d1b53e19552b887fb2" @@ -206,345 +264,478 @@ version = "1.0.2" [[HDF5]] deps = ["Blosc", "Compat", "HDF5_jll", "Libdl", "Mmap", "Random", "Requires"] -git-tree-sha1 = "1d18a48a037b14052ca462ea9d05dee3ac607d23" +git-tree-sha1 = "698c099c6613d7b7f151832868728f426abe698b" uuid = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" -version = "0.15.5" +version = "0.15.7" [[HDF5_jll]] deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "OpenSSL_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "fd83fa0bde42e01952757f01149dd968c06c4dba" +git-tree-sha1 = "4cc2bb72df6ff40b055295fdef6d92955f9dede8" uuid = "0234f1f7-429e-5d53-9886-15a909be8d59" -version = "1.12.0+1" +version = "1.12.2+2" [[HTTP]] -deps = ["Base64", "Dates", "IniFile", "MbedTLS", "NetworkOptions", "Sockets", "URIs"] -git-tree-sha1 = "1fd26bc48f96adcdd8823f7fc300053faf3d7ba1" +deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "PrecompileTools", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] +git-tree-sha1 = "ed5e9c58612c4e081aecdb6e1a479e18462e041e" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "0.9.9" +version = "1.10.17" + +[[HarfBuzz_jll]] +deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll"] +git-tree-sha1 = "f923f9a774fcf3f5cb761bfa43aeadd689714813" +uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" +version = "8.5.1+0" -[[IniFile]] +[[Hyperscript]] deps = ["Test"] -git-tree-sha1 = "098e4d2c533924c921f9f9847274f2ad89e018b8" -uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f" -version = "0.5.0" +git-tree-sha1 = "179267cfa5e712760cd43dcae385d7ea90cc25a4" +uuid = "47d2ed2b-36de-50cf-bf87-49c2cf4b8b91" +version = "0.0.5" + +[[HypertextLiteral]] +deps = ["Tricks"] +git-tree-sha1 = "7134810b1afce04bbc1045ca1985fbe81ce17653" +uuid = "ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2" +version = "0.9.5" + +[[IOCapture]] +deps = ["Logging", "Random"] +git-tree-sha1 = "b6d6bfdd7ce25b0f9b2f6b3dd56b2673a66c8770" +uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" +version = "0.2.5" [[InteractiveUtils]] deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" +version = "1.11.0" -[[IterTools]] -git-tree-sha1 = "05110a2ab1fc5f932622ffea2a003221f4782c18" -uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e" -version = "1.3.0" +[[IrrationalConstants]] +git-tree-sha1 = "e2222959fbc6c19554dc15174c81bf7bf3aa691c" +uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" +version = "0.2.4" -[[IteratorInterfaceExtensions]] -git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" -uuid = "82899510-4779-5014-852e-03e436cf321d" -version = "1.0.0" +[[JLFzf]] +deps = ["REPL", "Random", "fzf_jll"] +git-tree-sha1 = "82f7acdc599b65e0f8ccd270ffa1467c21cb647b" +uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c" +version = "0.1.11" [[JLLWrappers]] -deps = ["Preferences"] -git-tree-sha1 = "642a199af8b68253517b80bd3bfd17eb4e84df6e" +deps = ["Artifacts", "Preferences"] +git-tree-sha1 = "a007feb38b422fbdab534406aeca1b86823cb4d6" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.3.0" +version = "1.7.0" [[JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] -git-tree-sha1 = "81690084b6198a2e1da36fcfda16eeca9f9f24e4" +git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -version = "0.21.1" +version = "0.21.4" [[JpegTurbo_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "9aff0587d9603ea0de2c6f6300d9f9492bbefbd3" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "eac1206917768cb54957c65a615460d87b455fc1" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" -version = "2.0.1+3" +version = "3.1.1+0" [[LAME_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "df381151e871f41ee86cee4f5f6fd598b8a68826" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "059aabebaa7c82ccb853dd4a0ee9d17796f7e1bc" uuid = "c1c5ebd0-6772-5130-a774-d5fcae4a789d" -version = "3.100.0+3" +version = "3.100.3+0" -[[LZO_jll]] +[[LERC_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f128cd6cd05ffd6d3df0523ed99b90ff6f9b349a" +git-tree-sha1 = "bf36f528eec6634efc60d7ec062008f171071434" +uuid = "88015f11-f218-50d7-93a8-a6af411a945d" +version = "3.0.0+1" + +[[LLVMOpenMP_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "eb62a3deb62fc6d8822c0c4bef73e4412419c5d8" +uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" +version = "18.1.8+0" + +[[LZO_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1c602b1127f4751facb671441ca72715cc95938a" uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac" -version = "2.10.0+3" +version = "2.10.3+0" [[LaTeXStrings]] -git-tree-sha1 = "c7f1c695e06c01b95a67f0cd1d34994f3e7db104" +git-tree-sha1 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c" uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" -version = "1.2.1" +version = "1.4.0" [[Latexify]] -deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "Printf", "Requires"] -git-tree-sha1 = "f77a16cb3804f4a74f57e5272a6a4a9a628577cb" +deps = ["Format", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Requires"] +git-tree-sha1 = "4f34eaabe49ecb3fb0d58d6015e32fd31a733199" uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" -version = "0.15.5" +version = "0.16.8" + + [Latexify.extensions] + DataFramesExt = "DataFrames" + SparseArraysExt = "SparseArrays" + SymEngineExt = "SymEngine" + TectonicExt = "tectonic_jll" + + [Latexify.weakdeps] + DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" + SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" + SymEngine = "123dc426-2d89-5057-bbad-38513e3affd8" + tectonic_jll = "d7dd28d6-a5e6-559c-9131-7eb760cdacc5" + +[[LibCURL]] +deps = ["LibCURL_jll", "MozillaCACerts_jll"] +uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" +version = "0.6.4" [[LibCURL_jll]] -deps = ["LibSSH2_jll", "Libdl", "MbedTLS_jll", "Pkg", "Zlib_jll", "nghttp2_jll"] -git-tree-sha1 = "897d962c20031e6012bba7b3dcb7a667170dad17" +deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "7.70.0+2" +version = "8.6.0+0" [[LibGit2]] -deps = ["Printf"] +deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"] uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" +version = "1.11.0" + +[[LibGit2_jll]] +deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"] +uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5" +version = "1.7.2+0" [[LibSSH2_jll]] -deps = ["Libdl", "MbedTLS_jll", "Pkg"] -git-tree-sha1 = "717705533148132e5466f2924b9a3657b16158e8" +deps = ["Artifacts", "Libdl", "MbedTLS_jll"] uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8" -version = "1.9.0+3" - -[[LibVPX_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "85fcc80c3052be96619affa2fe2e6d2da3908e11" -uuid = "dd192d2f-8180-539f-9fb4-cc70b1dcf69a" -version = "1.9.0+1" +version = "1.11.0+1" [[Libdl]] uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" +version = "1.11.0" [[Libffi_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "a2cd088a88c0d37eef7d209fd3d8712febce0d90" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "c8da7e6a91781c41a863611c7e966098d783c57a" uuid = "e9f186c6-92d2-5b65-8a66-fee21dc1b490" -version = "3.2.1+4" - -[[Libgcrypt_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll", "Pkg"] -git-tree-sha1 = "b391a18ab1170a2e568f9fb8d83bc7c780cb9999" -uuid = "d4300ac3-e22c-5743-9152-c294e39db1e4" -version = "1.8.5+4" +version = "3.4.7+0" [[Libglvnd_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"] -git-tree-sha1 = "7739f837d6447403596a75d19ed01fd08d6f56bf" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll", "Xorg_libXext_jll"] +git-tree-sha1 = "d36c21b9e7c172a44a10484125024495e2625ac0" uuid = "7e76a0d4-f3c7-5321-8279-8d96eeed0f29" -version = "1.3.0+3" - -[[Libgpg_error_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "ec7f2e8ad5c9fa99fc773376cdbc86d9a5a23cb7" -uuid = "7add5ba3-2f88-524e-9cd5-f83b8a55f7b8" -version = "1.36.0+3" +version = "1.7.1+1" [[Libiconv_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "8e924324b2e9275a51407a4e06deb3455b1e359f" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = 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"f879ae9edbaa2c74c922e8b85bb83cc84ea1450b" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "321ccef73a96ba828cd51f2ab5b9f917fa73945a" uuid = "38a345b3-de98-5d2b-a5d3-14cd9215e700" -version = "2.34.0+7" +version = "2.41.0+0" [[LinearAlgebra]] -deps = ["Libdl"] +deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"] uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +version = "1.11.0" + +[[LogExpFunctions]] +deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] +git-tree-sha1 = "13ca9e2586b89836fd20cccf56e57e2b9ae7f38f" +uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" +version = "0.3.29" + + [LogExpFunctions.extensions] + LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" + LogExpFunctionsChangesOfVariablesExt = "ChangesOfVariables" + LogExpFunctionsInverseFunctionsExt = "InverseFunctions" + + [LogExpFunctions.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + ChangesOfVariables = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" + InverseFunctions = 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"Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "fbf139bce07a534df0e699dbb5f5cc9346f95cc1" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" -version = "0.9.1+5" +version = "1.9.2+0" diff --git a/extra/Plot_copy_states_julia.ipynb b/extra/Plot_copy_states_julia.ipynb new file mode 100644 index 00000000..89986e6d --- /dev/null +++ b/extra/Plot_copy_states_julia.ipynb @@ -0,0 +1,1564 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Copy States Optimization\n", + "\n", + "This notebook demonstrates how to parse and visualize the results produced by the ParticleDA.jl script \n", + "[`extra/weak_scaling/kathleen_slurm_copy_states.sh`](extra/weak_scaling/kathleen_slurm_copy_states.sh).\n", + "\n", + "It provides a practical example of how to process the benchmarking output generated on the Kathleen cluster, \n", + "summarize weak scaling performance metrics, and reproduce the plots presented in the accompanying report.\n", + "\n", + "The notebook serves as a reference for interpreting the performance impact of the new `copy_states!` \n", + "optimisation and offers a reproducible workflow for future benchmarking experiments.\n", + "\n", + "Before running the code blocks, run the following commands to get the kernel prepared:\n", + "```sh\n", + "julia\n", + "import Pkg\n", + "Pkg.update()\n", + "Pkg.precompile()\n", + "\n", + "using Pkg\n", + "Pkg.build(\"IJulia\")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import necessary packages and define helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "rename_optimization (generic function with 1 method)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using HDF5\n", + "using Serialization\n", + "using DataFrames, Plots, Statistics\n", + "using Plots.PlotMeasures \n", + "using DataFramesMeta\n", + "using Missings\n", + "using ColorSchemes\n", + "\n", + "has_data(v) = any(!ismissing, v)\n", + "safe_mean(v) = has_data(v) ? mean(skipmissing(v)) : missing\n", + "safe_std(v) = has_data(v) ? std(collect(skipmissing(v)); corrected=false) : missing\n", + "safe_min(v) = has_data(v) ? minimum(skipmissing(v)) : missing\n", + "safe_max(v) = has_data(v) ? maximum(skipmissing(v)) : missing\n", + "\n", + "\n", + "# The following functions rename the plot labels