More content in oneMKL chapter

Author: ndgrigorian

content/en/docs/oneMKL.md

@@ -5,7 +5,7 @@ date: 2024-07-02
 weight: 4
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-Since `dpctl.tensor.usm_ndarray` is a Python object carrying a USM allocation, it is possible to write extensions which wrap `oneAPI Math Kernel Library Interfaces` ([oneMKL Interfaces](https://github.com/oneapi-src/oneMKL)) routines and then call them on the USM data underlying the `usm_ndarray` container from Python.
+Since `dpctl.tensor.usm_ndarray` is a Python object with an underlying USM allocation, it is possible to write extensions which wrap `oneAPI Math Kernel Library Interfaces` ([oneMKL Interfaces](https://github.com/oneapi-src/oneMKL)) USM routines and then call them on the `dpctl.tensor.usm_ndarray` from Python. These low-level routines have the potential to greatly improve the performance of extensions.
 
 For an example routine from the `oneMKL` documentation, take [`geqrf`](https://spec.oneapi.io/versions/latest/elements/oneMKL/source/domains/lapack/geqrf.html#geqrf-usm-version):
 ```cpp
@@ -22,6 +22,10 @@ namespace oneapi::mkl::lapack {
 }
 ```
 
-The `pybind11` castings discussed in the previous section enable us to write a simple wrapper function for this routine with `dpctl::tensor::usm_ndarray` inputs and outputs, so long as we take the same precautions to avoid deadlocks. As a result, we can write the extension in much the same way as the `kde_sycl_ext` extension in the previous chapter.
+This general format (``sycl::queue``, arguments, and a vector of ``sycl::event``s) is more or less the same throughout the `oneMKL` USM routine.
 
-An example of a Python extension "mkl_interface_ext" that uses `oneMKL` calls to implement a QR decomposition can be found in "steps/mkl_interface" folder (see [README](steps/mkl_interface/README.md)).
+The `pybind11` castings discussed in the previous section enable us to write a simple wrapper function for this routine with ``dpctl::tensor::usm_ndarray`` inputs and outputs, so long as we take the same precautions to avoid deadlocks. As a result, we can write the extension in much the same way as the `"kde_sycl_ext"` extension in the previous chapter.
+
+An example of a Python extension `"mkl_interface_ext"` that uses `oneMKL` calls to implement a QR decomposition can be found in `"steps/mkl_interface"` folder (see [README](steps/mkl_interface/README.md)).
+
+`oneMKL` can be built for a variety of backends (see [oneMKL interfaces README](https://github.com/oneapi-src/oneMKL?tab=readme-ov-file#oneapi-math-kernel-library-onemkl-interfaces)). The example extension provides instructions for compiling for Intel, CUDA, and AMD, but the [`portBLAS`](https://github.com/codeplaysoftware/portBLAS) and [`portFFT`](https://github.com/codeplaysoftware/portFFT) backends are worth mentioning that. While the routines in `"mkl_interface_ext"` are not supported, these libraries are written in pure SYCL, and are therefore highly portable: they can offload to CPU, Intel, CUDA, and AMD devices. They are also open-source.