The ARTIQ compiler transforms the Python code of the kernels into machine code executable on the core device. It is invoked automatically when calling a function that uses the @kernel decorator.

Supported Python features

A number of Python features can be used inside a kernel for compilation and execution on the core device. They include for and while loops, conditionals (if, else, elif), functions, exceptions, and statically typed variables of the following types:

  • Booleans

  • 32-bit signed integers (default size)

  • 64-bit signed integers (use numpy.int64 to convert)

  • Double-precision floating point numbers

  • Lists of any supported types

  • String constants

  • User-defined classes, with attributes of any supported types (attributes that are not used anywhere in the kernel are ignored)

For a demonstration of some of these features, see the example.

When several instances of a user-defined class are referenced from the same kernel, every attribute must have the same type in every instance of the class.

Remote procedure calls

Kernel code can call host functions without any additional ceremony. However, such functions are assumed to return None, and if a value other than None is returned, an exception is raised. To call a host function returning a value other than None its return type must be annotated using the standard Python syntax, e.g.:

def return_four() -> TInt32:
    return 4

The Python types correspond to ARTIQ type annotations as follows:








TInt32 or TInt64





list of T


NumPy array

TArray(T, num_dims)


TRange32, TRange64








The ARTIQ compiler accepts nearly a strict subset of Python 3. However, by necessity there is a number of differences that can lead to bugs.

Arbitrary-length integers are not supported at all on the core device; all integers are either 32-bit or 64-bit. This especially affects calculations that result in a 32-bit signed overflow; if the compiler detects a constant that doesn’t fit into 32 bits, the entire expression will be upgraded to 64-bit arithmetics, however if all constants are small, 32-bit arithmetics will be used even if the result will overflow. Overflows are not detected.

The result of calling the builtin round function is different when used with the builtin float type and the numpy.float64 type on the host interpreter; round(1.0) returns an integer value 1, whereas round(numpy.float64(1.0)) returns a floating point value numpy.float64(1.0). Since both float and numpy.float64 are mapped to the builtin float type on the core device, this can lead to problems in functions marked @portable; the workaround is to explicitly cast the argument of round to float: round(float(numpy.float64(1.0))) returns an integer on the core device as well as on the host interpreter.

Empty lists do not have valid list element types, so they cannot be used in the kernel.

Asynchronous RPCs

If an RPC returns no value, it can be invoked in a way that does not block until the RPC finishes execution, but only until it is queued. (Submitting asynchronous RPCs too rapidly, as well as submitting asynchronous RPCs with arguments that are too large, can still block until completion.)

To define an asynchronous RPC, use the @rpc annotation with a flag:

def record_result(x):

Additional optimizations

The ARTIQ compiler runs many optimizations, most of which perform well on code that has pristine Python semantics. It also contains more powerful, and more invasive, optimizations that require opt-in to activate.

Fast-math flags

The compiler does not normally perform algebraically equivalent transformations on floating-point expressions, because this can dramatically change the result. However, it can be instructed to do so if all of the following is true:

  • Arguments and results will not be not-a-number or infinity values;

  • The sign of a zero value is insignificant;

  • Any algebraically equivalent transformations, such as reassociation or replacing division with multiplication by reciprocal, are legal to perform.

If this is the case for a given kernel, a fast-math flag can be specified to enable more aggressive optimization for this specific kernel:

def calculate(x, y, z):
    return x * z + y * z

This flag particularly benefits loops with I/O delays performed in fractional seconds rather than machine units, as well as updates to DDS phase and frequency.

Kernel invariants

The compiler attempts to remove or hoist out of loops any redundant memory load operations, as well as propagate known constants into function bodies, which can enable further optimization. However, it must make conservative assumptions about code that it is unable to observe, because such code can change the value of the attribute, making the optimization invalid.

When an attribute is known to never change while the kernel is running, it can be marked as a kernel invariant to enable more aggressive optimization for this specific attribute.

class Converter:
    kernel_invariants = {"ratio"}

    def __init__(self, ratio=1.0):
        self.ratio = ratio

    def convert(self, value):
        return value * self.ratio ** 2

In the synthetic example above, the compiler will be able to detect that the result of evaluating self.ratio ** 2 never changes and replace it with a constant, removing an expensive floating-point operation.

class Worker:
    kernel_invariants = {"interval"}

    def __init__(self, interval=1.0*us):
        self.interval = interval

    def work(self):
        # something useful

class Looper:
    def __init__(self, worker):
        self.worker = worker

    def loop(self):
        for _ in range(100):
            delay(self.worker.interval / 5.0)

In the synthetic example above, the compiler will be able to detect that the result of evaluating self.interval / 5.0 never changes, even though it neither knows the value of self.worker.interval beforehand nor can it see through the function call, and hoist the expensive floating-point division out of the loop, transforming the code for loop into an equivalent of the following:

def loop(self):
    precomputed_delay_mu = self.core.seconds_to_mu(self.worker.interval / 5.0)
    for _ in range(100):