Data-parallel Extension for Numba* (numba-dpex) is a standalone extension for
the [Numba](http://numba.pydata.org) Python JIT compiler. Numba-dpex provides
a generic kernel programming API and an offload feature that extends Numba's
auto-parallelizer to generate data-parallel kernels for `parfor` nodes.
Numba-dpex's kernel API has a design and API similar to Numba's `cuda.jit`
module, but is based on the [SYCL](https://sycl.tech/) language. The
code-generation for the kernel API currently supports
[SPIR-V](https://www.khronos.org/spir/)-based
[OpenCL](https://www.khronos.org/opencl/) and
[oneAPI Level Zero](https://spec.oneapi.io/level-zero/latest/index.html)
devices that are supported by Intel® DPC++ SYCL compiler runtime. Supported
devices include Intel® CPUs, integrated GPUs and discrete GPUs.
The offload functionality in numba-dpex is based on Numba's `parfor`
loop-parallelizer. Our compiler extends Numba's `parfor` feature to generate
kernels and offload them to devices supported by DPC++ SYCL compiler runtime.
The offload functionality is supported via a new NumPy drop-in replacement
library: [dpnp](https://github.com/IntelPython/dpnp). Note that `dpnp` and NumPy-based
expressions can be used together in the same function, with `dpnp` expressions getting
offloaded by `numba-dpex` and NumPy expressions getting parallelized by Numba.
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"description": "\n\nData-parallel Extension for Numba* (numba-dpex) is a standalone extension for\nthe [Numba](http://numba.pydata.org) Python JIT compiler. Numba-dpex provides\na generic kernel programming API and an offload feature that extends Numba's\nauto-parallelizer to generate data-parallel kernels for `parfor` nodes.\n\nNumba-dpex's kernel API has a design and API similar to Numba's `cuda.jit`\nmodule, but is based on the [SYCL](https://sycl.tech/) language. The\ncode-generation for the kernel API currently supports\n[SPIR-V](https://www.khronos.org/spir/)-based\n[OpenCL](https://www.khronos.org/opencl/) and\n[oneAPI Level Zero](https://spec.oneapi.io/level-zero/latest/index.html)\ndevices that are supported by Intel® DPC++ SYCL compiler runtime. Supported\ndevices include Intel® CPUs, integrated GPUs and discrete GPUs.\n\nThe offload functionality in numba-dpex is based on Numba's `parfor`\nloop-parallelizer. Our compiler extends Numba's `parfor` feature to generate\nkernels and offload them to devices supported by DPC++ SYCL compiler runtime.\nThe offload functionality is supported via a new NumPy drop-in replacement\nlibrary: [dpnp](https://github.com/IntelPython/dpnp). Note that `dpnp` and NumPy-based\nexpressions can be used together in the same function, with `dpnp` expressions getting\noffloaded by `numba-dpex` and NumPy expressions getting parallelized by Numba.\n",
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