capacity management, monitoring, and other O&M features supported; Custom SDS solution based on Rook Ceph and NooBaa; Integration with major distributed storage via CSI, including Ceph, GlusterFS

Data Foundation, formerly known as OpenShift Container Storage, which is based on Ceph, NooBaa and Rook. OpenShift Container Storage covers different use cases for OpenShift and is a powerful solution

= array[Rows, Fig] Board = array[Cols, Col] var b: Board const a = 0 rook = 5 # whatever makes sense b[a][0] = rook echo b[a][0] # 5 # with user-defined templates we can simplify the index notation int): int8 = b[i][j] template `[]=`(b: var Board; i, j: int; v: int8) = b[i][j] = v b[a, 0] = rook echo b[a, 0] # 5 Now, let’s investigate the case where one or both dimensions of the matrix can

= array[Rows, Fig] Board = array[Cols, Col] var b: Board const a = 0 rook = 5 # whatever makes sense b[a][0] = rook echo b[a][0] # 5 # with user-defined templates we can simplify the index notation int): int8 = b[i][j] template `[]=`(b: var Board; i, j: int; v: int8) = b[i][j] = v b[a, 0] = rook echo b[a, 0] # 5 Now, let’s investigate the case where one or both dimensions of the matrix can

iteration julia> l == S.L && q == S.Q true LinearAlgebra.bunchkaufman – Func�on. bunchkaufman(A, rook::Bool=false; check = true) -> S::BunchKaufman Compute the Bunch-Kaufman 3 factoriza�on of a Symmetric the components S.D, S.U or S.L as appropriate given S.uplo, and S.p. If rook is true, rook pivo�ng is used. If rook is false, rook pivo�ng is not used. When check = true, an error is thrown if the decomposi�on julia> d == S.D && u == S.U && p == S.p true LinearAlgebra.bunchkaufman! – Func�on. bunchkaufman!(A, rook::Bool=false; check = true) -> BunchKaufman bunchkaufman! is the same as bunchkaufman, but saves space

iteration julia> l == S.L && q == S.Q true LinearAlgebra.bunchkaufman – Func�on. bunchkaufman(A, rook::Bool=false; check = true) -> S::BunchKaufman Compute the Bunch-Kaufman 3 factoriza�on of a Symmetric the components S.D, S.U or S.L as appropriate given S.uplo, and S.p. If rook is true, rook pivo�ng is used. If rook is false, rook pivo�ng is not used. When check = true, an error is thrown if the decomposi�on julia> d == S.D && u == S.U && p == S.p true LinearAlgebra.bunchkaufman! – Func�on. bunchkaufman!(A, rook::Bool=false; check = true) -> BunchKaufman bunchkaufman! is the same as bunchkaufman, but saves space

iteration julia> l == S.L && q == S.Q true LinearAlgebra.bunchkaufman – Func�on. bunchkaufman(A, rook::Bool=false; check = true) -> S::BunchKaufman Compute the Bunch-Kaufman 3 factoriza�on of a Symmetric the components S.D, S.U or S.L as appropriate given S.uplo, and S.p. If rook is true, rook pivo�ng is used. If rook is false, rook pivo�ng is not used. When check = true, an error is thrown if the decomposi�on julia> d == S.D && u == S.U && p == S.p true LinearAlgebra.bunchkaufman! – Func�on. bunchkaufman!(A, rook::Bool=false; check = true) -> BunchKaufman bunchkaufman! is the same as bunchkaufman, but saves space

iteration julia> l == S.L && q == S.Q true LinearAlgebra.bunchkaufman – Func�on. bunchkaufman(A, rook::Bool=false; check = true) -> S::BunchKaufman Compute the Bunch-Kaufman 3 factoriza�on of a Symmetric the components S.D, S.U or S.L as appropriate given S.uplo, and S.p. If rook is true, rook pivo�ng is used. If rook is false, rook pivo�ng is not used. When check = true, an error is thrown if the decomposi�on julia> d == S.D && u == S.U && p == S.p true LinearAlgebra.bunchkaufman! – Func�on. bunchkaufman!(A, rook::Bool=false; check = true) -> BunchKaufman bunchkaufman! is the same as bunchkaufman, but saves space

= array[Rows, Fig] Board = array[Cols, Col] var b: Board const a = 0 rook = 5 # whatever makes sense b[a][0] = rook echo b[a][0] # 5 # with user-defined templates we can simplify the index notation int): int8 = b[i][j] template `[]=`(b: var Board; i, j: int; v: int8) = b[i][j] = v b[a, 0] = rook echo b[a, 0] # 5 Now, let’s investigate the case where one or both dimensions of the matrix can grow

= array[Rows, Fig] Board = array[Cols, Col] var b: Board const a = 0 rook = 5 # whatever makes sense b[a][0] = rook echo b[a][0] # 5 # with user-defined templates we can simplify the index notation int): int8 = b[i][j] template `[]=`(b: var Board; i, j: int; v: int8) = b[i][j] = v b[a, 0] = rook echo b[a, 0] # 5 Now, let’s investigate the case where one or both dimensions of the matrix can grow