Improve REPL latency #1
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Thanks for this package. Is there a manual fix for this issue if you have built Julia from source? |
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Would it be just a matter of rerunning the |
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Yes, it is just a matter of running that script during the creation of the sysimage. |
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My apologies but just to make sure that I understand correctly, in some more detail:
I will give it a try asap. |
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No, that is not correct. The |
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We can't do this for binary distributions yet since |
I am still confused after reading JuliaLang/julia#30367: Is it at this point clear what exactly needs to happen, and is it just a matter of e.g. bundling the "generate_precompile.jl" script with the binary? Can't it just be downloaded from github when the package is build? Anyway, if so, is it possible to provide a clear set of instructions what exactly needs to be done ("change this line to that, add that line") by non-expert people (i.e. me) that want to try this out with a julia distro built from source? Or is it still unclear exactly what to do? |
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We could just download it but I'd prefer not to. Even if it was available, I'd have to figure out the correct invocation of PackageCompiler since the function I'm currently using doesn't allow me to add a file when generating the system image. It shouldn't be hard, though. |
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Can use |
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The README says that REPL latency would be fixed with Julia 1.1, is it still an issue? |
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Unfortunately, it is. |
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Is this issue abandoned? |
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I've had no problems with the final suggestion, i.e. install MKL.jl and PackageCompiler.jl, and after building MKL, just run import PackageCompiler
PackageCompiler.build_sysimg(PackageCompiler.default_sysimg_path(), "generate_precompile.jl")You only need to make sure to have the file "generate_precompile.jl" (and execute this command in the directory where the file is located). If you build Julia from source, its in |
- Downstream binary libraries that depend on BLAS such as SuiteSparse (solving sparse linear systems) and ARPACK (for large scale eigenvalue computations) will currently not work once MKL.jl has been installed. We are working on removing these limitations. Tested on JuliaLinearAlgebra#14 and there are no issues. ```julia julia> using LinearAlgebra; BLAS.vendor() :mkl julia> using Arpack, SparseArrays julia> ⊗(A, B) = kron(A, B); julia> function Hub_ED(U, μ, L,) id = [1 0; 0 1] c = [0 0; 1 0] c_up = c ⊗ id c_dn = id ⊗ c id² = id ⊗ id n_up = c_up' * c_up n_dn = c_dn' * c_dn Û = U*(n_up * n_dn) - μ*(n_up + n_dn) c_dg_up(i) = foldl(⊗, sparse.([i==j ? c_up' : id² for j in 1:L])) cup(i) = foldl(⊗, sparse.([i==j ? c_up : id² for j in 1:L])) c_dg_dn(i) = foldl(⊗, sparse.([i==j ? c_dn' : id² for j in 1:L])) cdn(i) = foldl(⊗, sparse.([i==j ? c_dn : id² for j in 1:L])) Ûf(i) = foldl(⊗, sparse.([i==j ? Û : id² for j in 1:L])) function c_dg_c(i) out = c_dg_up(i)*cup(i+1) + c_dg_dn(i)*cdn(i+1) out + out' end H = -sum(c_dg_c, 1:(L-1)) + sum(Ûf, 1:L) @show typeof(H) (λ, ϕ), t = @timed Arpack.eigs(H, nev=1, which=:SR) λ, ϕ, t end Hub_ED (generic function with 1 method) julia> Hub_ED(1, 1, 8) typeof(H) = SparseMatrixCSC{Int64,Int64} ([-15.813000555255263], [-1.949487752371787e-18; -2.0668833887408264e-18; … ; 2.035974575802704e-18; 1.216026630767711e-18], 0.340870967) ``` - It is not possible to revert the effect of install MKL.jl. To return to OpenBLAS, it is necessary to reinstall Julia. Enabling openBlas is possible using `MKL.enable_openblas_startup()` Tested on Windows: ```julia julia> using MKL julia> MKL.enable_openblas_startup() [ Info: Replacing libblas_name in C:\Julia-1.3.0-rc3\bin\..\share\julia\base\build_h.jl ┌ Info: Building `inference.o`: └ `'C:\Julia-1.3.0-rc3\bin\julia.exe' -C native --output-ji 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\basecompiler.ji' --output-o 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\basecompiler.o' compiler/compiler.jl` essentials.jl ctypes.jl generator.jl reflection.jl options.jl promotion.jl tuple.jl pair.jl traits.jl range.jl expr.jl error.jl bool.jl number.jl int.jl operators.jl pointer.jl refvalue.jl indices.jl array.jl abstractarray.jl bitarray.jl bitset.jl abstractdict.jl abstractset.jl iterators.jl namedtuple.jl docs/core.jl ordering.jl sort.jl compiler/utilities.jl compiler/validation.jl compiler/inferenceresult.jl compiler/params.jl compiler/inferencestate.jl compiler/typeutils.jl compiler/typelimits.jl compiler/typelattice.jl compiler/tfuncs.jl compiler/abstractinterpretation.jl compiler/typeinfer.jl compiler/optimize.jl compiler/ssair/driver.jl compiler/ssair/ir.jl compiler/ssair/domtree.jl compiler/ssair/slot2ssa.jl compiler/ssair/queries.jl compiler/ssair/passes.jl compiler/ssair/inlining.jl