for better readability\n", + "# e.g., rename_regime(\"1(1.0) particles\") returns \"Extreme degeneracy\"\n", + "# If no match is found, the original string is returned\n", + "function rename_regime(trial)\n", + " if trial == \"1(1.0) particles\"\n", + " return \"Extreme degeneracy\"\n", + " elseif trial == \"1(0.999) particles\"\n", + " return \"High degeneracy\"\n", + " elseif trial == \"1(0.99) particles\"\n", + " return \"Near-degeneracy(0.99)\"\n", + " elseif trial == \"50%(1.0) particles\"\n", + " return \"Balanced case\"\n", + " elseif trial == \"all(1.0) particles\"\n", + " return \"Uniform case\"\n", + " end\n", + " return trial\n", + "end\n", + "\n", + "function rename_stats(metric)\n", + " if metric == \"waitall_ratio\"\n", + " return \"Waitall/Overall Time Ratio\"\n", + " elseif metric == \"overall\"\n", + " return \"Overall Time (s)\"\n", + " elseif metric == \"copy_states\"\n", + " return \"Copy States Time (s)\"\n", + " elseif metric == \"waitall_phase\"\n", + " return \"Waitall Time (s)\"\n", + " elseif metric == \"optimize_resample\"\n", + " return \"Optimise Resample (s)\"\n", + " elseif metric == \"resample_ratio\"\n", + " return \"Overhead Time Ratio\"\n", + " end\n", + " return metric\n", + "end\n", + "\n", + "function rename_optimization(optimization)\n", + " if optimization == \"original\"\n", + " return \"Baseline\"\n", + " elseif optimization == \"only_optimize_resampling\"\n", + " return \"Optimise Resampling Only\"\n", + " elseif optimization == \"only_dedup\"\n", + " return \"Deduplication Only\"\n", + " elseif optimization == \"dedup_threading\"\n", + " return \"Deduplication + Threading\"\n", + " elseif optimization == \"dedup_threading_optimize_resampling\"\n", + " return \"Full Optimisation\"\n", + " end\n", + " return optimization\n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Functions to parse output" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "df_from_h5 (generic function with 1 method)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "function timer_dict_to_df(timer_dict, optimization, flatten=false)\n", + " rows = []\n", + "\n", + " for (trial, ranks) in timer_dict\n", + " # parse the original trial string in the format \"k:total_rank:nprt_per_rank:n_float_per_particle:perm:p\"\n", + " k, total_rank, nprt_per_rank, n_float_per_particle, perm, p = split(trial, \":\")\n", + " k = k == \"half\" ? \"50%\" : k\n", + " trial_name = \"$k($p) particles\"\n", + " for (rank, timers) in ranks\n", + " function format_row(op, metric, value)\n", + " return (\n", + " optimization = optimization,\n", + " trial = String(trial_name),\n", + " perm = String(perm),\n", + " total_rank = parse(Int, total_rank),\n", + " particle_size = n_float_per_particle,\n", + " nprt_per_rank = nprt_per_rank,\n", + " rank = string(rank),\n", + " op = String(op),\n", + " metric = String(metric),\n", + " value = value,\n", + " )\n", + " end\n", + " function recursive_push(inner_timers)\n", + " for (op, metrics) in inner_timers\n", + " if op == \"receive loop\" && flatten\n", + " for (inner_op, inner_metrics) in metrics[\"inner_timers\"]\n", + " for (metric, value) in inner_metrics\n", + " push!(rows, format_row(inner_op, metric, value))\n", + " end\n", + " end\n", + " else\n", + " push!(rows, format_row(op, \"time_s\", metrics[\"time_ns\"] / 1e9))\n", + " push!(rows, format_row(op, \"n_calls\", metrics[\"n_calls\"]))\n", + " recursive_push(metrics[\"inner_timers\"])\n", + " end\n", + " end\n", + " end\n", + " push!(rows, format_row(\"overall\", \"time_s\", timers[\"time_ns\"] / 1e9))\n", + " push!(rows, format_row(\"overall\", \"n_calls\", timers[\"n_calls\"]))\n", + " recursive_push(timers[\"inner_timers\"])\n", + " end\n", + " end\n", + " return DataFrame(rows)\n", + "end\n", + "\n", + "function df_from_h5(root_dir, category)\n", + " optimization = something(split(category, \"/\")[end], \"unknown_optimization\")\n", + "\n", + " root = dirname(@__FILE__)\n", + " h5path(rank) = joinpath(root, \"../$(root_dir)/$(category)/\", \"all_timers_$(rank).h5\") \n", + " all_timer_dfs = DataFrame()\n", + " for rank in [2, 4, 8, 16, 32]\n", + " blob = h5open(h5path(rank)) do f\n", + " read(f, \"all_timers\")\n", + " end\n", + "\n", + " # Deserialize back into Dict{String,Dict{Int,Dict{String,Any}}}\n", + " merged_timers = deserialize(IOBuffer(blob))\n", + "\n", + " # Convert to DataFrame\n", + " timer_df = timer_dict_to_df(merged_timers, optimization)\n", + " \n", + " # Concat to all_timer_dfs\n", + " all_timer_dfs = vcat(all_timer_dfs, timer_df)\n", + " end\n", + "\n", + " wide = unstack(\n", + " all_timer_dfs,\n", + " [:optimization, :trial, :perm, :total_rank, :nprt_per_rank, :particle_size, :rank, :op],\n", + " :metric,\n", + " :value\n", + " )\n", + " # Sort by perm and trial\n", + " wide = sort!(wide, [:perm, :trial])\n", + "\n", + " rand_df = wide[wide.perm .== \"randperm\", :]\n", + " return rand_df\n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Render utils" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "render_stats (generic function with 2 methods)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "function widen_ops(df::DataFrame)\n", + " rename!(df, Symbol.(names(df)))\n", + "\n", + " keys = [:optimization, :trial, :perm, :total_rank, :nprt_per_rank, :particle_size, :rank]\n", + " wanted = [\"overall\",\"waitall\",\"broadcast\",\"copy states\",\"write from buffer\",\"write to buffer\", \"waitall phase\", \"buffer write-back\",\n", + " \"receive loop\",\"send loop\",\"irecv\",\"remote duplicates copy\", \"optimize resample\", \"local copy\", \"remote receive\", \"send plan\", \"receive plan\", \"local replication\", \"local replication\", \"remote replication\"]\n", + "\n", + " df2 = subset(df, :op => ByRow(in(wanted)))\n", + "\n", + " transform!