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DEBUG: improve displaying complex data types #329
Consider improving support of complex data types. With “vanilla” Numba, debugger can only analyse the contents of simple types, such as integers and doubles. But with a numpy array, debugger output starts to be difficult for an average user. For an example, see https://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#running-with-gdb-enabled, in particular how the debugger shows the variable “c” (which is a numpy array). We should aim to show such types as single objects, similar to how we do it with C++ class objects.
High-level objective
Fix bugs
Double function call in Numba, wrapper or wrong debug info generation. Blocker for backtrace and local variables.
Debugging features to support
Fulfill gap with Numba functionality
Other features
Consider improving support of complex data types. With “vanilla” Numba, debugger can only analyse the contents of simple types, such as integers and doubles. But with a numpy array, debugger output starts to be difficult for an average user. For an example, see https://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#running-with-gdb-enabled, in particular how the debugger shows the variable “c” (which is a numpy array). We should aim to show such types as single objects, similar to how we do it with C++ class objects.
Single kernel function, offloaded:
A kernel function calling another offloaded function:
Variables: #338
info args
#437Overwrite the value of a variable (command “print variable = <new_value>”)
Non-functional requirements
Additional
Numba 0.55 (#654)
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