Removing Caveats #17
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Removing Caveats #17
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- 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).
This is not really true. Here is without MKL on my linux machine: And with MKL: |
Yeah, I said less than 5 seconds. For me, 3 seconds is really nothing. The way the Readme is written implies that it takes minutes to load the Julia |
|
I think we can add another caveat instead of current ones, which is losing Python interoperability and the chance of crashing when calling Python libraries caused by #11, JuliaLang/PackageCompiler.jl#154, and JuliaLang/julia#30828 |
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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 #14 and there are no issues.
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:
After restart:
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 https://github.com/JuliaComputing/MKL.jl/issues/1.Tried on Windows and many different installations, only the first boot may take a little longer (less than 20 seconds). The next boots are just the same as normal Julia (less than 5 seconds).