I have one macbook and one windows laptop both has intel integrated graphics, without Nvidia graphics. i can't install cupy [Exception: Your CUDA environment is invalid. Please check above error log.] any other option to execute python script on GPU?
😂😂😂😂😂😂 this code is run for 10**8 is 2.second,10**9 is 21 second, the data transferring time really takes only 0.1 s for 10**8 and 10**9 as under 1s .the real work is takes long the computation time is just happens suddednly because it is cpu happening not on gpu after the sieve done on gpu takes 1.9 s then only cpu capable of transferring data from gpu
Not all problems admit to parallel processing ... some problems are inherently sequential. here's an inherently parallel problem: if a pixel is inside a polygon, turn the pixel black. if you have 1,000,000 pixels and 1,000,000 processors, you can solve this problem really quickly. here's a problem that is not a parallel problem: compute f(27.194) where f(x) = cos(x^3 + 2x^2 + 13) But even if a problem is admits to parallel processing on GPUs; GPUs don't run Python. But you can translate a C program, into steps that WILL run on a GPU. Sometimes Python is just the *WRONG* language to use, to solve a problem. .
I have one macbook and one windows laptop both has intel integrated graphics, without Nvidia graphics. i can't install cupy [Exception: Your CUDA environment is invalid. Please check above error log.] any other option to execute python script on GPU?
You cannot done on integrated graphics
Hi, I tried edit this code to get max of an array using cuda but it does not work and I dont know how to fix it
can you please help me ?
well explained concepts and code
Wow, a really interesting video. I whish the code was givven so that I don't have to copy it from the screen. Thanks a lot for this tutorial.
very helpful..thanks
Glad it was helpful!
So, GPU is 5000x faster right? That's an estimate we've got
😂😂😂😂😂😂 this code is run for 10**8 is 2.second,10**9 is 21 second, the data transferring time really takes only 0.1 s for 10**8 and 10**9 as under 1s .the real work is takes long the computation time is just happens suddednly because it is cpu happening not on gpu after the sieve done on gpu takes 1.9 s then only cpu capable of transferring data from gpu
God damn it, I can't fucking install it in PyCharm.
Not all problems admit to parallel processing ... some problems are inherently sequential.
here's an inherently parallel problem: if a pixel is inside a polygon, turn the pixel black.
if you have 1,000,000 pixels and 1,000,000 processors, you can solve this problem really quickly.
here's a problem that is not a parallel problem: compute f(27.194) where f(x) = cos(x^3 + 2x^2 + 13)
But even if a problem is admits to parallel processing on GPUs; GPUs don't run Python.
But you can translate a C program, into steps that WILL run on a GPU.
Sometimes Python is just the *WRONG* language to use, to solve a problem.
.