(df2, :n_calls => ByRow(x -> x == 0 ? missing : x) => :n_calls)\n", + "\n", + " g = groupby(df2, vcat(keys, [:op]))\n", + " avg = combine(g,\n", + " [:time_s, :n_calls] => ((t, c) -> sum(t) / sum(c) ) => :time_s_per_call\n", + " )\n", + "\n", + " wide = unstack(avg, keys, :op, :time_s_per_call; combine=first)\n", + " rename!(wide, Symbol.(replace.(string.(names(wide)), r\"[ -]\" => \"_\")))\n", + " return wide\n", + "end\n", + "\n", + "function stats(df_stats)\n", + " df_stats = @chain df_stats[df_stats.total_rank .> 1, :] begin\n", + " @transform(\n", + " :waitall_ratio = :waitall_phase ./ :overall,\n", + " :localcopy_ratio = :local_replication ./ :overall,\n", + " :remotedup_ratio = :remote_replication ./ :overall,\n", + " :writefrombuf_ratio = :buffer_write_back ./ :overall,\n", + " :resample_ratio = :optimize_resample ./ :overall,\n", + " )\n", + " end\n", + " return df_stats\n", + "end\n", + "\n", + "function render_stats(df, stats_to_plot=[\"overall\", \"copy_states\", \"waitall_phase\", \"waitall_ratio\"])\n", + " # Configuration for plots\n", + " trials = [\"1(1.0) particles\", \"1(0.99) particles\", \"50%(1.0) particles\", \"all(1.0) particles\"]\n", + " optimizations = unique(df.optimization)\n", + " ntrials = length(trials)\n", + " nstats = length(stats_to_plot)\n", + " n_opts = length(optimizations)\n", + "\n", + " # --- Create the individual plots for the grid ---\n", + " axes = []\n", + " for (i, trial) in enumerate(trials)\n", + " for (j, stat) in enumerate(stats_to_plot)\n", + " # Create a new plot object for this subplot\n", + " p = plot(legend=false, palette=:auto, bottom_margin=10mm)\n", + " \n", + " # --- Set conditional labels and titles ---\n", + " if i == 1\n", + " plot!(p, title = rename_stats(stat), top_margin = 10mm)\n", + " end\n", + " if j == 1\n", + " plot!(p, ylabel = rename_regime(trial), left_margin = 20mm)\n", + " end\n", + " if i == ntrials\n", + " plot!(p, xlabel = \"Total Rank\", bottom_margin = 10mm)\n", + " end\n", + "\n", + " df_filtered = df[df.trial .== trial, :]\n", + " \n", + " # --- Add each optimization as a series to the plot ---\n", + " for optimization in optimizations\n", + " sub = select(df_filtered, :optimization, :total_rank, :rank, stat => :value)\n", + " subrk = sub[sub.optimization .== optimization, :]\n", + " isempty(subrk) && continue\n", + "\n", + " # Determine if log scale should be used\n", + " use_log = stat in [\"overall\", \"copy_states\", \"waitall_phase\"]\n", + "\n", + " # Group and calculate statistics.\n", + " g = if use_log\n", + " # Add a small epsilon to avoid log(0) issues\n", + " subrk_log = @transform(subrk, :log_value = log10.(:value .+ 1e-12))\n", + " \n", + " # Calculate stats in both linear (for mean) and log (for std) space\n", + " g_linear = combine(groupby(subrk, :total_rank), :value => safe_mean => :mean)\n", + " g_log = combine(groupby(subrk_log, :total_rank), :log_value => safe_std => :std_log)\n", + " \n", + " # Join them together and return\n", + " leftjoin(g_linear, g_log, on = :total_rank)\n", + " else\n", + " combine(groupby(subrk, :total_rank),\n", + " :value => safe_mean => :mean,\n", + " :value => safe_std => :std)\n", + " end\n", + "\n", + " # Filter out rows with missing data and sort\n", + " filter!(row -> all(!ismissing, values(row)), g)\n", + " sort!(g, :total_rank)\n", + " isempty(g) && continue\n", + " \n", + " # Prepare plot variables (mean and ribbon) based on the scale\n", + " local plot_mean, ribbon_val\n", + " if use_log\n", + " plot!(p, yaxis=:log)\n", + " # Use the arithmetic mean for the central line\n", + " plot_mean = g.mean\n", + " \n", + " # Calculate the geometric standard deviation as a multiplicative factor\n", + " gstd = 10 .^ g.std_log\n", + " \n", + " # Define ribbon bounds multiplicatively around the arithmetic mean\n", + " lower_bound = plot_mean ./ gstd\n", + " upper_bound = plot_mean .* gstd\n", + " \n", + " # The ribbon is the distance from the central line\n", + " ribbon_val = (plot_mean .- lower_bound, upper_bound .- plot_mean)\n", + " else\n", + " plot_mean = g.mean\n", + " ribbon_val = g.std\n", + " end\n", + "\n", + " plot!(p, string.(g.total_rank), plot_mean,\n", + " ribbon = ribbon_val, seriestype = :path,\n", + " markersize = 4, linewidth = 1.5)\n", + " end\n", + " push!(axes, p)\n", + " end\n", + " end\n", + "\n", + " # --- Create the global legend ---\n", + " legend_labels = permutedims([rename_optimization(opt) for opt in optimizations])\n", + " legend_plot = plot(\n", + " (1:n_opts)', # Dummy data for legend entries\n", + " legend = :top,\n", + " legend_columns = -1, # Force a single horizontal row\n", + " labels = legend_labels,\n", + " framestyle = :none,\n", + " palette = :auto,\n", + " size = (1,1)\n", + " )\n", + "\n", + " # --- Combine legend and plots into the final figure ---\n", + " final_fig = plot(\n", + " legend_plot,\n", + " axes...,\n", + " layout = @layout([A{0.05h}; grid(ntrials, nstats)]),\n", + " size = (400 * nstats, 300 * ntrials)\n", + " )\n", + "\n", + " return final_fig\n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Render output\n", + "\n", + "⚠️ Important Notes on Experiment Rendering\n", + "\n", + "After the recent code refactor, **the experiment output is currently limited** — \n", + "users can only reproduce results for **deduplicating** with **threading** and **full optimization (with optimized resampling)**. \n", + "Comparative outputs like **“original vs. deduplicating”** cannot be generated without code changes.