# Explicit SIMD SYCL* Extension

oneAPI provides an Explicit SIMD SYCL extension (ESIMD) for lower-level Intel GPU programming.

ESIMD provides APIs that are similar to Intel's GPU Instruction Set Architecture (ISA), but it enables you to write explicitly vectorized device code. This explicit enabling gives you more control over the generated code and allows you to depend less on compiler optimizations.

The specification, documented ESIMD API headers, and working code examples are available on GitHub*.

### Note

This extension is under active development and the APIs are subject to change. There are currently a number of restrictions on the extension, which are specified below.

ESIMD kernels and functions always require a subgroup size of one, which means that the compiler does not provide vectorization across work items in a subgroup. Instead, you must explicitly express the vectorization in your code. Below is an example that adds the elements of two arrays and writes the results to the third:

    float *A = static_cast<float *>(malloc_shared(Size * sizeof(float), dev, ctxt));
    float *B = static_cast<float *>(malloc_shared(Size * sizeof(float), dev, ctxt));
    float *C = static_cast<float *>(malloc_shared(Size * sizeof(float), dev, ctxt));

    for (unsigned i = 0; i < Size; ++i) {
      A[i] = B[i] = i;
    }

    // Many work items are needed. Each work item processes Vector Length (VL)
    // elements of data.
    cl::sycl::range<1> GlobalRange{Size / VL};
    // Number of work items in each workgroup.
    cl::sycl::range<1> LocalRange{GroupSize};

    cl::sycl::nd_range<1> Range(GlobalRange, LocalRange);

    auto e = q.submit([&](handler &cgh) {
      cgh.parallel_for<class Test>(Range, [=](nd_item<1> ndi) SYCL_ESIMD_KERNEL {
        using namespace sycl::ext::intel::experimental::esimd;

        int i = ndi.get_global_id(0);
        simd<float, VL> va = block_load<float, VL>(A + i * VL);
        simd<float, VL> vb = block_load<float, VL>(B + i * VL);
        simd<float, VL> vc = va + vb;
        block_store<float, VL>(C + i * VL, vc);
      });
    });

In the example above, the lambda function passed to the `parallel_for` is marked with a special attribute: `SYCL_ESIMD_KERNEL`. This attribute tells the compiler that the kernel is ESIMD-based and ESIMD APIs can be used inside it. Here the `simd` objects and `block_load`/`block_store` intrinsics are used. They are available only in the ESIMD extension.

Fully runnable code samples can be found in the GitHub repo.

## Compiling and Running ESIMD Code

Compiling and running code that uses the ESIMD extension is the same as compiler and running code that uses the standard SYCL:

    $ clang++ -fsycl vadd_usm.cpp

The resulting executable (`$./a.out`) can be run only on Intel GPU hardware, such as Intel® UHD Graphics 600 or later. The DPC++ runtime automatically recognizes ESIMD kernels and dispatches their execution, so no additional setup is needed.

## Restrictions

This section contains lists of the main restrictions that apply when using the ESIMD extension.

### Note

Some extensions are not enforced by the compiler, which may lead to undefined program behavior.

- Features not supported with ESIMD:
  - Ahead-of-time (AOT) compilation
  - C and C++ standard libraries support
  - Device library extensions
  - A host device (in some cases)
- Unsupported standard SYCL APIs:
  - Local accessors
  - Most image APIs
  - Memory access through a raw pointer returned by `sycl::accessor::get_pointer()`
- Other restrictions:
  - Only Intel GPU devices are supported.

Parent topic: Explicit Vector Programming