TriangularSolve
Performs some triangular solves. For example:
julia> using TriangularSolve, LinearAlgebra, MKL;
julia> BLAS.set_num_threads(1)
julia> BLAS.get_config().loaded_libs
1-element Vector{LinearAlgebra.BLAS.LBTLibraryInfo}:
LBTLibraryInfo(libmkl_rt.so, ilp64)
julia> N = 100;
julia> A = rand(N,N); B = rand(N,N); C = similar(A);
julia> @benchmark TriangularSolve.rdiv!($C, $A, UpperTriangular($B), Val(false)) # false means single threaded
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 15.909 μs … 41.524 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 17.916 μs ┊ GC (median): 0.00%
Time (mean ± σ): 17.751 μs ± 697.786 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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15.9 μs Histogram: log(frequency) by time 19.9 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> @benchmark rdiv!(copyto!($C, $A), UpperTriangular($B))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 17.578 μs … 75.835 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 19.852 μs ┊ GC (median): 0.00%
Time (mean ± σ): 19.827 μs ± 1.342 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
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17.6 μs Histogram: log(frequency) by time 22.4 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> @benchmark ldiv!($C, LowerTriangular($B), $A)
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 19.102 μs … 69.966 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 21.561 μs ┊ GC (median): 0.00%
Time (mean ± σ): 21.565 μs ± 890.952 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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19.1 μs Histogram: log(frequency) by time 23.4 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> @benchmark TriangularSolve.ldiv!($C, LowerTriangular($B), $A, Val(false)) # false means single threaded
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 19.082 μs … 39.078 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 19.694 μs ┊ GC (median): 0.00%
Time (mean ± σ): 19.765 μs ± 774.848 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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19.1 μs Histogram: frequency by time 22.1 μs <
Memory estimate: 0 bytes, allocs estimate: 0.Multithreaded benchmarks:
julia> BLAS.set_num_threads(min(Threads.nthreads(), TriangularSolve.VectorizationBase.num_cores()))
julia> @benchmark TriangularSolve.rdiv!($C, $A, UpperTriangular($B))
BenchmarkTools.Trial: 10000 samples with 3 evaluations.
Range (min … max): 8.309 μs … 24.357 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 8.769 μs ┊ GC (median): 0.00%
Time (mean ± σ): 8.812 μs ± 382.702 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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8.31 μs Histogram: frequency by time 9.7 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> @benchmark rdiv!(copyto!($C, $A), UpperTriangular($B))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 11.996 μs … 151.147 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 14.163 μs ┊ GC (median): 0.00%
Time (mean ± σ): 14.281 μs ± 2.372 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
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12 μs Histogram: frequency by time 17.3 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> @benchmark TriangularSolve.ldiv!($C, LowerTriangular($B), $A)
BenchmarkTools.Trial: 10000 samples with 5 evaluations.
Range (min … max): 7.903 μs … 22.442 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 9.871 μs ┊ GC (median): 0.00%
Time (mean ± σ): 9.789 μs ± 864.957 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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7.9 μs Histogram: log(frequency) by time 11.8 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> @benchmark ldiv!($C, LowerTriangular($B), $A)
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 13.507 μs … 142.574 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 15.258 μs ┊ GC (median): 0.00%
Time (mean ± σ): 15.319 μs ± 2.045 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
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13.5 μs Histogram: frequency by time 18.5 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> versioninfo()
Julia Version 1.8.0-DEV.438
Commit 88a6376e99* (2021-08-28 11:03 UTC)
Platform Info:
OS: Linux (x86_64-redhat-linux)
CPU: 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-12.0.1 (ORCJIT, tigerlake)
Environment:
JULIA_NUM_THREADS = 8For editing convenience (you can copy/paste the above into a REPL and it should automatically strip julia> s and outputs, but the above is less convenient to edit if you want to try changing the benchmarks):
using TriangularSolve, LinearAlgebra, MKL;
BLAS.set_num_threads(Threads.nthreads())
BLAS.get_config().loaded_libs
N = 100;
A = rand(N,N); B = rand(N,N); C = similar(A);
@benchmark TriangularSolve.rdiv!($C, $A, UpperTriangular($B), Val(false))
@benchmark rdiv!(copyto!($C, $A), UpperTriangular($B))
@benchmark TriangularSolve.ldiv!($C, LowerTriangular($B), $A, Val(false))
@benchmark ldiv!($C, LowerTriangular($B), $A)
BLAS.set_num_threads(TriangularSolve.VectorizationBase.num_cores())
@benchmark TriangularSolve.rdiv!($C, $A, UpperTriangular($B))
@benchmark rdiv!(copyto!($C, $A), UpperTriangular($B))
@benchmark TriangularSolve.ldiv!($C, LowerTriangular($B), $A)
@benchmark ldiv!($C, LowerTriangular($B), $A)
versioninfo()Currently, rdiv! with UpperTriangular and ldiv! with LowerTriangulra matrices are the only supported configurations.