\n", + "\n", + "If you wish to **re-render the complete experiment results** (including comparisons), \n", + "please **download the pre-generated outputs** from the following link and place them in your working directory:\n", + "📂 [Experiment Results on Google Drive](https://drive.google.com/drive/folders/1iUj-jO30uOswNp_1vpmpfT9S0MJkUmV5?usp=drive_link)\n", + "\n", + "Make sure your experiment output directory follows the structure below for successful rendering:\n", + "```\n", + "/\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"output_type": "execute_result" + } + ], + "source": [ + "root_dir = \"test/output\"\n", + "dfs = [df_from_h5(root_dir, category) for category in [\n", + " \"original\",\n", + " \"only_dedup\",\n", + " \"dedup_threading\",\n", + " \"dedup_threading_optimize_resampling\"\n", + "]]\n", + "\n", + "union_df = vcat(dfs...)\n", + "union_df = widen_ops(union_df)\n", + "union_df = stats(union_df)\n", + "render_stats(union_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.11.5", + "language": "julia", + "name": "julia-1.11" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/extra/Project.toml b/extra/Project.toml index f5896477..ae957020 100644 --- a/extra/Project.toml +++ b/extra/Project.toml @@ -1,4 +1,5 @@ [deps] +ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4" HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" diff --git a/extra/weak_scaling/Project.toml b/extra/weak_scaling/Project.toml new file mode 100644 index 00000000..57c0e24f --- /dev/null +++ b/extra/weak_scaling/Project.toml @@ -0,0 +1,2 @@ +[deps] +ParticleDA = "61cd1fb4-f4c4-4bc8-80c6-ea5639a6ca2e" diff --git a/extra/weak_scaling/kathleen_slurm_copy_states.sh b/extra/weak_scaling/kathleen_slurm_copy_states.sh new file mode 100644 index 00000000..9a6a0024 --- /dev/null +++ b/extra/weak_scaling/kathleen_slurm_copy_states.sh @@ -0,0 +1,25 @@ +#!/bin/bash -l +#SBATCH --job-name=ParticleDAScaling +#SBATCH --time=02:00:00 +#SBATCH --mem-per-cpu=4G +#SBATCH --cpus-per-task=40 +#SBATCH --nodes=16 +#SBATCH --ntasks-per-node=1 +#SBATCH --output=slurm_log/%x-%j.out +#SBATCH --error=slurm_log/%x-%j.err + +export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK +export JULIA_NUM_THREADS=$OMP_NUM_THREADS + +julia --project=. -e 'using Pkg; Pkg.instantiate(); Pkg.precompile()' + +PARTICLEDA_WEAKSCALING_DIR=$HOME/ParticleDA.jl/extra/weak_scaling +RESULTS_DIR=$PARTICLEDA_WEAKSCALING_DIR/output +mkdir -p $RESULTS_DIR +JULIA_DIR=$HOME/.julia + +cd $PARTICLEDA_WEAKSCALING_DIR + +$JULIA_DIR/bin/mpiexecjl -n $SLURM_NNODES\ + julia --project=. \ + $PARTICLEDA_WEAKSCALING_DIR/optimized_copy_states.jl -t $RESULTS_DIR/all_timers_$SLURM_NNODES.h5 -o \ No newline at end of file diff --git a/extra/weak_scaling/kathleen_slurm_weak_scaling.sh b/extra/weak_scaling/kathleen_slurm_weak_scaling.sh new file mode 100644 index 00000000..5317e775 --- /dev/null +++ b/extra/weak_scaling/kathleen_slurm_weak_scaling.sh @@ -0,0 +1,21 @@ +#!/bin/bash -l +#SBATCH --job-name=ParticleDAScaling +#SBATCH --time=02:00:00 +#SBATCH --mem-per-cpu=4G +#SBATCH --nodes=4 +#SBATCH --ntasks-per-node=1 +#SBATCH --cpus-per-task=40 +#SBATCH --output=slurm_log/%x-%j.out +#SBATCH --error=slurm_log/%x-%j.err + +export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK +export JULIA_NUM_THREADS=$OMP_NUM_THREADS + +PARTICLEDA_WEAKSCALING_DIR=$HOME/ParticleDA.jl/extra/weak_scaling +JULIA_DIR=$HOME/.julia + +cd $PARTICLEDA_WEAKSCALING_DIR + +$JULIA_DIR/bin/mpiexecjl -n $SLURM_NNODES\ + julia --project=$PARTICLEDA_WEAKSCALING_DIR \ + $PARTICLEDA_WEAKSCALING_DIR/run_particleda.jl \ No newline at end of file diff --git a/extra/weak_scaling/optimized_copy_states.jl b/extra/weak_scaling/optimized_copy_states.jl new file mode 100644 index 00000000..864b1d30 --- /dev/null +++ b/extra/weak_scaling/optimized_copy_states.jl @@ -0,0 +1,183 @@ +using Test, ParticleDA, MPI, Random, TimerOutputs, HDF5, Serialization, Logging +TimerOutputs.enable_debug_timings(ParticleDA) + +const rng = Random.TaskLocalRNG() +Random.seed!(rng, 1234) + +build_expected_buffer(indices, r, npr, nf) = + Float64.((1:nf) .+ ((indices[r*npr .+ (1:npr)] .- 1) .* nf)') +function sample_indices(n::Int; k::Int=10, p::Float64=0.99) + @assert 1 ≤ k < n "k must be between 1 and n-1" + @assert 0.0 ≤ p ≤ 1.0 "p must be in [0,1]" + + # 1. Pick k unique “favorite” indices via a random permutation + fav = randperm(rng, n)[1:k] + + # 2. Build the complement + other = setdiff(1:n, fav) + + # 3. Decide for each of the n draws whether it comes from fav (true) or other (false) + mask = rand(rng, n) .< p # Bool vector of length n + + # 4. Preallocate result + result = Vector{Int}(undef, n) + + # 5. How many draws from each group? + na = count(mask) + nb = n - na + + # 6. Sample with replacement from each group + result[mask] .= rand(rng, fav, na) + result[.!mask] .= rand(rng, other, nb) + + return result +end + +MPI.Init() +my_rank = MPI.Comm_rank(MPI.COMM_WORLD) +my_size = MPI.Comm_size(MPI.COMM_WORLD) + +@info "Number of threads available: ", Threads.nthreads() + +n_particle_per_rank = 1000 +n_particle = n_particle_per_rank * my_size +verbose = "-v" in ARGS || "--verbose" in ARGS +output_timer = "-t" in ARGS || "--output-timer" in ARGS +if output_timer + if length(ARGS) < 2 + error("Please provide the output filename for timers.") + end + output_filename = ARGS[2] + @info "Outputting timers to HDF5 file '$output_filename'" +end +# default: dedup enabled for testing +no_dedup = "-nd" in ARGS || "--no-dedup" in ARGS +@info "Deduplication enabled: ", !no_dedup +optimize_resample = "-o" in ARGS || "--optimize-resample" in ARGS +@info "Optimized resampling enabled: ", optimize_resample + +n_float_per_particle = 100000 +# total number of floats per rank +N = n_float_per_particle * n_particle_per_rank +# build & reshape in one go, then allocate a similar array +init_local_states = reshape((my_rank*N .+ (1:N)) .* 1.0, + n_float_per_particle, + n_particle_per_rank) +local_states = similar(init_local_states) + +if verbose + for i = 1:my_size + if i == my_rank + 1 + @info "rank $(my_rank): local states: ", local_states + end + MPI.Barrier(MPI.COMM_WORLD) + end +end + +buffer = zeros((n_float_per_particle, n_particle_per_rank)) + + +trial_sets = Dict( + "1:$my_size:$n_particle_per_rank:$n_float_per_particle:randperm:1.0" => () -> sort!(sample_indices(n_particle, k=1, p=1.0)), + "1:$my_size:$n_particle_per_rank:$n_float_per_particle:randperm:0.99" => () -> sort!(sample_indices(n_particle, k=1, p=0.99)), + "half:$my_size:$n_particle_per_rank:$n_float_per_particle:randperm:1.0" => () -> sort!(sample_indices(n_particle, k=div(n_particle, 2), p=1.0)), + "all:$my_size:$n_particle_per_rank:$n_float_per_particle:randperm:1.0" => () -> collect(1:n_particle) +) + +local_timer_dicts = Dict{String, Dict{String,Any}}() + +for (trial_name, indices_func) in trial_sets + if verbose && my_rank == 0 + @info "Resampling particles to indices ", indices + end + indices = collect(1:n_particle) # Placeholder for actual indices + # repeat experiment 10 times to get average time + # warm up run + @info "Warm up run..." + ParticleDA.optimized_resample!(indices, my_size) + ParticleDA.copy_states!( + local_states, + buffer, + indices, + my_rank, + n_particle_per_rank + ) + @info "Starting timed runs for trial '$trial_name'..." + + timer = TimerOutputs.TimerOutput("copy_states") + for _ in 1:10 + indices = collect(indices_func()) + copyto!(local_states, init_local_states) + @timeit timer "overall" begin + if optimize_resample && my_rank == 0 + @timeit timer "optimize resample" indices = ParticleDA.optimized_resample!(indices, my_size) + end + + # broadcast no matter whether we optimize or not to eliminate the overall time bias + @timeit timer "broadcast" MPI.Bcast!(indices, 0, MPI.COMM_WORLD) + + @timeit timer "copy states" ParticleDA.copy_states!( + local_states, + buffer, + indices, + my_rank, + n_particle_per_rank, + timer + ) + end + end + local_timer_dicts[trial_name] = TimerOutputs.todict(timer["overall"]) + + if verbose + for i = 1:my_size + if i == my_rank + 1 + # reconstruct expected buffer for this rank + expected = build_expected_buffer(indices, my_rank, + n_particle_per_rank, + n_float_per_particle) + + # compare + match = local_states == expected + + @info "rank $(my_rank): local_states =" + show(stdout, "text/plain", local_states); + @info "rank $(my_rank): expected =" + show(stdout, "text/plain", expected); + @info "rank $(my_rank): match = ", match + end + MPI.Barrier(MPI.COMM_WORLD) + end + end + + # build the expected buffer + expected = build_expected_buffer(indices, my_rank, + n_particle_per_rank, + n_float_per_particle) + + @test local_states == expected +end + +if output_timer + all_local = MPI.gather(local_timer_dicts, MPI.COMM_WORLD; root=0) + if my_rank == 0 + merged = Dict{String,Dict{Int,Dict{String,Any}}}() + for r in 0:my_size-1 + for (trial, tdict) in all_local[r+1] + rankmap = get!(merged, trial, Dict{Int,Dict{String,Any}}()) + rankmap[r] = tdict + end + end + + buf = IOBuffer() + serialize(buf, merged) + blob = take!(buf) # Vector{UInt8} + + h5open(output_filename, "w") do f + write(f, "all_timers", blob) + end + end +end + + +MPI.Barrier(MPI.COMM_WORLD) +MPI.Finalize() diff --git a/extra/weak_scaling/parametersW1.yaml b/extra/weak_scaling/parametersW1.yaml index 230c6f85..be3191b1 100644 --- a/extra/weak_scaling/parametersW1.yaml +++ b/extra/weak_scaling/parametersW1.yaml @@ -1,32 +1,33 @@ -# Parameter file for ParticleDA using TDAC model with Optimal Filter. -# Developer: Alex Beskos, May 2021 - -filter: - nprt: 200 - n_time_step: 250 - verbose: true - enable_timers: true - output_filename: "optimal_filter_test.h5" - +simulate_observations: + n_time_step: 51 + seed: 123 model: llw2d: - x_length: 200.0e3 - y_length: 200.0e3 - nx: 51 - ny: 51 station_filename: "stationsW1.txt" - obs_noise_std: [0.01] - nu: 2.5 - lambda: 5.0e3 - sigma : [0.1, 10.0, 10.0] - nu_initial_state: 2.5 - lambda_initial_state: 5.0e3 - sigma_initial_state: 0.001 - - n_integration_step: 10 - time_step: 5.0 - peak_height: 30.0 - peak_position: [1e4,1e4] + sigma: 0.01 + nx: 51 + n_integration_step: 16 + x_length: 200000.0 + peak_position: + - 10000.0 + - 10000.0 + lambda_initial_state: 5000.0 + obs_noise_std: + - 0.0025 + padding: 0 + lambda: 5000.0 + ny: 51 + y_length: 200000.0 + sigma_initial_state: 0.001 + use_peak_initial_state_mean: true + observed_state_var_indices: + - 1 + time_step: 0.5 +filter: + optimize_resampling: true + output_filename: "llw2d_filtering.h5" + nprt: 2000 # Arbitrary - this will be overwritten in run_particleda.jl script + enable_timers: true diff --git a/extra/weak_scaling/run_particleda.jl b/extra/weak_scaling/run_particleda.jl index 9b12a146..965ec785 100644 --- a/extra/weak_scaling/run_particleda.jl +++ b/extra/weak_scaling/run_particleda.jl @@ -1,34 +1,56 @@ using ParticleDA using TimerOutputs using MPI +using LinearAlgebra +using YAML +using Logging + +# Verify BLAS implementation is OpenBLAS +@assert occursin("openblas", string(BLAS.get_config())) + +# Set size of thread pool for BLAS operations to 1 +BLAS.set_num_threads(1) # Initialise MPI MPI.Init() mpi_size = MPI.Comm_size(MPI.COMM_WORLD) -# Save some variables for later use -test_dir = joinpath(dirname(pathof(ParticleDA)), "..", "test") -module_src = joinpath(test_dir, "model", "model.jl") -input_file = joinpath(test_dir, "integration_test_1.yaml") -truth_file = "test_observations.h5" -# Instantiate the test environment -using Pkg -Pkg.activate(test_dir) -Pkg.instantiate() - # Include the sample model source code and load it -include(module_src) -using .Model +llw2d_src = joinpath(dirname(pathof(ParticleDA)), "..", "test", "models", "llw2d.jl") +include(llw2d_src) +using .LLW2d +observation_file = "test_observations.h5" +parameters_file = "parametersW1.yaml" +output_file = "llw2d_filtering.h5" +#filter_type = OptimalFilter +filter_type = BootstrapFilter +summary_stat_type = NaiveMeanSummaryStat + +my_rank = MPI.Comm_rank(MPI.COMM_WORLD) -input_dict = ParticleDA.read_input_file("parametersW1.yaml") -run_custom_params = Dict(input_dict) +@info "Rank $(my_rank): # Julia threads = $(Threads.nthreads()), # BLAS threads = $(BLAS.get_num_threads())" + +if my_rank == 0 && !isfile(observation_file) + observation_sequence = simulate_observations_from_model( + LLW2d.init, parameters_file, observation_file + ) +end +if my_rank == 0 && isfile(output_file) + rm(output_file) +end + +MPI.Barrier(MPI.COMM_WORLD) -# Real run TimerOutputs.enable_debug_timings(ParticleDA) +# update parameters to enable weak scaling +parameters = YAML.load_file(parameters_file) +parameters["filter"]["nprt"] = mpi_size * 1000 +open(parameters_file, "w") do io + YAML.write(io, parameters) +end + +@info "Optimized resampling enabled: ", parameters["filter"]["optimize_resampling"] -run_custom_params["model"]["llw2d"]["padding"]=0 -run_custom_params["filter"]["verbose"]=true -run_custom_params["filter"]["enable_timers"]=true -run_custom_params["filter"]["output_filename"]=string("weak_scaling_r",mpi_size,".h5") -run_custom_params["filter"]["nprt"]=mpi_size * 64 -ParticleDA.run_particle_filter(Model.init, run_custom_params, BootstrapFilter(), truth_file) +final_states, final_statistics = run_particle_filter( + LLW2d.init, parameters_file, observation_file, filter_type, summary_stat_type +) diff --git a/extra/weak_scaling/test_observations.h5 b/extra/weak_scaling/test_observations.h5 deleted file mode 100644 index 678210e2..00000000 Binary files a/extra/weak_scaling/test_observations.h5 and /dev/null differ diff --git a/src/ParticleDA.jl b/src/ParticleDA.jl index 31de0ab4..20bbf8fb 100644 --- a/src/ParticleDA.jl +++ b/src/ParticleDA.jl @@ -271,6 +271,12 @@ function run_particle_filter( @timeit_debug timer "Resample" resample!( filter_data.resampling_indices, filter_data.weights, rng ) + if filter_params.optimize_resampling + # Optimize resampling indices to minimize data movement when copying states + @timeit_debug timer "Optimize Resample" filter_data.resampling_indices .= optimized_resample!( + filter_data.resampling_indices, my_size + ) + end else @timeit_debug timer "Gather weights" MPI.Gather!( @@ -286,7 +292,8 @@ function run_particle_filter( filter_data.copy_buffer, filter_data.resampling_indices, my_rank, - nprt_per_rank + nprt_per_rank, + timer ) if filter_params.verbose diff --git a/src/params.jl b/src/params.jl index cf73d469..bdf5abce 100644 --- a/src/params.jl +++ b/src/params.jl @@ -17,6 +17,8 @@ Parameters for ParticleDA run. Keyword arguments: the scheduler to balance load across threads but potentially increase overheads. If simulation of the model being filtered use multiple threads then it may be beneficial to set the `n_tasks = 1` to avoid too much contention between threads. +* `optimize_resampling::Bool`: Flag to control whether to optimize resampling indices + to minimize data movement when copying states between MPI ranks. """ Base.@kwdef struct FilterParameters{V<:Union{AbstractSet, AbstractVector}} master_rank::Int = 0 @@ -27,6 +29,7 @@ Base.@kwdef struct FilterParameters{V<:Union{AbstractSet, AbstractVector}} particle_save_time_indices::V = [] seed::Union{Nothing, Int} = nothing n_tasks::Int = -1 + optimize_resampling::Bool = true end diff --git a/src/utils.jl b/src/utils.jl index 5980420c..762047a9 100644 --- a/src/utils.jl +++ b/src/utils.jl @@ -1,3 +1,4 @@ +using ExactOptimalTransport, HiGHS function normalized_exp!(weight::AbstractVector) weight .-= maximum(weight) @@ -5,6 +6,45 @@ function normalized_exp!(weight::AbstractVector) weight ./= sum(weight) end +# Solve an optimal transport problem to minimize the number of particles +# that need to be communicated between ranks during resampling. +function optimized_resample!(resampled_indices::AbstractVector{Int}, nrank::Int) + nprt_per_rank = length(resampled_indices) ÷ nrank + stock_queue = [Int[] for _ in 1:nrank] + + # Assign each resampled index to its corresponding rank + for resampled_idx in resampled_indices + rank = div(resampled_idx - 1, nprt_per_rank) + 1 + push!(stock_queue[rank], resampled_idx) + end + + supply_vector = Float64[length(stock_queue[rank]) for rank in 1:nrank] + demand_vector = Float64.(fill(nprt_per_rank, nrank)) + cost_matrix = ones(Float64, nrank, nrank) + for i in 1:nrank + cost_matrix[i, i] = 0 + end + + # Solve the optimal transport problem using the HiGHS solver + optimizer = HiGHS.Optimizer() + HiGHS._set_option(optimizer, "log_to_console", false) + y = emd(supply_vector, demand_vector, cost_matrix, optimizer) + + # update resampled_indices + for i in 1:nrank + idx = 1 + for j in 1:nrank + nmove = Int(y[j, i]) + for _ in 1:nmove + resampled_indices[(i - 1) * nprt_per_rank + idx] = popfirst!(stock_queue[j]) + idx += 1 + end + end + end + return resampled_indices +end + + # Resample particles from given weights using Stochastic Universal Sampling function resample!( resampled_indices::AbstractVector{Int}, @@ -49,7 +89,8 @@ function copy_states!