Update Libraries/oneMKL/sparse_conjugate_gradient/README.md

Co-authored-by: Nicolas Offermans <[email protected]>

Author: spencerpatty

Libraries/oneMKL/sparse_conjugate_gradient/README.md

@@ -17,7 +17,7 @@ Sparse Conjugate Gradient uses oneMKL sparse linear algebra routines to solve a
 This sample performs its computations on the default SYCL* device. You can set the `SYCL_DEVICE_TYPE` environment variable to `cpu` or `gpu` to select the device to use.
 
 ## Key Implementation Details
-oneMKL sparse routines use a two-stage method where the sparse matrix is analyzed to prepare subsequent calculations (the _optimize_ step). Sparse matrix-vector multiplication and triangular solves (`gemv` and `trsv`) are used to implement the main loop, along with vector routines from BLAS. Two implementations are provided: The first implementation, in `sparse_cg.cpp`, has several places where a device to host copy and wait are initiated to allow the alpha and beta coefficients to be initiated in the BLAS vector routines as host scalars.  The second implementation, in `sparse_cg2.cpp`, keeps the coefficients for alpha and beta on the device, which require that custom axpby2 and axpy3 fucntions are written to handle the construction of alpha and beta coefficients on-the-fly from the device. This removes some of the synchronization points that are seen in the first implementation.
+oneMKL sparse routines use a two-stage method where the sparse matrix is analyzed to prepare subsequent calculations (the _optimize_ step). Sparse matrix-vector multiplication and triangular solves (`gemv` and `trsv`) are used to implement the main loop, along with vector routines from BLAS. Two implementations are provided: The first implementation, in `sparse_cg.cpp`, has several places where a device to host copy and wait are initiated to allow the alpha and beta coefficients to be initiated in the BLAS vector routines as host scalars.  The second implementation, in `sparse_cg2.cpp`, keeps the coefficients for alpha and beta on the device, which require that custom axpby2 and axpy3 functions are written to handle the construction of alpha and beta coefficients on-the-fly from the device. This removes some of the synchronization points that are seen in the first implementation.
 
 ## Using Visual Studio Code* (Optional)
 You can use Visual Studio Code (VS Code) extensions to set your environment, create launch configurations,