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Sparse array README fixes #649

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3 changes: 1 addition & 2 deletions README.md
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Expand Up @@ -49,7 +49,7 @@ GPU sparse arrays.
For **host-side** types, your custom sparse types should implement:

- `dense_array_type` - the corresponding dense array type. For example, for a `CuSparseVector` or `CuSparseMatrixCXX`, the `dense_array_type` is `CuArray`
- `sparse_array_type` - the **untyped** sparse array type corresponding to a given parametrized type. A `CuSparseVector{Tv, Ti}` would have a `sparse_array_type` of `CuVector` -- note the lack of type parameters!
- `sparse_array_type` - the **untyped** sparse array type corresponding to a given parametrized type. A `CuSparseVector{Tv, Ti}` would have a `sparse_array_type` of `CuSparseVector` -- note the lack of type parameters!
- `csc_type(::Type{T})` - the compressed sparse column type for your backend. A `CuSparseMatrixCSR` would have a `csc_type` of `CuSparseMatrixCSC`.
- `csr_type(::Type{T})` - the compressed sparse row type for your backend. A `CuSparseMatrixCSC` would have a `csr_type` of `CuSparseMatrixCSR`.
- `coo_type(::Type{T})` - the coordinate sparse matrix type for your backend. A `CuSparseMatrixCSC` would have a `coo_type` of `CuSparseMatrixCOO`.
Expand Down Expand Up @@ -90,4 +90,3 @@ You'll also need to inform `GPUArrays.jl` and `GPUCompiler.jl` how to adapt your
```julia
KA.get_backend(::MySparseVector) = MyBackend()
```
```
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