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  • pdf文档 julia 1.10.10

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 1692 页 | 6.34 MB | 3 月前
    3

  • pdf文档 Julia 1.10.9

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 1692 页 | 6.34 MB | 3 月前
    3

  • pdf文档 Julia 1.11.4

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2007 页 | 6.73 MB | 3 月前
    3

  • pdf文档 Julia 1.11.5 Documentation

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2007 页 | 6.73 MB | 3 月前
    3

  • pdf文档 Julia 1.11.6 Release Notes

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2007 页 | 6.73 MB | 3 月前
    3

  • pdf文档 julia 1.13.0 DEV

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2058 页 | 7.45 MB | 3 月前
    3

  • pdf文档 Julia 1.12.0 RC1

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2057 页 | 7.44 MB | 3 月前
    3

  • pdf文档 Julia 1.12.0 Beta4

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2057 页 | 7.44 MB | 3 月前
    3

  • pdf文档 Julia 1.12.0 Beta3

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2057 页 | 7.44 MB | 3 月前
    3

  • pdf文档 julia 1.12.0 beta1

    function compute_dot(DX::Vector{Float64}, DY::Vector{Float64}) @assert length(DX) == length(DY) n = length(DX) incx = incy = 1 product = @ccall "libLAPACK".ddot( n::Ref{Int32}, DX::Ptr{Float64}, incx::Ref{Int32} (u::Vector) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds @simd for i in 1:n #by asserting that `u` is a `Vector` we can assume it has 1-based indexing �→ u[i] = sin(2pi*dx*i) end end function (u::Vector, du) n = length(u) dx = 1.0 / (n-1) @fastmath @inbounds du[1] = (u[2] - u[1]) / dx @fastmath @inbounds @simd for i in 2:n-1 du[i] = (u[i+1] - u[i-1]) / (2*dx) end @fastmath @inbounds du[n]
    0 码力 | 2047 页 | 7.41 MB | 3 月前
    3
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