compiler/ssair/verify.jl compiler/ssair/legacy.jl compiler/bootstrap.jl ┌ Info: Building `sys.o`: └ `'C:\Julia-1.3.0-rc3\bin\julia.exe' -C native --output-ji 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\sys.ji' --output-o 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\sys.o' -J 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\basecompiler.ji' --startup-file=no sysimg.jl` coreio.jl exports.jl essentials.jl ctypes.jl gcutils.jl generator.jl reflection.jl options.jl promotion.jl tuple.jl expr.jl pair.jl traits.jl range.jl error.jl bool.jl number.jl int.jl operators.jl pointer.jl refvalue.jl refpointer.jl checked.jl indices.jl array.jl abstractarray.jl subarray.jl views.jl baseext.jl ntuple.jl abstractdict.jl iterators.jl namedtuple.jl hashing.jl rounding.jl float.jl twiceprecision.jl complex.jl rational.jl multinverses.jl abstractarraymath.jl arraymath.jl simdloop.jl reduce.jl reshapedarray.jl reinterpretarray.jl bitarray.jl bitset.jl multimedia.jl some.jl dict.jl abstractset.jl set.jl char.jl strings/basic.jl strings/string.jl strings/substring.jl build_h.jl version_git.jl osutils.jl c.jl io.jl iobuffer.jl intfuncs.jl strings/strings.jl strings/search.jl strings/unicode.jl strings/util.jl strings/io.jl parse.jl shell.jl regex.jl pcre.jl show.jl arrayshow.jl methodshow.jl cartesian.jl multidimensional.jl permuteddimsarray.jl broadcast.jl missing.jl version.jl sysinfo.jl libc.jl env.jl linked_list.jl condition.jl threads.jl lock.jl task.jl weakkeydict.jl logging.jl libuv.jl uv_constants.jl asyncevent.jl iostream.jl stream.jl filesystem.jl cmd.jl process.jl grisu/grisu.jl secretbuffer.jl floatfuncs.jl math.jl reducedim.jl accumulate.jl ordering.jl sort.jl fastmath.jl Enums.jl gmp.jl mpfr.jl combinatorics.jl hashing2.jl irrationals.jl mathconstants.jl printf.jl meta.jl channels.jl deepcopy.jl download.jl summarysize.jl errorshow.jl stacktraces.jl initdefs.jl threadcall.jl uuid.jl loading.jl util.jl asyncmap.jl experimental.jl deprecated.jl docs\basedocs.jl client.jl docs\Docs.jl Base ─────────── 25.654957 seconds Base64 ───────── 3.794240 seconds CRC32c ───────── 0.010188 seconds SHA ──────────── 0.181228 seconds FileWatching ─── 0.091911 seconds Unicode ──────── 0.010193 seconds Mmap ─────────── 0.074751 seconds Serialization ── 0.862091 seconds Libdl ────────── 0.030937 seconds Markdown ─────── 1.054587 seconds LibGit2 ──────── 2.649440 seconds Logging ──────── 0.506780 seconds Sockets ──────── 1.877789 seconds Printf ───────── 0.012369 seconds Profile ──────── 0.153111 seconds Dates ────────── 2.098834 seconds DelimitedFiles ─ 0.104160 seconds Random ───────── 0.525311 seconds UUIDs ────────── 0.019996 seconds Future ───────── 0.011412 seconds LinearAlgebra ── 12.891522 seconds SparseArrays ─── 6.296322 seconds SuiteSparse ──── 1.802302 seconds Distributed ──── 6.485953 seconds SharedArrays ─── 0.222248 seconds Pkg ──────────── 13.223301 seconds Test ─────────── 0.961266 seconds REPL ─────────── 1.154462 seconds Statistics ───── 0.162380 seconds Stdlibs total ── 57.315055 seconds Sysimage built. Summary: Total ─────── 82.973635 seconds Base: ─────── 25.654957 seconds 30.9194% Stdlibs: ──── 57.315055 seconds 69.0762% Build shared library "C:\\Users\\yahyaaba\\.julia\\packages\\PackageCompiler\\CJQcs\\sysimg\\backup\\native\\sys.dll": `'C:\Users\yahyaaba\.julia\packages\WinRPM\Y9QdZ\deps\usr\x86_64-w64-mingw32\sys-root\mingw\bin\gcc.exe' --sysroot 'C:\Users\yahyaaba\.julia\packages\WinRPM\Y9QdZ\deps\usr\x86_64-w64-mingw32\sys-root' -shared '-DJULIAC_PROGRAM_LIBNAME="C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\sys.dll"' -o 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\sys.dll' -Wl,--whole-archive 'C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\sys.o' -Wl,--no-whole-archive -std=gnu99 '-IC:\Julia-1.3.0-rc3\include\julia' -DJULIA_ENABLE_THREADING=1 '-LC:\Julia-1.3.0-rc3\bin' -Wl,--stack,8388608 -ljulia -lopenlibm -m64 -Wl,--export-all-symbols` [ Info: Copying system image: C:\Users\yahyaaba\.julia\packages\PackageCompiler\CJQcs\sysimg\backup\native\sys.dll to C:\Julia-1.3.0-rc3\lib\julia\sys.dll ``` After restart: ```julia julia> using LinearAlgebra julia> BLAS.vendor() :openblas64 ``` - The current version of MKL.jl doesn't have access to the same precompilation information as the official binaries and source builds so the REPL will have more latency after MKL.jl has been installed. It should be possible to fix this once Julia 1.1 has been released. See JuliaLinearAlgebra#1. Tried on Windows and many different installations, only the first boot may take a little longer (less than 20 seconds). The next boots is just the same as normal Julia (less than 5 seconds).
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I tried @KristofferC 's suggestion to use but it fails on julia 1.0: testing on julia 1.2 daviehh@28faa48 it fails due to missing repl? |
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I think we can finally close this issue 🚀 . |
Currently, the new system image doesn't include the precompile information in
generate_precompile.jl. Once JuliaLang/julia#30367 is included in a Julia release, it should be possible to fix this issue and therefore match the latency of the official binaries.The text was updated successfully, but these errors were encountered: