diff --git a/stdlib/LinearAlgebra/src/blas.jl b/stdlib/LinearAlgebra/src/blas.jl
index e8d44c1ae15339..ed7546ac244ed1 100644
--- a/stdlib/LinearAlgebra/src/blas.jl
+++ b/stdlib/LinearAlgebra/src/blas.jl
@@ -161,12 +161,26 @@ end
 "Check that upper/lower (for special matrices) is correctly specified"
 function chkuplo(uplo::AbstractChar)
     if !(uplo == 'U' || uplo == 'L')
-        throw(ArgumentError("uplo argument must be 'U' (upper) or 'L' (lower), got $uplo"))
+        throw(ArgumentError(lazy"uplo argument must be 'U' (upper) or 'L' (lower), got $uplo"))
     end
     uplo
 end
 
 # Level 1
+# A help function to pick the pointer and inc for 1d like inputs.
+@inline function vec_pointer_stride(x::AbstractArray, stride0check = nothing)
+    isdense(x) && return pointer(x), 1 # simpify runtime check when possibe
+    ndims(x) == 1 || strides(x) == Base.size_to_strides(stride(x, 1), size(x)...) ||
+        throw(ArgumentError("only support vector like inputs"))
+    st = stride(x, 1)
+    isnothing(stride0check) || (st == 0 && throw(stride0check))
+    ptr = st > 0 ? pointer(x) : pointer(x, lastindex(x))
+    ptr, st
+end
+isdense(x) = x isa DenseArray
+isdense(x::Base.FastContiguousSubArray) = isdense(parent(x))
+isdense(x::Base.ReshapedArray) = isdense(parent(x))
+isdense(x::Base.ReinterpretArray) = isdense(parent(x))
 ## copy
 
 """
@@ -257,7 +271,11 @@ for (fname, elty) in ((:dscal_,:Float64),
             DX
         end
 
-        scal!(DA::$elty, DX::AbstractArray{$elty}) = scal!(length(DX),DA,DX,stride(DX,1))
+        function scal!(DA::$elty, DX::AbstractArray{$elty})
+            p, st = vec_pointer_stride(DX, ArgumentError("dest vector with 0 stride is not allowed"))
+            GC.@preserve DX scal!(length(DX), DA, p, abs(st))
+            DX
+        end
     end
 end
 scal(n, DA, DX, incx) = scal!(n, DA, copy(DX), incx)
@@ -361,73 +379,16 @@ for (fname, elty) in ((:cblas_zdotu_sub,:ComplexF64),
     end
 end
 
-@inline function _dot_length_check(x,y)
-    n = length(x)
-    if n != length(y)
-        throw(DimensionMismatch("dot product arguments have lengths $(length(x)) and $(length(y))"))
-    end
-    n
-end
-
 for (elty, f) in ((Float32, :dot), (Float64, :dot),
                   (ComplexF32, :dotc), (ComplexF64, :dotc),
                   (ComplexF32, :dotu), (ComplexF64, :dotu))
     @eval begin
-        function $f(x::DenseArray{$elty}, y::DenseArray{$elty})
-            n = _dot_length_check(x,y)
-            $f(n, x, 1, y, 1)
-        end
-
-        function $f(x::StridedVector{$elty}, y::DenseArray{$elty})
-            n = _dot_length_check(x,y)
-            xstride = stride(x,1)
-            ystride = stride(y,1)
-            x_delta = xstride < 0 ? n : 1
-            GC.@preserve x $f(n, pointer(x, x_delta), xstride, y, ystride)
+        function $f(x::AbstractArray{$elty}, y::AbstractArray{$elty})
+            n, m = length(x), length(y)
+            n == m || throw(DimensionMismatch(lazy"dot product arguments have lengths $n and $m"))
+            GC.@preserve x y $f(n, vec_pointer_stride(x)..., vec_pointer_stride(y)...)
         end
-
-        function $f(x::DenseArray{$elty}, y::StridedVector{$elty})
-            n = _dot_length_check(x,y)
-            xstride = stride(x,1)
-            ystride = stride(y,1)
-            y_delta = ystride < 0 ? n : 1
-            GC.@preserve y $f(n, x, xstride, pointer(y, y_delta), ystride)
-        end
-
-        function $f(x::StridedVector{$elty}, y::StridedVector{$elty})
-            n = _dot_length_check(x,y)
-            xstride = stride(x,1)
-            ystride = stride(y,1)
-            x_delta = xstride < 0 ? n : 1
-            y_delta = ystride < 0 ? n : 1
-            GC.@preserve x y $f(n, pointer(x, x_delta), xstride, pointer(y, y_delta), ystride)
-        end
-    end
-end
-
-function dot(DX::Union{DenseArray{T},AbstractVector{T}}, DY::Union{DenseArray{T},AbstractVector{T}}) where T<:BlasReal
-    require_one_based_indexing(DX, DY)
-    n = length(DX)
-    if n != length(DY)
-        throw(DimensionMismatch(lazy"dot product arguments have lengths $(length(DX)) and $(length(DY))"))
-    end
-    return dot(n, DX, stride(DX, 1), DY, stride(DY, 1))
-end
-function dotc(DX::Union{DenseArray{T},AbstractVector{T}}, DY::Union{DenseArray{T},AbstractVector{T}}) where T<:BlasComplex
-    require_one_based_indexing(DX, DY)
-    n = length(DX)
-    if n != length(DY)
-        throw(DimensionMismatch(lazy"dot product arguments have lengths $(length(DX)) and $(length(DY))"))
     end
-    return dotc(n, DX, stride(DX, 1), DY, stride(DY, 1))
-end
-function dotu(DX::Union{DenseArray{T},AbstractVector{T}}, DY::Union{DenseArray{T},AbstractVector{T}}) where T<:BlasComplex
-    require_one_based_indexing(DX, DY)
-    n = length(DX)
-    if n != length(DY)
-        throw(DimensionMismatch(lazy"dot product arguments have lengths $(length(DX)) and $(length(DY))"))
-    end
-    return dotu(n, DX, stride(DX, 1), DY, stride(DY, 1))
 end
 
 ## nrm2
@@ -461,7 +422,11 @@ for (fname, elty, ret_type) in ((:dnrm2_,:Float64,:Float64),
         end
     end
 end
-nrm2(x::Union{AbstractVector,DenseArray}) = nrm2(length(x), x, stride1(x))
+# openblas returns 0 for negative stride
+function nrm2(x::AbstractArray)
+    p, st = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+    GC.@preserve x nrm2(length(x), p, abs(st))
+end
 
 ## asum
 
@@ -498,7 +463,10 @@ for (fname, elty, ret_type) in ((:dasum_,:Float64,:Float64),
         end
     end
 end
-asum(x::Union{AbstractVector,DenseArray}) = asum(length(x), x, stride1(x))
+function asum(x::AbstractArray)
+    p, st = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+    GC.@preserve x asum(length(x), p, abs(st))
+end
 
 ## axpy
 
@@ -542,15 +510,17 @@ for (fname, elty) in ((:daxpy_,:Float64),
         end
     end
 end
-function axpy!(alpha::Number, x::Union{DenseArray{T},StridedVector{T}}, y::Union{DenseArray{T},StridedVector{T}}) where T<:BlasFloat
+function axpy!(alpha::Number, x::AbstractArray{T}, y::AbstractArray{T}) where T<:BlasFloat
     if length(x) != length(y)
         throw(DimensionMismatch(lazy"x has length $(length(x)), but y has length $(length(y))"))
     end
-    return axpy!(length(x), convert(T,alpha), x, stride(x, 1), y, stride(y, 1))
+    GC.@preserve x y axpy!(length(x), T(alpha), vec_pointer_stride(x)...,
+        vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))...)
+    y
 end
 
-function axpy!(alpha::Number, x::Array{T}, rx::Union{UnitRange{Ti},AbstractRange{Ti}},
-               y::Array{T}, ry::Union{UnitRange{Ti},AbstractRange{Ti}}) where {T<:BlasFloat,Ti<:Integer}
+function axpy!(alpha::Number, x::Array{T}, rx::AbstractRange{Ti},
+               y::Array{T}, ry::AbstractRange{Ti}) where {T<:BlasFloat,Ti<:Integer}
     if length(rx) != length(ry)
         throw(DimensionMismatch("ranges of differing lengths"))
     end
@@ -562,10 +532,10 @@ function axpy!(alpha::Number, x::Array{T}, rx::Union{UnitRange{Ti},AbstractRange
     end
     GC.@preserve x y axpy!(
         length(rx),
-        convert(T, alpha),
-        pointer(x) + (first(rx) - 1)*sizeof(T),
+        T(alpha),
+        pointer(x, minimum(rx)),
         step(rx),
-        pointer(y) + (first(ry) - 1)*sizeof(T),
+        pointer(y, minimum(ry)),
         step(ry))
 
     return y
@@ -612,12 +582,14 @@ for (fname, elty) in ((:daxpby_,:Float64), (:saxpby_,:Float32),
     end
 end
 
-function axpby!(alpha::Number, x::Union{DenseArray{T},AbstractVector{T}}, beta::Number, y::Union{DenseArray{T},AbstractVector{T}}) where T<:BlasFloat
+function axpby!(alpha::Number, x::AbstractArray{T}, beta::Number, y::AbstractArray{T}) where T<:BlasFloat
     require_one_based_indexing(x, y)
     if length(x) != length(y)
         throw(DimensionMismatch(lazy"x has length $(length(x)), but y has length $(length(y))"))
     end
-    return axpby!(length(x), convert(T, alpha), x, stride(x, 1), convert(T, beta), y, stride(y, 1))
+    GC.@preserve x y axpby!(length(x), T(alpha), vec_pointer_stride(x)..., T(beta),
+        vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))...)
+    y
 end
 
 ## iamax
@@ -633,7 +605,11 @@ for (fname, elty) in ((:idamax_,:Float64),
         end
     end
 end
-iamax(dx::Union{AbstractVector,DenseArray}) = iamax(length(dx), dx, stride1(dx))
+function iamax(dx::AbstractArray)
+    p, st = vec_pointer_stride(dx)
+    st <= 0 && return BlasInt(0)
+    iamax(length(dx), p, st)
+end
 
 """
     iamax(n, dx, incx)
@@ -673,20 +649,16 @@ for (fname, elty) in ((:dgemv_,:Float64),
             end
             chkstride1(A)
             lda = stride(A,2)
-            sX = stride(X,1)
-            sY = stride(Y,1)
+            pX, sX = vec_pointer_stride(X, ArgumentError("input vector with 0 stride is not allowed"))
+            pY, sY = vec_pointer_stride(Y, ArgumentError("dest vector with 0 stride is not allowed"))
+            pA = pointer(A)
             if lda < 0
-                colindex = lastindex(A, 2)
+                pA += (size(A, 2) - 1) * lda * sizeof($elty)
                 lda = -lda
                 trans == 'N' ? (sX = -sX) : (sY = -sY)
-            else
-                colindex = firstindex(A, 2)
             end
             lda >= size(A,1) || size(A,2) <= 1 || error("when `size(A,2) > 1`, `abs(stride(A,2))` must be at least `size(A,1)`")
             lda = max(1, size(A,1), lda)
-            pA = pointer(A, Base._sub2ind(A, 1, colindex))
-            pX = pointer(X, stride(X,1) > 0 ? firstindex(X) : lastindex(X))
-            pY = pointer(Y, stride(Y,1) > 0 ? firstindex(Y) : lastindex(Y))
             GC.@preserve A X Y ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ref{$elty},
                  Ptr{$elty}, Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt},
@@ -767,14 +739,16 @@ for (fname, elty) in ((:dgbmv_,:Float64),
                        y::AbstractVector{$elty})
             require_one_based_indexing(A, x, y)
             chkstride1(A)
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+            GC.@preserve x y ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ref{BlasInt},
                  Ref{BlasInt}, Ref{$elty}, Ptr{$elty}, Ref{BlasInt},
                  Ptr{$elty}, Ref{BlasInt}, Ref{$elty}, Ptr{$elty},
                  Ref{BlasInt}, Clong),
                  trans, m, size(A,2), kl,
                  ku, alpha, A, max(1,stride(A,2)),
-                 x, stride(x,1), beta, y, stride(y,1), 1)
+                 px, stx, beta, py, sty, 1)
             y
         end
         function gbmv(trans::AbstractChar, m::Integer, kl::Integer, ku::Integer, alpha::($elty), A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -828,13 +802,15 @@ for (fname, elty, lib) in ((:dsymv_,:Float64,libblastrampoline),
                 throw(DimensionMismatch(lazy"A has size $(size(A)), and y has length $(length(y))"))
             end
             chkstride1(A)
-            ccall((@blasfunc($fname), $lib), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+            GC.@preserve x y ccall((@blasfunc($fname), $lib), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{$elty}, Ptr{$elty},
                  Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt}, Ref{$elty},
                  Ptr{$elty}, Ref{BlasInt}, Clong),
                  uplo, n, alpha, A,
-                 max(1,stride(A,2)), x, stride(x,1), beta,
-                 y, stride(y,1), 1)
+                 max(1,stride(A,2)), px, stx, beta,
+                 py, sty, 1)
             y
         end
         function symv(uplo::AbstractChar, alpha::($elty), A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -891,15 +867,15 @@ for (fname, elty) in ((:zhemv_,:ComplexF64),
             end
             chkstride1(A)
             lda = max(1, stride(A, 2))
-            incx = stride(x, 1)
-            incy = stride(y, 1)
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+            GC.@preserve x y ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{$elty}, Ptr{$elty},
                  Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt}, Ref{$elty},
                  Ptr{$elty}, Ref{BlasInt}, Clong),
                 uplo, n, α, A,
-                lda, x, incx, β,
-                y, incy, 1)
+                lda, px, stx, β,
+                py, sty, 1)
             y
         end
         function hemv(uplo::AbstractChar, α::($elty), A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -977,19 +953,21 @@ for (fname, elty) in ((:zhpmv_, :ComplexF64),
 end
 
 function hpmv!(uplo::AbstractChar,
-               α::Number, AP::Union{DenseArray{T}, AbstractVector{T}}, x::Union{DenseArray{T}, AbstractVector{T}},
-               β::Number, y::Union{DenseArray{T}, AbstractVector{T}}) where {T <: BlasComplex}
-    chkuplo(uplo)
+               α::Number, AP::AbstractArray{T}, x::AbstractArray{T},
+               β::Number, y::AbstractArray{T}) where {T <: BlasComplex}
     require_one_based_indexing(AP, x, y)
     N = length(x)
     if N != length(y)
-        throw(DimensionMismatch("x has length $(N), but y has length $(length(y))"))
+        throw(DimensionMismatch(lazy"x has length $(N), but y has length $(length(y))"))
     end
     if 2*length(AP) < N*(N + 1)
-        throw(DimensionMismatch("Packed Hermitian matrix A has size smaller than length(x) =  $(N)."))
+        throw(DimensionMismatch(lazy"Packed hermitian matrix A has size smaller than length(x) = $(N)."))
     end
     chkstride1(AP)
-    return hpmv!(uplo, N, convert(T, α), AP, x, stride(x, 1), convert(T, β), y, stride(y, 1))
+    px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+    py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+    GC.@preserve x y hpmv!(uplo, N, T(α), AP, px, stx, T(β), py, sty)
+    y
 end
 
 """
@@ -1031,13 +1009,15 @@ for (fname, elty) in ((:dsbmv_,:Float64),
             chkuplo(uplo)
             require_one_based_indexing(A, x, y)
             chkstride1(A)
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+            GC.@preserve x y ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ref{$elty},
                  Ptr{$elty}, Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt},
                  Ref{$elty}, Ptr{$elty}, Ref{BlasInt}, Clong),
                  uplo, size(A,2), k, alpha,
-                 A, max(1,stride(A,2)), x, stride(x,1),
-                 beta, y, stride(y,1), 1)
+                 A, max(1,stride(A,2)), px, stx,
+                 beta, py, sty, 1)
             y
         end
         function sbmv(uplo::AbstractChar, k::Integer, alpha::($elty), A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -1130,19 +1110,21 @@ for (fname, elty) in ((:dspmv_, :Float64),
 end
 
 function spmv!(uplo::AbstractChar,
-               α::Real, AP::Union{DenseArray{T}, AbstractVector{T}}, x::Union{DenseArray{T}, AbstractVector{T}},
-               β::Real, y::Union{DenseArray{T}, AbstractVector{T}}) where {T <: BlasReal}
-    chkuplo(uplo)
+               α::Real, AP::AbstractArray{T}, x::AbstractArray{T},
+               β::Real, y::AbstractArray{T}) where {T <: BlasReal}
     require_one_based_indexing(AP, x, y)
     N = length(x)
     if N != length(y)
-        throw(DimensionMismatch("x has length $(N), but y has length $(length(y))"))
+        throw(DimensionMismatch(lazy"x has length $(N), but y has length $(length(y))"))
     end
     if 2*length(AP) < N*(N + 1)
-        throw(DimensionMismatch("Packed symmetric matrix A has size smaller than length(x) = $(N)."))
+        throw(DimensionMismatch(lazy"Packed symmetric matrix A has size smaller than length(x) = $(N)."))
     end
     chkstride1(AP)
-    return spmv!(uplo, N, convert(T, α), AP, x, stride(x, 1), convert(T, β), y, stride(y, 1))
+    px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+    py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+    GC.@preserve x y spmv!(uplo, N, T(α), AP, px, stx, T(β), py, sty)
+    y
 end
 
 """
@@ -1201,16 +1183,17 @@ for (fname, elty) in ((:dspr_, :Float64),
 end
 
 function spr!(uplo::AbstractChar,
-              α::Real, x::Union{DenseArray{T}, AbstractVector{T}},
-              AP::Union{DenseArray{T}, AbstractVector{T}}) where {T <: BlasReal}
+              α::Real, x::AbstractArray{T},
+              AP::AbstractArray{T}) where {T <: BlasReal}
     chkuplo(uplo)
     require_one_based_indexing(AP, x)
     N = length(x)
     if 2*length(AP) < N*(N + 1)
-        throw(DimensionMismatch("Packed symmetric matrix A has size smaller than length(x) = $(N)."))
+        throw(DimensionMismatch(lazy"Packed symmetric matrix A has size smaller than length(x) = $(N)."))
     end
     chkstride1(AP)
-    return spr!(uplo, N, convert(T, α), x, stride(x, 1), AP)
+    px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+    return GC.@preserve x spr!(uplo, N, T(α), px, stx , AP)
 end
 
 """
@@ -1251,13 +1234,15 @@ for (fname, elty) in ((:zhbmv_,:ComplexF64),
             chkuplo(uplo)
             require_one_based_indexing(A, x, y)
             chkstride1(A)
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            py, sty = vec_pointer_stride(y, ArgumentError("dest vector with 0 stride is not allowed"))
+            GC.@preserve x y ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ref{$elty},
                  Ptr{$elty}, Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt},
                  Ref{$elty}, Ptr{$elty}, Ref{BlasInt}, Clong),
                  uplo, size(A,2), k, alpha,
-                 A, max(1,stride(A,2)), x, stride(x,1),
-                 beta, y, stride(y,1), 1)
+                 A, max(1,stride(A,2)), px, stx,
+                 beta, py, sty, 1)
             y
         end
         function hbmv(uplo::AbstractChar, k::Integer, alpha::($elty), A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -1312,12 +1297,13 @@ for (fname, elty) in ((:dtrmv_,:Float64),
                 throw(DimensionMismatch(lazy"A has size ($n,$n), x has length $(length(x))"))
             end
             chkstride1(A)
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            GC.@preserve x ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{UInt8}, Ref{UInt8}, Ref{BlasInt},
                  Ptr{$elty}, Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt},
                  Clong, Clong, Clong),
                  uplo, trans, diag, n,
-                 A, max(1,stride(A,2)), x, max(1,stride(x, 1)), 1, 1, 1)
+                 A, max(1,stride(A,2)), px, stx, 1, 1, 1)
             x
         end
         function trmv(uplo::AbstractChar, trans::AbstractChar, diag::AbstractChar, A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -1368,12 +1354,13 @@ for (fname, elty) in ((:dtrsv_,:Float64),
                 throw(DimensionMismatch(lazy"size of A is $n != length(x) = $(length(x))"))
             end
             chkstride1(A)
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            GC.@preserve x ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{UInt8}, Ref{UInt8}, Ref{BlasInt},
                  Ptr{$elty}, Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt},
                  Clong, Clong, Clong),
                  uplo, trans, diag, n,
-                 A, max(1,stride(A,2)), x, stride(x, 1), 1, 1, 1)
+                 A, max(1,stride(A,2)), px, stx, 1, 1, 1)
             x
         end
         function trsv(uplo::AbstractChar, trans::AbstractChar, diag::AbstractChar, A::AbstractMatrix{$elty}, x::AbstractVector{$elty})
@@ -1402,13 +1389,13 @@ for (fname, elty) in ((:dger_,:Float64),
             if m != length(x) || n != length(y)
                 throw(DimensionMismatch(lazy"A has size ($m,$n), x has length $(length(x)), y has length $(length(y))"))
             end
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            py, sty = vec_pointer_stride(y, ArgumentError("input vector with 0 stride is not allowed"))
+            GC.@preserve x y ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{BlasInt}, Ref{BlasInt}, Ref{$elty}, Ptr{$elty},
                  Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt}, Ptr{$elty},
                  Ref{BlasInt}),
-                 m, n, α, x,
-                 stride(x, 1), y, stride(y, 1), A,
-                 max(1,stride(A,2)))
+                 m, n, α, px, stx, py, sty, A, max(1,stride(A,2)))
             A
         end
     end
@@ -1436,11 +1423,11 @@ for (fname, elty, lib) in ((:dsyr_,:Float64,libblastrampoline),
             if length(x) != n
                 throw(DimensionMismatch(lazy"A has size ($n,$n), x has length $(length(x))"))
             end
-            ccall((@blasfunc($fname), $lib), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            GC.@preserve x ccall((@blasfunc($fname), $lib), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{$elty}, Ptr{$elty},
                  Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt}),
-                 uplo, n, α, x,
-                 stride(x, 1), A, max(1,stride(A, 2)))
+                 uplo, n, α, px, stx, A, max(1,stride(A, 2)))
             A
         end
     end
@@ -1467,11 +1454,11 @@ for (fname, elty, relty) in ((:zher_,:ComplexF64, :Float64),
             if length(x) != n
                 throw(DimensionMismatch(lazy"A has size ($n,$n), x has length $(length(x))"))
             end
-            ccall((@blasfunc($fname), libblastrampoline), Cvoid,
+            px, stx = vec_pointer_stride(x, ArgumentError("input vector with 0 stride is not allowed"))
+            GC.@preserve x ccall((@blasfunc($fname), libblastrampoline), Cvoid,
                 (Ref{UInt8}, Ref{BlasInt}, Ref{$relty}, Ptr{$elty},
                  Ref{BlasInt}, Ptr{$elty}, Ref{BlasInt}, Clong),
-                 uplo, n, α, x,
-                 stride(x, 1), A, max(1,stride(A,2)), 1)
+                 uplo, n, α, px, stx, A, max(1,stride(A,2)), 1)
             A
         end
     end
@@ -2085,8 +2072,8 @@ end
 
 end # module
 
-function copyto!(dest::Array{T}, rdest::Union{UnitRange{Ti},AbstractRange{Ti}},
-                 src::Array{T}, rsrc::Union{UnitRange{Ti},AbstractRange{Ti}}) where {T<:BlasFloat,Ti<:Integer}
+function copyto!(dest::Array{T}, rdest::AbstractRange{Ti},
+                 src::Array{T}, rsrc::AbstractRange{Ti}) where {T<:BlasFloat,Ti<:Integer}
     if minimum(rdest) < 1 || maximum(rdest) > length(dest)
         throw(ArgumentError(lazy"range out of bounds for dest, of length $(length(dest))"))
     end
@@ -2098,9 +2085,9 @@ function copyto!(dest::Array{T}, rdest::Union{UnitRange{Ti},AbstractRange{Ti}},
     end
     GC.@preserve src dest BLAS.blascopy!(
         length(rsrc),
-        pointer(src) + (first(rsrc) - 1) * sizeof(T),
+        pointer(src, minimum(rsrc)),
         step(rsrc),
-        pointer(dest) + (first(rdest) - 1) * sizeof(T),
+        pointer(dest, minimum(rdest)),
         step(rdest))
 
     return dest
diff --git a/stdlib/LinearAlgebra/src/matmul.jl b/stdlib/LinearAlgebra/src/matmul.jl
index f27a3a768b8669..80d9872fdca6ec 100644
--- a/stdlib/LinearAlgebra/src/matmul.jl
+++ b/stdlib/LinearAlgebra/src/matmul.jl
@@ -498,7 +498,8 @@ function gemv!(y::StridedVector{T}, tA::AbstractChar, A::StridedVecOrMat{T}, x::
     nA == 0 && return _rmul_or_fill!(y, β)
     alpha, beta = promote(α, β, zero(T))
     if alpha isa Union{Bool,T} && beta isa Union{Bool,T} &&
-        stride(A, 1) == 1 && stride(A, 2) >= size(A, 1)
+        stride(A, 1) == 1 && abs(stride(A, 2)) >= size(A, 1) &&
+        !iszero(stride(x, 1)) # We only check input's stride here.
         return BLAS.gemv!(tA, alpha, A, x, beta, y)
     else
         return generic_matvecmul!(y, tA, A, x, MulAddMul(α, β))
@@ -516,8 +517,9 @@ function gemv!(y::StridedVector{Complex{T}}, tA::AbstractChar, A::StridedVecOrMa
     nA == 0 && return _rmul_or_fill!(y, β)
     alpha, beta = promote(α, β, zero(T))
     if alpha isa Union{Bool,T} && beta isa Union{Bool,T} &&
-        stride(A, 1) == 1 && stride(A, 2) >= size(A, 1) &&
-        stride(y, 1) == 1 && tA == 'N' # reinterpret-based optimization is valid only for contiguous `y`
+        stride(A, 1) == 1 && abs(stride(A, 2)) >= size(A, 1) &&
+        stride(y, 1) == 1 && tA == 'N' && # reinterpret-based optimization is valid only for contiguous `y`
+        !iszero(stride(x, 1))
         BLAS.gemv!(tA, alpha, reinterpret(T, A), x, beta, reinterpret(T, y))
         return y
     else
@@ -535,7 +537,9 @@ function gemv!(y::StridedVector{Complex{T}}, tA::AbstractChar, A::StridedVecOrMa
     mA == 0 && return y
     nA == 0 && return _rmul_or_fill!(y, β)
     alpha, beta = promote(α, β, zero(T))
-    @views if alpha isa Union{Bool,T} && beta isa Union{Bool,T} && stride(A, 1) == 1 && stride(A, 2) >= size(A, 1)
+    @views if alpha isa Union{Bool,T} && beta isa Union{Bool,T} &&
+        stride(A, 1) == 1 && abs(stride(A, 2)) >= size(A, 1) &&
+        !iszero(stride(x, 1))
         xfl = reinterpret(reshape, T, x) # Use reshape here.
         yfl = reinterpret(reshape, T, y)
         BLAS.gemv!(tA, alpha, A, xfl[1, :], beta, yfl[1, :])
diff --git a/stdlib/LinearAlgebra/test/blas.jl b/stdlib/LinearAlgebra/test/blas.jl
index 54b227bca76853..d39f7c45ba205e 100644
--- a/stdlib/LinearAlgebra/test/blas.jl
+++ b/stdlib/LinearAlgebra/test/blas.jl
@@ -4,20 +4,31 @@ module TestBLAS
 
 using Test, LinearAlgebra, Random
 using LinearAlgebra: BlasReal, BlasComplex
+fabs(x::Real) = abs(x)
+fabs(x::Complex) = abs(real(x)) + abs(imag(x))
+
+# help function to build packed storage
+function pack(A, uplo)
+    AP = eltype(A)[]
+    n = size(A, 1)
+    for j in 1:n, i in (uplo==:L ? (j:n) : (1:j))
+        push!(AP, A[i,j])
+    end
+    return AP
+end
 
+@testset "vec_pointer_stride" begin
+    a = zeros(4,4,4)
+    @test BLAS.asum(view(a,1:2:4,:,:)) == 0 # vector like
+    @test_throws ArgumentError BLAS.asum(view(a,1:3:4,:,:)) # non-vector like
+end
 Random.seed!(100)
 ## BLAS tests - testing the interface code to BLAS routines
 @testset for elty in [Float32, Float64, ComplexF32, ComplexF64]
 
     @testset "syr2k!" begin
-        U = randn(5,2)
-        V = randn(5,2)
-        if elty == ComplexF32 || elty == ComplexF64
-            U = complex.(U, U)
-            V = complex.(V, V)
-        end
-        U = convert(Array{elty, 2}, U)
-        V = convert(Array{elty, 2}, V)
+        U = randn(elty, 5, 2)
+        V = randn(elty, 5, 2)
         @test tril(LinearAlgebra.BLAS.syr2k('L','N',U,V)) ≈ tril(U*transpose(V) + V*transpose(U))
         @test triu(LinearAlgebra.BLAS.syr2k('U','N',U,V)) ≈ triu(U*transpose(V) + V*transpose(U))
         @test tril(LinearAlgebra.BLAS.syr2k('L','T',U,V)) ≈ tril(transpose(U)*V + transpose(V)*U)
@@ -26,12 +37,8 @@ Random.seed!(100)
 
     if elty in (ComplexF32, ComplexF64)
         @testset "her2k!" begin
-            U = randn(5,2)
-            V = randn(5,2)
-            U = complex.(U, U)
-            V = complex.(V, V)
-            U = convert(Array{elty, 2}, U)
-            V = convert(Array{elty, 2}, V)
+            U = randn(elty, 5, 2)
+            V = randn(elty, 5, 2)
             @test tril(LinearAlgebra.BLAS.her2k('L','N',U,V)) ≈ tril(U*V' + V*U')
             @test triu(LinearAlgebra.BLAS.her2k('U','N',U,V)) ≈ triu(U*V' + V*U')
             @test tril(LinearAlgebra.BLAS.her2k('L','C',U,V)) ≈ tril(U'*V + V'*U)
@@ -48,21 +55,21 @@ Random.seed!(100)
     U4 = triu(fill(elty(1), 4,4))
     Z4 = zeros(elty, (4,4))
 
-    elm1 = convert(elty, -1)
-    el2 = convert(elty, 2)
-    v14 = convert(Vector{elty}, [1:4;])
-    v41 = convert(Vector{elty}, [4:-1:1;])
+    elm1 = elty(-1)
+    el2 = elty(2)
+    v14 = elty[1:4;]
+    v41 = elty[4:-1:1;]
 
     let n = 10
         @testset "dot products" begin
             if elty <: Real
-                x1 = convert(Vector{elty}, randn(n))
-                x2 = convert(Vector{elty}, randn(n))
+                x1 = randn(elty, n)
+                x2 = randn(elty, n)
                 @test BLAS.dot(x1,x2) ≈ sum(x1.*x2)
                 @test_throws DimensionMismatch BLAS.dot(x1,rand(elty, n + 1))
             else
-                z1 = convert(Vector{elty}, complex.(randn(n),randn(n)))
-                z2 = convert(Vector{elty}, complex.(randn(n),randn(n)))
+                z1 = randn(elty, n)
+                z2 = randn(elty, n)
                 @test BLAS.dotc(z1,z2) ≈ sum(conj(z1).*z2)
                 @test BLAS.dotu(z1,z2) ≈ sum(z1.*z2)
                 @test_throws DimensionMismatch BLAS.dotc(z1,rand(elty, n + 1))
@@ -70,92 +77,60 @@ Random.seed!(100)
             end
         end
         @testset "iamax" begin
-            if elty <: Real
-                x = convert(Vector{elty}, randn(n))
-                @test BLAS.iamax(x) == argmax(abs.(x))
-            else
-                z = convert(Vector{elty}, complex.(randn(n),randn(n)))
-                @test BLAS.iamax(z) == argmax(map(x -> abs(real(x)) + abs(imag(x)), z))
-            end
+            x = randn(elty, n)
+            @test BLAS.iamax(x) == findmax(fabs, x)[2]
         end
         @testset "rot!" begin
-            if elty <: Real
-                x = convert(Vector{elty}, randn(n))
-                y = convert(Vector{elty}, randn(n))
-                c = rand(elty)
-                s = rand(elty)
+            x = randn(elty, n)
+            y = randn(elty, n)
+            c = rand(real(elty))
+            for sty in unique!([real(elty), elty])
+                s = rand(sty)
                 x2 = copy(x)
                 y2 = copy(y)
                 BLAS.rot!(n, x, 1, y, 1, c, s)
                 @test x ≈ c*x2 + s*y2
-                @test y ≈ -s*x2 + c*y2
-            else
-                x = convert(Vector{elty}, complex.(randn(n),rand(n)))
-                y = convert(Vector{elty}, complex.(randn(n),rand(n)))
-                cty = (elty == ComplexF32) ? Float32 : Float64
-                c = rand(cty)
-                for sty in [cty, elty]
-                    s = rand(sty)
-                    x2 = copy(x)
-                    y2 = copy(y)
-                    BLAS.rot!(n, x, 1, y, 1, c, s)
-                    @test x ≈ c*x2 + s*y2
-                    @test y ≈ -conj(s)*x2 + c*y2
-                end
+                @test y ≈ -conj(s)*x2 + c*y2
             end
         end
         @testset "axp(b)y" begin
-            if elty <: Real
-                x1 = convert(Vector{elty}, randn(n))
-                x2 = convert(Vector{elty}, randn(n))
-                α  = rand(elty)
-                β  = rand(elty)
-                @test BLAS.axpy!(α,copy(x1),copy(x2)) ≈ α*x1 + x2
-                @test BLAS.axpby!(α,copy(x1),β,copy(x2)) ≈ α*x1 + β*x2
-                @test_throws DimensionMismatch BLAS.axpy!(α, copy(x1), rand(elty, n + 1))
-                @test_throws DimensionMismatch BLAS.axpby!(α, copy(x1), β, rand(elty, n + 1))
-                @test_throws DimensionMismatch BLAS.axpy!(α, copy(x1), 1:div(n,2), copy(x2), 1:n)
-                @test_throws ArgumentError BLAS.axpy!(α, copy(x1), 0:div(n,2), copy(x2), 1:(div(n, 2) + 1))
-                @test_throws ArgumentError BLAS.axpy!(α, copy(x1), 1:div(n,2), copy(x2), 0:(div(n, 2) - 1))
-                @test BLAS.axpy!(α,copy(x1),1:n,copy(x2),1:n) ≈ x2 + α*x1
-            else
-                z1 = convert(Vector{elty}, complex.(randn(n), randn(n)))
-                z2 = convert(Vector{elty}, complex.(randn(n), randn(n)))
-                α  = rand(elty)
-                @test BLAS.axpy!(α, copy(z1), copy(z2)) ≈ z2 + α * z1
-                @test_throws DimensionMismatch BLAS.axpy!(α, copy(z1), rand(elty, n + 1))
-                @test_throws DimensionMismatch BLAS.axpy!(α, copy(z1), 1:div(n, 2), copy(z2), 1:(div(n, 2) + 1))
-                @test_throws ArgumentError BLAS.axpy!(α, copy(z1), 0:div(n,2), copy(z2), 1:(div(n, 2) + 1))
-                @test_throws ArgumentError BLAS.axpy!(α, copy(z1), 1:div(n,2), copy(z2), 0:(div(n, 2) - 1))
-                @test BLAS.axpy!(α,copy(z1),1:n,copy(z2),1:n) ≈ z2 + α*z1
+            x1 = randn(elty, n)
+            x2 = randn(elty, n)
+            α  = rand(elty)
+            β  = rand(elty)
+            for X1 in (x1, view(x1,n:-1:1)), X2 in (x2, view(x2, n:-1:1))
+                @test BLAS.axpy!(α,deepcopy(X1),deepcopy(X2)) ≈ α*X1 + X2
+                @test BLAS.axpby!(α,deepcopy(X1),β,deepcopy(X2)) ≈ α*X1 + β*X2
             end
+            for ind1 in (1:n, n:-1:1), ind2 in (1:n, n:-1:1)
+                @test BLAS.axpy!(α,copy(x1),ind1,copy(x2),ind2) ≈ x2 + α*(ind1 == ind2 ? x1 : reverse(x1))
+            end
+            @test_throws DimensionMismatch BLAS.axpy!(α, copy(x1), rand(elty, n + 1))
+            @test_throws DimensionMismatch BLAS.axpby!(α, copy(x1), β, rand(elty, n + 1))
+            @test_throws DimensionMismatch BLAS.axpy!(α, copy(x1), 1:div(n,2), copy(x2), 1:n)
+            @test_throws ArgumentError BLAS.axpy!(α, copy(x1), 0:div(n,2), copy(x2), 1:(div(n, 2) + 1))
+            @test_throws ArgumentError BLAS.axpy!(α, copy(x1), 1:div(n,2), copy(x2), 0:(div(n, 2) - 1))
         end
         @testset "nrm2, iamax, and asum for StridedVectors" begin
             a = rand(elty,n)
-            b = view(a,2:2:n,1)
-            @test BLAS.nrm2(b) ≈ norm(b)
-            if elty <: Real
-                @test BLAS.asum(b) ≈ sum(abs.(b))
-                @test BLAS.iamax(b) ≈ argmax(abs.(b))
-            else
-                @test BLAS.asum(b) ≈ sum(abs.(real(b))) + sum(abs.(imag(b)))
-                @test BLAS.iamax(b) == argmax(map(x -> abs(real(x)) + abs(imag(x)), b))
+            for ind in (2:2:n, n:-2:2)
+                b = view(a, ind, 1)
+                @test BLAS.nrm2(b) ≈ sqrt(sum(abs2, b))
+                @test BLAS.asum(b) ≈ sum(fabs, b)
+                @test BLAS.iamax(b) == findmax(fabs, b)[2] * (step(ind) >= 0)
             end
         end
-        # scal
-        α = rand(elty)
-        a = rand(elty,n)
-        @test BLAS.scal(n,α,a,1) ≈ α * a
-
-        @testset "trsv" begin
-            A = triu(rand(elty,n,n))
-            @testset "Vector and SubVector" for x in (rand(elty, n), view(rand(elty,2n),1:2:2n))
-                @test A\x ≈ BLAS.trsv('U','N','N',A,x)
-                @test_throws DimensionMismatch BLAS.trsv('U','N','N',A,Vector{elty}(undef,n+1))
+        @testset "scal" begin
+            α = rand(elty)
+            a = rand(elty,n)
+            @test BLAS.scal(n,α,a,1) ≈ α * a
+            for v in (a, view(a, n:-1:1))
+                @test BLAS.scal!(α, deepcopy(v)) ≈ α * v
             end
         end
-        @testset "ger, her, syr" for x in (rand(elty, n), view(rand(elty,2n), 1:2:2n)),
-            y in (rand(elty,n), view(rand(elty,3n), 1:3:3n))
+
+        @testset "ger, her, syr" for x in (rand(elty, n), view(rand(elty,2n), 1:2:2n), view(rand(elty,n), n:-1:1)),
+            y in (rand(elty,n), view(rand(elty,3n), 1:3:3n), view(rand(elty,2n), 2n:-2:2))
 
             A = rand(elty,n,n)
             α = rand(elty)
@@ -178,32 +153,66 @@ Random.seed!(100)
             end
         end
         @testset "copy" begin
-            x1 = convert(Vector{elty}, randn(n))
-            x2 = convert(Vector{elty}, randn(n))
-            BLAS.copyto!(x2, 1:n, x1, 1:n)
-            @test x2 == x1
+            x1 = randn(elty, n)
+            x2 = randn(elty, n)
+            for ind1 in (1:n, n:-1:1), ind2 in (1:n, n:-1:1)
+                @test x2 === BLAS.copyto!(x2, ind1, x1, ind2) == (ind1 == ind2 ? x1 : reverse(x1))
+            end
             @test_throws DimensionMismatch BLAS.copyto!(x2, 1:n, x1, 1:(n - 1))
             @test_throws ArgumentError BLAS.copyto!(x1, 0:div(n, 2), x2, 1:(div(n, 2) + 1))
             @test_throws ArgumentError BLAS.copyto!(x1, 1:(div(n, 2) + 1), x2, 0:div(n, 2))
         end
-        # trmv
-        A = triu(rand(elty,n,n))
-        x = rand(elty,n)
-        @test BLAS.trmv('U','N','N',A,x) ≈ A*x
+        @testset "trmv and trsv" begin
+            A = rand(elty,n,n)
+            x = rand(elty,n)
+            xerr = Vector{elty}(undef,n+1)
+            for uplo in ('U', 'L'), diag in ('U','N'), trans in ('N', 'T', 'C')
+                Wrapper = if uplo == 'U'
+                    diag == 'U' ? UnitUpperTriangular : UpperTriangular
+                else
+                    diag == 'U' ? UnitLowerTriangular : LowerTriangular
+                end
+                fun = trans == 'N' ? identity : trans == 'T' ? transpose : adjoint
+                fullA = collect(fun(Wrapper(A)))
+                @testset "trmv" begin
+                    @test BLAS.trmv(uplo,trans,diag,A,x) ≈ fullA * x
+                    @test_throws DimensionMismatch BLAS.trmv(uplo,trans,diag,A,xerr)
+                    for xx in (x, view(x, n:-1:1))
+                        @test BLAS.trmv!(uplo,trans,diag,A,deepcopy(xx)) ≈ fullA * xx
+                    end
+                end
+                @testset "trsv" begin
+                    @test BLAS.trsv(uplo,trans,diag,A,x) ≈ fullA \ x
+                    @test_throws DimensionMismatch BLAS.trsv(uplo,trans,diag,A,xerr)
+                    for xx in (x, view(x, n:-1:1))
+                        @test BLAS.trsv!(uplo,trans,diag,A,deepcopy(xx)) ≈ fullA \ xx
+                    end
+                end
+            end
+        end
         @testset "symmetric/Hermitian multiplication" begin
             x = rand(elty,n)
             A = rand(elty,n,n)
+            y = rand(elty, n)
+            α = randn(elty)
+            β = randn(elty)
             Aherm = A + A'
             Asymm = A + transpose(A)
-            @testset "symv and hemv" begin
-                @test BLAS.symv('U',Asymm,x) ≈ Asymm*x
-                offsizevec, offsizemat = Array{elty}.(undef,(n+1, (n,n+1)))
-                @test_throws DimensionMismatch BLAS.symv!('U',one(elty),Asymm,x,one(elty),offsizevec)
-                @test_throws DimensionMismatch BLAS.symv('U',offsizemat,x)
+            offsizevec, offsizemat = Array{elty}.(undef,(n+1, (n,n+1)))
+            @testset "symv and hemv" for uplo in ('U', 'L')
+                @test BLAS.symv(uplo,Asymm,x) ≈ Asymm*x
+                for xx in (x, view(x, n:-1:1)), yy in (y, view(y, n:-1:1))
+                    @test BLAS.symv!(uplo,α,Asymm,xx,β,deepcopy(yy)) ≈ α * Asymm * xx + β * yy
+                end
+                @test_throws DimensionMismatch BLAS.symv!(uplo,α,Asymm,x,β,offsizevec)
+                @test_throws DimensionMismatch BLAS.symv(uplo,offsizemat,x)
                 if elty <: BlasComplex
-                    @test BLAS.hemv('U',Aherm,x) ≈ Aherm*x
-                    @test_throws DimensionMismatch BLAS.hemv('U',offsizemat,x)
-                    @test_throws DimensionMismatch BLAS.hemv!('U',one(elty),Aherm,x,one(elty),offsizevec)
+                    @test BLAS.hemv(uplo,Aherm,x) ≈ Aherm*x
+                    for xx in (x, view(x, n:-1:1)), yy in (y, view(y, n:-1:1))
+                        @test BLAS.hemv!(uplo,α,Aherm,xx,β,deepcopy(yy)) ≈ α * Aherm * xx + β * yy
+                    end
+                    @test_throws DimensionMismatch BLAS.hemv(uplo,offsizemat,x)
+                    @test_throws DimensionMismatch BLAS.hemv!(uplo,one(elty),Aherm,x,one(elty),offsizevec)
                 end
             end
 
