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# hipSPARSE precision support

This section provides an overview of the numerical precision types supported by the hipSPARSE library for sparse matrix operations.

## Supported precision types

hipSPARSE supports the following precision types across its functions:

Type prefix | C++ type | Description
--- | --- | ---
`s` | `float` | Single-precision real (32-bit)
`d` | `double` | Double-precision real (64-bit)
`c` | `hipFloatComplex` | Single-precision complex (32-bit real, 32-bit imaginary)
`z` | `hipDoubleComplex` | Double-precision complex (64-bit real, 64-bit imaginary)

### Function naming convention

hipSPARSE follows a naming convention where the precision type is included in the function name:

- Functions containing `hipsparseS` operate on single-precision real data.

- Functions containing `hipsparseD` operate on double-precision real data.

- Functions containing `hipsparseC` operate on single-precision complex data.

- Functions containing `hipsparseZ` operate on double-precision complex data.

For example, the dense matrix sparse vector multiplication function is implemented as:

- `hipsparseSgemvi` - For single-precision real sparse matrices.

- `hipsparseDgemvi` - For double-precision real sparse matrices.

- `hipsparseCgemvi` - For single-precision complex sparse matrices.

- `hipsparseZgemvi` - For double-precision complex sparse matrices.

In the documentation, these functions are often represented generically as `hipsparseXgemvi()`, where `X` is a placeholder for the precision type character.

### Understanding precision in function signatures

In the function signatures throughout the documentation, precision information is indicated directly in the parameter types. For example:

    hipsparseStatus_t hipsparseSgemvi(hipsparseHandle_t handle,
                                      hipsparseOperation_t transA,
                                      int m,
                                      int n,
                                      const float *alpha,
                                      const float *A,
                                      int lda,
                                      int nnz,
                                      const float *x,
                                      const int *xInd,
                                      const float *beta,
                                      float *y,
                                      hipsparseIndexBase_t idxBase,
                                      void *pBuffer)

The parameter types (`float`, `double`, `hipFloatComplex`, or `hipDoubleComplex`) correspond to the function precision type character and indicate the precision used by that specific function variant.

### Generic functions and mixed precision

hipSPARSE provides generic functions that allow for mixed-precision operations. These functions use data type enumerations to specify precision when creating matrix or vector descriptors and during computation.

For example, when creating a sparse matrix descriptor:

    // Create CSR matrix with single-precision values
    hipsparseCreateCsr(&matA, m, n, nnz,
                       rowPtr, colInd, values,
                       HIPSPARSE_INDEX_32I, HIPSPARSE_INDEX_32I,
                       HIPSPARSE_INDEX_BASE_ZERO, HIP_R_32F);

Or when creating a dense vector:

    // Create dense vector with double-precision values
    hipsparseCreateDnVec(&vecX, size, xValues, HIP_R_64F);

Functions like `hipsparseSpMV` use a separate parameter for computation precision:

    // Matrix-vector multiplication with computation in single precision
    hipsparseSpMV(handle, transA, &alpha, matA, vecX, &beta, vecY,
                  HIP_R_32F, HIPSPARSE_MV_ALG_DEFAULT, buffer);

This approach enables:

- Using different precision types for matrices and vectors.

- Specifying computation precision independently of storage precision.

- Supporting mixed-precision workflows with a unified API.

The advantage of using different data types is to save on memory bandwidth and storage when a user application allows for it while performing the actual computation in a higher precision.

### Real versus complex precision

Most sparse matrix operations in hipSPARSE support both real and complex precisions, however certain algorithms or optimizations might be specific to real or complex data:

- Some functions might have different performance characteristics for complex data compared to real data.

- Certain conversion routines might operate differently depending on whether the data is real or complex.

### Core precision types

The four core precision types (s, d, c, z) are supported across most functions in hipSPARSE. Some specialized functions might only support a subset of these types. See the specific function documentation to confirm which precision types are supported for a particular operation.