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https://stackoverflow.com/questions/74469770/how-to-implement-a-priority-queue-in-numba-where-one-element-of-the-item-is-a-li
4 replies
The function is fastest when I pass the namedtuples as separate arguments to the function, but this is not ideal and becomes very messy.
I have tried the following data types to store them to make them easier to use.
- numba list
- numba dict
- tuple
- structref
- namedtuple
But all are too slow or don't work (structref is the best but still over twice as slow)
Does anyone have any suggestions as to what I can use here to try and reflect the behavior of passing the objects separately?
11 replies
1 reply
>>> set_of_tuples = set()
>>> set_of_tuples.add((1, 1))
>>> set_of_tuples.add((1, 0))
>>> (1,1) in set_of_tuples
True
Hi, I am trying to pass a global numba typed Dict containing strings (keys) and numpy objects (values) into a numba njit function. My goal is copying the same behaviour than native Python, yet I can't make it with the typed Dict.
Can I put a numpy function in a typed Dict (e.g. np.mean)?
Thanks a lot!
19 replies
test_cuda in the following snippet compiles? If not, how can I implement carray for numba cuda? I also have the same questions for cffi.from_buffer to get the pointer of a cuda array. Thanks!A = np.dtype([("x", "f4")], align=True)
A_t = nb.from_dtype(A)
@nb.cfunc(nt.float32(nt.CPointer(A_t)))
def test(o):
return nb.carray(o, 1)[0].x
@cuda.jit(nt.float32(nt.CPointer(A_t)), device=True)
def test_cuda(o):
return nb.carray(o, 1)[0].x
```---------------------------Loop invariant code motion---------------------------
Allocation hoisting:
No allocation hoisting found
Instruction hoisting:
loop #0:
Has the following hoisted:
$expr_out_var.56 = const(float64, 0.0)
Failed to hoist the following:
dependency: $parfor_index_tuple_var.57 = build_tuple(items=[Var($parforindex_50.179, <string>:2), Var($parforindex_51.181, <string>:3)])
loop #1:
Has the following hoisted:
$60load_global.7 = global(getSurfacePnt: CPUDispatcher(<function getSurfacePnt at 0x7f9e4a7094c0>))
$const68.11 = const(int, 0)
$const78.16 = const(int, 1)
Failed to hoist the following:
dependency: $54binary_subscr.5 = getitem(value=cutCedgeIdxList, index=$parfor__index_58.221, fn=<built-in function getitem>)
dependency: p = getitem(value=edge2ptIdxList, index=$54binary_subscr.5, fn=<built-in function getitem>)
dependency: $70binary_subscr.12 = static_getitem(value=p, index=0, index_var=$const68.11, fn=<built-in function getitem>)
dependency: $80binary_subscr.17 = static_getitem(value=p, index=1, index_var=$const78.16, fn=<built-in function getitem>)
dependency: $84call_function.19 = call $push_global_to_block.222(func, $72binary_subscr.13, $82binary_subscr.18, func=$push_global_to_block.222, args=[Var(func, render.py:200), Var($72binary_subscr.13, render.py:220), Var($82binary_subscr.18, render.py:220)], kws=(), vararg=None, varkwarg=None, target=None)
4 replies
2 replies
Arrow data and related computation. Any idea? I would like to contribute in this aspect, if we can achieve some kind of aggrement about which arrow parts could support, which parts are not.
Hi, I just found about about numba, and I'm wondering how should I use it if my code dependends on a third party library (which is in pure Python).
More specifically, my code is implemented as a class, which contains some method I'd like to to JIT compile, but these methods make calls to some methods of a wrapped class which comes from a library.
Is there a way to make the jit annotation act "recursively" on called methods and/or wrapped classes?
2 replies