@@ -233,40 +242,24 @@ Random.seed!(100)
                 # Both matrix dimensions n coincide, as we have Hermitian matrices.
                 # Define the inputs and outputs of hpmv!, y = α*A*x+β*y
                 α = rand(elty)
-                M = rand(elty, n, n)
-                AL = Hermitian(M, :L)
-                AU = Hermitian(M, :U)
+                A = rand(elty, n, n)
                 x = rand(elty, n)
                 β = rand(elty)
                 y = rand(elty, n)
-
-                y_result_julia_lower = α*AL*x + β*y
-
-                # Create lower triangular packing of AL
-                AP = typeof(AL[1,1])[]
-                for j in 1:n
-                    for i in j:n
-                        push!(AP, AL[i,j])
+                for uplo in (:L, :U)
+                    Cuplo = String(uplo)[1]
+                    AH = Hermitian(A, uplo)
+                    # Create lower/upper triangular packing of AL
+                    AP = pack(AH, uplo)
+                    for xx in (x, view(x,n:-1:1)), yy in (y, view(y,n:-1:1))
+                        @test BLAS.hpmv!(Cuplo, α, AP, xx, β, deepcopy(yy)) ≈ α*AH*xx + β*yy
                     end
+                    AP′ = view(zeros(elty, n*(n+1)),1:2:n*(n+1))
+                    @test_throws ErrorException BLAS.hpmv!(Cuplo, α, AP′, x, β, y)
+                    AP′ = view(AP, 1:length(AP′) - 1)
+                    @test_throws DimensionMismatch BLAS.hpmv!(Cuplo, α, AP′, x, β, y)
+                    @test_throws DimensionMismatch BLAS.hpmv!(Cuplo, α, AP′, x, β, view(y,1:n-1))
                 end
-
-                y_result_blas_lower = copy(y)
-                BLAS.hpmv!('L', α, AP, x, β, y_result_blas_lower)
-                @test y_result_julia_lower ≈ y_result_blas_lower
-
-                y_result_julia_upper = α*AU*x + β*y
-
-                # Create upper triangular packing of AU
-                AP = typeof(AU[1,1])[]
-                for j in 1:n
-                    for i in 1:j
-                        push!(AP, AU[i,j])
-                    end
-                end
-
-                y_result_blas_upper = copy(y)
-                BLAS.hpmv!('U', α, AP, x, β, y_result_blas_upper)
-                @test y_result_julia_upper ≈ y_result_blas_upper
             end
         end
 
@@ -276,41 +269,24 @@ Random.seed!(100)
                 # Both matrix dimensions n coincide, as we have symmetric matrices.
                 # Define the inputs and outputs of spmv!, y = α*A*x+β*y
                 α = rand(elty)
-                M = rand(elty, n, n)
-                AL = Symmetric(M, :L)
-                AU = Symmetric(M, :U)
+                A = rand(elty, n, n)
                 x = rand(elty, n)
                 β = rand(elty)
                 y = rand(elty, n)
-
-                y_result_julia_lower = α*AL*x + β*y
-
-                # Create lower triangular packing of AL
-                AP = typeof(M[1,1])[]
-                for j in 1:n
-                    for i in j:n
-                        push!(AP, AL[i,j])
-                    end
-                end
-
-                y_result_blas_lower = copy(y)
-                BLAS.spmv!('L', α, AP, x, β, y_result_blas_lower)
-                @test y_result_julia_lower ≈ y_result_blas_lower
-
-
-                y_result_julia_upper = α*AU*x + β*y
-
-                # Create upper triangular packing of AU
-                AP = typeof(M[1,1])[]
-                for j in 1:n
-                    for i in 1:j
-                        push!(AP, AU[i,j])
+                for uplo in (:L, :U)
+                    Cuplo = String(uplo)[1]
+                    AS = Symmetric(A, uplo)
+                    # Create lower/upper triangular packing of AL
+                    AP = pack(AS, uplo)
+                    for xx in (x, view(x,n:-1:1)), yy in (y, view(y,n:-1:1))
+                        @test BLAS.spmv!(Cuplo, α, AP, xx, β, deepcopy(yy)) ≈ α*AS*xx + β*yy
                     end
+                    AP′ = view(zeros(elty, n*(n+1)),1:2:n*(n+1))
+                    @test_throws ErrorException BLAS.spmv!(Cuplo, α, AP′, x, β, y)
+                    AP′ = view(AP, 1:length(AP′) - 1)
+                    @test_throws DimensionMismatch BLAS.spmv!(Cuplo, α, AP′, x, β, y)
+                    @test_throws DimensionMismatch BLAS.spmv!(Cuplo, α, AP′, x, β, view(y,1:n-1))
                 end
-
-                y_result_blas_upper = copy(y)
-                BLAS.spmv!('U', α, AP, x, β, y_result_blas_upper)
-                @test y_result_julia_upper ≈ y_result_blas_upper
             end
         end
 
@@ -321,39 +297,29 @@ Random.seed!(100)
                 M = rand(elty, n, n)
                 AL = Symmetric(M, :L)
                 AU = Symmetric(M, :U)
-                x = rand(elty, n)
-
-                function pack(A, uplo)
-                    AP = elty[]
-                    for j in 1:n
-                        for i in (uplo==:L ? (j:n) : (1:j))
-                            push!(AP, A[i,j])
-                        end
-                    end
-                    return AP
+                for x in (rand(elty, n), view(rand(elty, n), n:-1:1))
+                    ALP_result_julia_lower = pack(α*x*x' + AL, :L)
+                    ALP_result_blas_lower = pack(AL, :L)
+                    BLAS.spr!('L', α, x, ALP_result_blas_lower)
+                    @test ALP_result_julia_lower ≈ ALP_result_blas_lower
+                    ALP_result_blas_lower = append!(pack(AL, :L), ones(elty, 10))
+                    BLAS.spr!('L', α, x, ALP_result_blas_lower)
+                    @test ALP_result_julia_lower ≈ ALP_result_blas_lower[1:end-10]
+                    ALP_result_blas_lower = reshape(pack(AL, :L), 1, length(ALP_result_julia_lower), 1)
+                    BLAS.spr!('L', α, x, ALP_result_blas_lower)
+                    @test ALP_result_julia_lower ≈ vec(ALP_result_blas_lower)
+
+                    AUP_result_julia_upper = pack(α*x*x' + AU, :U)
+                    AUP_result_blas_upper = pack(AU, :U)
+                    BLAS.spr!('U', α, x, AUP_result_blas_upper)
+                    @test AUP_result_julia_upper ≈ AUP_result_blas_upper
+                    AUP_result_blas_upper = append!(pack(AU, :U), ones(elty, 10))
+                    BLAS.spr!('U', α, x, AUP_result_blas_upper)
+                    @test AUP_result_julia_upper ≈ AUP_result_blas_upper[1:end-10]
+                    AUP_result_blas_upper = reshape(pack(AU, :U), 1, length(AUP_result_julia_upper), 1)
+                    BLAS.spr!('U', α, x, AUP_result_blas_upper)
+                    @test AUP_result_julia_upper ≈ vec(AUP_result_blas_upper)
                 end
-
-                ALP_result_julia_lower = pack(α*x*x' + AL, :L)
-                ALP_result_blas_lower = pack(AL, :L)
-                BLAS.spr!('L', α, x, ALP_result_blas_lower)
-                @test ALP_result_julia_lower ≈ ALP_result_blas_lower
-                ALP_result_blas_lower = append!(pack(AL, :L), ones(elty, 10))
-                BLAS.spr!('L', α, x, ALP_result_blas_lower)
-                @test ALP_result_julia_lower ≈ ALP_result_blas_lower[1:end-10]
-                ALP_result_blas_lower = reshape(pack(AL, :L), 1, length(ALP_result_julia_lower), 1)
-                BLAS.spr!('L', α, x, ALP_result_blas_lower)
-                @test ALP_result_julia_lower ≈ vec(ALP_result_blas_lower)
-
-                AUP_result_julia_upper = pack(α*x*x' + AU, :U)
-                AUP_result_blas_upper = pack(AU, :U)
-                BLAS.spr!('U', α, x, AUP_result_blas_upper)
-                @test AUP_result_julia_upper ≈ AUP_result_blas_upper
-                AUP_result_blas_upper = append!(pack(AU, :U), ones(elty, 10))
-                BLAS.spr!('U', α, x, AUP_result_blas_upper)
-                @test AUP_result_julia_upper ≈ AUP_result_blas_upper[1:end-10]
-                AUP_result_blas_upper = reshape(pack(AU, :U), 1, length(AUP_result_julia_upper), 1)
-                BLAS.spr!('U', α, x, AUP_result_blas_upper)
-                @test AUP_result_julia_upper ≈ vec(AUP_result_blas_upper)
             end
         end
 