( buffer::AbstractMatrix{T}, resampling_indices::Vector{Int}, my_rank::Int, - nprt_per_rank::Int + nprt_per_rank::Int, + to::TimerOutputs.TimerOutput = TimerOutputs.TimerOutput() ) where T # These are the particle indices stored on this rank @@ -58,39 +99,101 @@ function copy_states!( # These are the particle indices this rank should have after resampling particles_want = resampling_indices[particles_have] - # These are the ranks that have the particles this rank should have - rank_has = floor.(Int, (particles_want .- 1) / nprt_per_rank) - - # We could work out how many sends and receives we have to do and allocate - # this appropriately but, lazy reqs = Vector{MPI.Request}(undef, 0) + # Determine which particles need to be sent where + @timeit_debug to "send plan" sends_to = _determine_sends(resampling_indices, my_rank, nprt_per_rank) + # Categorize the particles this rank wants into local copies and remote copies + @timeit_debug to "receive plan" local_copies, remote_copies = _categorize_wants(particles_want, my_rank, nprt_per_rank) + # Send particles to processes that want them - for (k,id) in enumerate(resampling_indices) - rank_wants = floor(Int, (k - 1) / nprt_per_rank) - if id in particles_have && rank_wants != my_rank - local_id = id - my_rank * nprt_per_rank - req = MPI.Isend(view(particles, :, local_id), rank_wants, id, MPI.COMM_WORLD) - push!(reqs, req) + @timeit_debug to "send loop" begin + for (dest_rank, unique_ids) in sends_to + for id in unique_ids + local_id = id - my_rank * nprt_per_rank + req = MPI.Isend(view(particles, :, local_id), dest_rank, id, MPI.COMM_WORLD) + push!(reqs, req) + end end end # Receive particles this rank wants from ranks that have them # If I already have them, just do a local copy # Receive into a buffer so we dont accidentally overwrite stuff - for (k,proc,id) in zip(1:nprt_per_rank, rank_has, particles_want) - if proc == my_rank + @timeit_debug to "receive loop" begin + for (id, buffer_indices) in remote_copies + source_rank = floor(Int, (id - 1) / nprt_per_rank) + first_k = buffer_indices[1] # Receive into the first required slot. + req = MPI.Irecv!(view(buffer, :, first_k), source_rank, id, MPI.COMM_WORLD) + push!(reqs, req) + end + end + + # Perform local copies + @timeit_debug to "local replication" begin + for (id, buffer_indices) in local_copies local_id = id - my_rank * nprt_per_rank - buffer[:, k] .= view(particles, :, local_id) - else - req = MPI.Irecv!(view(buffer, :, k), proc, id, MPI.COMM_WORLD) - push!(reqs,req) + source_view = view(particles, :, local_id) + Threads.@threads for k in buffer_indices + buffer[:, k] .= source_view + end end end # Wait for all comms to complete - MPI.Waitall(reqs) + @timeit_debug to "waitall phase" MPI.Waitall(reqs) + + # Perform remote copies for particles received from other ranks + @timeit_debug to "remote replication" begin + for (id, buffer_indices) in remote_copies + if length(buffer_indices) > 1 + source_view = view(buffer, :, buffer_indices[1]) + # TODO: threading in chunks + Threads.@threads for i in 2:length(buffer_indices) + k = buffer_indices[i] + buffer[:, k] .= source_view + end + end + end + end - particles .= buffer + @timeit_debug to "buffer write-back" begin + Threads.@threads for j in 1:size(particles, 2) + # @views creates a non-allocating view of the column, which is faster inside a loop + @views particles[:, j] .= buffer[:, j] + end + end +end +function _determine_sends(resampling_indices::Vector{Int}, my_rank::Int, nprt_per_rank::Int) + sends_to = Dict{Int, Set{Int}}() + for (new_idx, old_id) in enumerate(resampling_indices) + source_rank = floor(Int, (old_id - 1) / nprt_per_rank) + + if source_rank == my_rank + dest_rank = floor(Int, (new_idx - 1) / nprt_per_rank) + if dest_rank != my_rank + unique_ids_for_dest = get!(() -> Set{Int}(), sends_to, dest_rank) + push!(unique_ids_for_dest, old_id) + end + end + end + return sends_to +end + +function _categorize_wants(particles_want::Vector{Int}, my_rank::Int, nprt_per_rank::Int) + local_copies = Dict{Int, Vector{Int}}() + remote_copies = Dict{Int, Vector{Int}}() + + for k in 1:nprt_per_rank + id = particles_want[k] + source_rank = floor(Int, (id - 1) / nprt_per_rank) + dict = source_rank == my_rank ? local_copies : remote_copies + + vec = get!(dict, id) do + Int[] # initialize a new vector only if id not present + end + push!(vec, k) + end + return local_copies, remote_copies end diff --git a/test/integration_test_7.yaml b/test/integration_test_7.yaml new file mode 100644 index 00000000..ac62080f --- /dev/null +++ b/test/integration_test_7.yaml @@ -0,0 +1,15 @@ +filter: + nprt: 10 + optimize_resampling: false + +model: + llw2d: + nx: 51 + ny: 51 + n_stations_x: 4 + n_stations_y: 4 + padding: 0 + +simulate_observations: + seed: 123 + n_time_step: 10 diff --git a/test/runtests.jl b/test/runtests.jl index 5880be6f..9daf1d2a 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -472,11 +472,18 @@ end @test all(resampled_indices .== 5) end +@testset "Optimized resampling unit tests" begin + indices = [1, 3, 5, 5, 5, 5] + n_rank = 3 + optimized_indices = ParticleDA.optimized_resample!(copy(indices), n_rank) + @test optimized_indices == [1, 5, 3, 5, 5, 5] +end + @testset "Integration test -- $(input_file) with $(filter_type) and $(stat_type)" for filter_type in (ParticleDA.BootstrapFilter, ParticleDA.OptimalFilter), stat_type in (ParticleDA.MeanSummaryStat, ParticleDA.MeanAndVarSummaryStat), - input_file in ["integration_test_$i.yaml" for i in 1:6] + input_file in ["integration_test_$i.yaml" for i in 1:7] observation_file_path = tempname() ParticleDA.simulate_observations_from_model( LLW2d.init,