Let's compare PyCUDA to CUDA in terms of simplicity in parallelization before we write our first PyCUDA program on PyCharm.
In the following table, we can explore the scope of PyCUDA with respect to CUDA so as to understand scenarios when PyCUDA could be advantageous to CUDA:
|
CUDA |
PyCUDA |
|
Based on C/C++ programming language |
Based on the Python programming language |
|
Uses C/C++ combined with specialized code to accelerate computations |
Uses Python for GPUs to interface CUDA and accelerate computations |
|
Reduction is a key feature in CUDA that is extremely important to maximize parallelization and efficiently harness threads. |
Reduction in PyCUDA is much simpler to use than CUDA, considering the significance of reduction. |