@@ -365,33 +331,51 @@ Random.seed!(100)
         #will work for SymTridiagonal,Tridiagonal,Bidiagonal!
         @testset "banded matrix mv" begin
             @testset "gbmv" begin
-                TD  = Tridiagonal(rand(elty,n-1),rand(elty,n),rand(elty,n-1))
-                x   = rand(elty,n)
+                TD = Tridiagonal(rand(elty,n-1),rand(elty,n),rand(elty,n-1))
+                x  = rand(elty, n)
                 #put TD into the BLAS format!
                 fTD = zeros(elty,3,n)
                 fTD[1,2:n] = TD.du
                 fTD[2,:] = TD.d
                 fTD[3,1:n-1] = TD.dl
                 @test BLAS.gbmv('N',n,1,1,fTD,x) ≈ TD*x
+                y = rand(elty, n)
+                α = randn(elty)
+                β = randn(elty)
+                for xx in (x, view(x, n:-1:1)), yy in (y, view(y, n:-1:1))
+                    @test BLAS.gbmv!('N',n,1,1,α,fTD,xx,β,deepcopy(yy)) ≈ α * TD * xx + β * yy
+                end
             end
             #will work for SymTridiagonal only!
-            @testset "sbmv" begin
+            @testset "sbmv and hbmv" begin
+                x = rand(elty,n)
                 if elty <: BlasReal
                     ST  = SymTridiagonal(rand(elty,n),rand(elty,n-1))
-                    x   = rand(elty,n)
                     #put TD into the BLAS format!
                     fST = zeros(elty,2,n)
                     fST[1,2:n] = ST.ev
                     fST[2,:] = ST.dv
                     @test BLAS.sbmv('U',1,fST,x) ≈ ST*x
+                    y = rand(elty, n)
+                    α = randn(elty)
+                    β = randn(elty)
+                    for xx in (x, view(x, n:-1:1)), yy in (y, view(y, n:-1:1))
+                        @test BLAS.sbmv!('U',1,α,fST,xx,β,deepcopy(yy)) ≈ α * ST * xx + β * yy
+                    end
                 else
-                    dv = real(rand(elty,n))
+                    dv = rand(real(elty),n)
                     ev = rand(elty,n-1)
                     bH = zeros(elty,2,n)
                     bH[1,2:n] = ev
                     bH[2,:] = dv
                     fullH = diagm(0 => dv, -1 => conj(ev), 1 => ev)
                     @test BLAS.hbmv('U',1,bH,x) ≈ fullH*x
+                    y = rand(elty, n)
+                    α = randn(elty)
+                    β = randn(elty)
+                    for xx in (x, view(x, n:-1:1)), yy in (y, view(y, n:-1:1))
+                        @test BLAS.hbmv!('U',1,α,bH,xx,β,deepcopy(yy)) ≈ α * fullH * xx + β * yy
+                    end
                 end
             end
         end
@@ -595,8 +579,8 @@ end
         @test BLAS.iamax(x) == 2
 
         M = fill(elty(1.0), 3, 3)
-        BLAS.scal!(elty(2), view(M,:,2))
-        BLAS.scal!(elty(3), view(M,3,:))
+        @test BLAS.scal!(elty(2), view(M,:,2)) === view(M,:,2)
+        @test BLAS.scal!(elty(3), view(M,3,:)) === view(M,3,:)
         @test M == elty[1. 2. 1.; 1. 2. 1.; 3. 6. 3.]
     # Level 2
         A = WrappedArray(elty[1 2; 3 4])
@@ -688,4 +672,36 @@ end
 @test LinearAlgebra.BLAS.libblas == "libblastrampoline"
 @test LinearAlgebra.BLAS.liblapack == "libblastrampoline"
 
+@testset "test for 0-strides" for elty in (Float32, Float64, ComplexF32, ComplexF64)
+    A = randn(elty, 10, 10);
+    a = view([randn(elty)], 1 .+ 0(1:10))
+    b = view([randn(elty)], 1 .+ 0(1:10))
+    α, β = randn(elty), randn(elty)
+    @testset "dot/dotc/dotu" begin
+        if elty <: Real
+            @test BLAS.dot(a,b) ≈ sum(a.*b)
+        else
+            @test BLAS.dotc(a,b) ≈ sum(conj(a).*b)
+            @test BLAS.dotu(a,b) ≈ sum(a.*b)
+        end
+    end
+    @testset "axp(b)y!" begin
+        @test BLAS.axpy!(α,a,copy(b)) ≈ α*a + b
+        @test BLAS.axpby!(α,a,β,copy(b)) ≈ α*a + β*b
+        @test_throws "dest" BLAS.axpy!(α,a,b)
+        @test_throws "dest" BLAS.axpby!(α,a,β,b)
+    end
+    @test BLAS.iamax(a) == 0
+    @test_throws "dest" BLAS.scal!(b[1], a)
+    @testset "nrm2/asum" begin # OpenBLAS allways return 0.0
+        @test_throws "input" BLAS.nrm2(a)
+        @test_throws "input" BLAS.asum(a)
+    end
+    # All level2 reject 0-stride array.
+    @testset "gemv!" begin
+        @test_throws "input" BLAS.gemv!('N', true, A, a, false, copy(b))
+        @test_throws "dest" BLAS.gemv!('N', true, A, copy(a), false, b)
+    end
+end
+
 end # module TestBLAS
diff --git a/stdlib/LinearAlgebra/test/matmul.jl b/stdlib/LinearAlgebra/test/matmul.jl
index 1c482f8cae97ac..ea73814a2848b8 100644
--- a/stdlib/LinearAlgebra/test/matmul.jl
+++ b/stdlib/LinearAlgebra/test/matmul.jl
@@ -297,6 +297,15 @@ end
     end
 end
 
+@testset "matrix x vector with negative lda or 0 stride" for T in (Float32, Float64)
+    for TA in (T, complex(T)), TB in (T, complex(T))
+        A = view(randn(TA, 10, 10), 1:10, 10:-1:1) # negative lda
+        v = view([randn(TB)], 1 .+ 0(1:10)) # 0 stride
+        Ad, vd = copy(A), copy(v)
+        @test Ad * vd ≈ A * vd ≈ Ad * v ≈ A * v
+    end
+end
+
 @testset "issue #15286" begin
     A = reshape(map(Float64, 1:20), 5, 4)
     C = zeros(8, 8)