leofang

import cupy as cp
from pycuda import gpuarray
import pycuda.autoinit  # this import creates a CUDA context!


def from_cupy(arr):
    shape = arr.shape
    dtype = arr.dtype

    # a dummy object always returns the underlying pointer address

leofang

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I hereby claim:

• I am leofang on github.
• I am leofang (https://keybase.io/leofang) on keybase.
• I have a public key ASCZZ6NP9Kjx8B_n1wSCGFk3VCeEqKI2g7CAiIrhaAKcsAo

To claim this, I am signing this object:

leofang

import sys
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

import numpy as np
import cupy as cp
from cupyx.time import repeat


shape = (256, 512, 512)

leofang

# Many Python libraries provide low-level CUDA support, but when it comes to interoperability 
# things get complicated. This script shows a simple example on converting a CUDA stream 
# created from CuPy's API to one that is compatible with Numba.


from numba import cuda
import cupy as cp


def stream_cupy_to_numba(cp_stream):

leofang

# build locally at the root of cthyb-ohmic:
# docker build --build-arg arg_build_time="$(date +"%b. %d, %T %Z, %Y")"  --squash -t leofang/cthyb-ohmic .
FROM centos:latest

# get cthyb-ohmic; this layer will expose the source, so remember to squash when building!
ADD . /opt/cthyb-ohmic/

# get alps; note the pipe slot after tar
RUN curl -s http://alps.comp-phys.org/static/software/releases/alps-2.2.b3-r7462-src-with-boost.tar.gz | \
    tar -xzf - -C /opt/