use SparseArrays machinery for printing

[CarloLucibello]

Types here could hookup into SparseArrays show implementation for a nice printing.

julia> using SparseArrays, SuiteSparseGraphBLAS

julia> x = sprand(10, 10, 0.5)
10×10 SparseMatrixCSC{Float64, Int64} with 39 stored entries:
  ⋅       0.359801   ⋅         ⋅          ⋅          ⋅         ⋅        0.579892  0.592154  0.088285
  ⋅        ⋅         ⋅        0.522135    ⋅         0.840658  0.561799  0.158865   ⋅         ⋅ 
  ⋅       0.84019    ⋅         ⋅         0.189342    ⋅         ⋅         ⋅        0.889942  0.435336
  ⋅        ⋅        0.793216  0.0387143  0.295225    ⋅         ⋅        0.159388  0.97282   0.630283
 0.06779  0.784768   ⋅         ⋅          ⋅          ⋅         ⋅         ⋅        0.71467    ⋅ 
  ⋅       0.452141  0.5673     ⋅          ⋅         0.497747  0.892377   ⋅        0.624008   ⋅ 
  ⋅       0.461338   ⋅         ⋅         0.0744113   ⋅         ⋅         ⋅        0.817912   ⋅ 
  ⋅       0.685151   ⋅         ⋅          ⋅         0.4771     ⋅         ⋅         ⋅        0.110999
  ⋅        ⋅        0.671881   ⋅          ⋅          ⋅        0.848146  0.205909   ⋅         ⋅ 
  ⋅       0.775386   ⋅         ⋅         0.453898    ⋅        0.76114    ⋅         ⋅        0.478859

julia> GBMatrix(x)
10x10 GraphBLAS double matrix, bitmap by col
  39 entries, memory: 1.1 KB

    (5,1)    0.06779
    (1,2)    0.359801
    (3,2)    0.84019
    (5,2)    0.784768
    (6,2)    0.452141
    (7,2)    0.461338
    (8,2)    0.685151
    (10,2)    0.775386
    (4,3)    0.793216
    (6,3)    0.5673
    (9,3)    0.671881
    (2,4)    0.522135
    (4,4)    0.0387143
    (3,5)    0.189342
    (4,5)    0.295225
    (7,5)    0.0744113
    (10,5)    0.453898
    (2,6)    0.840658
    (6,6)    0.497747
    (8,6)    0.4771
    (2,7)    0.561799
    (6,7)    0.892377
    (9,7)    0.848146
    (10,7)    0.76114
    (1,8)    0.579892
    (2,8)    0.158865
    (4,8)    0.159388
    (9,8)    0.205909
    (1,9)    0.592154
    ...

This is what CUSPARSE types do in CUDA.jl https://github.com/JuliaGPU/CUDA.jl/blob/583b948ef4f1ab739047d19e7f65657a354ec28b/lib/cusparse/array.jl#L503

julia> using SparseArrays, CUDA

julia> x = sprand(3,3,0.5)
3×3 SparseMatrixCSC{Float64, Int64} with 7 stored entries:
  ⋅        0.662806   0.34835
 0.422577  0.0506328  0.932217
  ⋅        0.649229   0.753374

julia> xd = x |> cu
3×3 CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32} with 7 stored entries:
  ⋅         0.66280556   0.34835035
 0.4225766  0.050632793  0.9322167
  ⋅         0.649229     0.7533741

[rayegun]

Yep I plan on doing this soon, thanks for the pointer on CuSparseMatrixCSC that will help.