5.2. Writing HSA Kernels

5.2.1. Introduction

HSA provides an execution model similar to OpenCL. Instructions are executed in parallel by a group of hardware threads. In some way, this is similar to single-instruction-multiple-data (SIMD) model but with the convenience that the fine-grain scheduling is hidden from the programmer instead of programming with SIMD vectors as a data structure. In HSA, the code you write will be executed by multiple threads at once (often hundreds or thousands). Your solution will be modeled by defining a thread hierarchy of grid, workgroup and workitem.

Numba’s HSA support exposes facilities to declare and manage this hierarchy of threads.

5.2.2. Introduction for CUDA Programmers

HSA execution model is similar to CUDA. The main difference will be the shared memory model employed by HSA so that there are no device memory. The GPU hardware uses the machine’s main memory (or host memory in CUDA term) directly. Therefore, you will not need to_device() and copy_to_host() in HSA programming.

Here’s a quick mapping of the CUDA terms to HSA: * workitem is CUDA threads * workgroup is CUDA thread block * grid is CUDA grid

5.2.3. Kernel declaration

A kernel function is a GPU function that is meant to be called from CPU code. It gives it two fundamental characteristics:

  • kernels cannot explicitly return a value; all result data must be written to an array passed to the function (if computing a scalar, you will probably pass a one-element array);
  • kernels explicitly declare their thread hierarchy when called: i.e. the number of workgroups and the number of workitems per workgroup (note that while a kernel is compiled once, it can be called multiple times with different workgroup sizes or grid sizes).

At first sight, writing a HSA kernel with Numba looks very much like writing a JIT function for the CPU:

def increment_by_one(an_array):
    Increment all array elements by one.
    # code elided here; read further for different implementations

5.2.4. Kernel invocation

A kernel is typically launched in the following way:

itempergroup = 32
groupperrange = (an_array.size + (itempergroup - 1)) // itempergroup
increment_by_one[groupperrange, itempergroup](an_array)

We notice two steps here:

  • Instantiate the kernel proper, by specifying a number of workgroup (or “workgroup per grid”), and a number of workitems per workgroup. The product of the two will give the total number of workitem launched. Kernel instantiation is done by taking the compiled kernel function (here increment_by_one) and indexing it with a tuple of integers.
  • Running the kernel, by passing it the input array (and any separate output arrays if necessary). By default, running a kernel is synchronous: the function returns when the kernel has finished executing and the data is synchronized back. Choosing the workgroup size

It might seem curious to have a two-level hierarchy when declaring the number of workitem needed by a kernel. The workgroup size (i.e. number of workitem per workgroup) is often crucial:

  • On the software side, the workgroup size determines how many threads share a given area of shared memory.

  • On the hardware side, the workgroup size must be large enough for full

    occupation of execution units. Multi-dimensional workgroup and grid

To help deal with multi-dimensional arrays, HSA allows you to specify multi-dimensional workgroups and grids. In the example above, you could make itempergroup and groupperrange tuples of one, two or three integers. Compared to 1D declarations of equivalent sizes, this doesn’t change anything to the efficiency or behaviour of generated code, but can help you write your algorithms in a more natural way.

5.2.5. WorkItem positioning

When running a kernel, the kernel function’s code is executed by every thread once. It therefore has to know which thread it is in, in order to know which array element(s) it is responsible for (complex algorithms may define more complex responsibilities, but the underlying principle is the same).

One way is for the thread to determines its position in the grid and workgroup and manually compute the corresponding array position:

def increment_by_one(an_array):
    # workitem id in a 1D workgroup
    tx = hsa.get_local_id(0)
    # workgroup id in a 1D grid
    ty = hsa.get_group_id(0)
    # workgroup size, i.e. number of workitem per workgroup
    bw = hsa.get_local_size(0)
    # Compute flattened index inside the array
    pos = tx + ty * bw
    # The above is equivalent to pos = hsa.get_global_id(0)
    if pos < an_array.size:  # Check array boundaries
        an_array[pos] += 1


Unless you are sure the workgroup size and grid size is a divisor of your array size, you must check boundaries as shown above.

get_local_id(), get_local_size(), get_group_id() and get_global_id() are special functions provided by the HSA backend for the sole purpose of knowing the geometry of the thread hierarchy and the position of the current workitem within that geometry.


Takes the index of the dimension being queried

Returns local workitem ID in the the current workgroup for the given dimension. For 1D workgroup, the index is an integer spanning the range from 0 inclusive to numba.hsa.get_local_size() exclusive.


Takes the index of the dimension being queried

Returns the size of the workgroup at the given dimension. The value is declared when instantiating the kernel. This value is the same for all workitems in a given kernel, even if they belong to different workgroups (i.e. each workgroups is “full”).


Takes the index of the dimension being queried

Returns the workgroup ID in the grid of workgroup launched a kernel.


Takes the index of the dimension being queried

Returns the global workitem ID for the given dimension. Unlike numba.hsa .get_local_id(), this number is unique for all workitems in a grid.