pycuda | CUDA integration for Python , plus shiny features | GPU library
kandi X-RAY | pycuda Summary
kandi X-RAY | pycuda Summary
CUDA integration for Python, plus shiny features
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Configure the frontend
- Substitute variables in a file
- Compile a CUDA code into a CUDA module
- Get a configuration schema
- Add functionality
- Returns the device allocation
- Call post - processing
- Return a config schema
- Creates the options needed for the Boost C ++ compiler
- Search a list of filenames
- Compile a CUDA code
- Find the path to the python module
- Sets up the boost library if needed
- Continuously print out a delay
- Matrix multiplication op
- Substitute substitutions in a file
- Generates a concatenation kernel
- Hack for distutils
- Returns the kernel transpose kernel
- Convert a NumPy array to a NumPy array
- Convert nparray to a NumPy array
- Check git submodules
- Generate random numpy array
- Rotate an image
- Construct a put kernel
- Make a function that returns a unary array - like function
- Get a reduction kernel for a given stage
- Create a default context
- Run the GPU
pycuda Key Features
pycuda Examples and Code Snippets
Lines of Code : 1License : Permissive (MIT)
Copypip3 install -U pycuda --user
for index, row in df.iterrows():
s1 = set(df.iloc[index]['prop'])
if temp in s1:
df.iat[index, df.columns.get_loc('prop')] = 's'
df = pd.DataFrame({'temp': ['re'] * 7,
'prop': [[mod = SourceModule(code)
myRand = mod.get_function("myRand")
mod = SourceModule(code, no_extern_c=True)
myRand = mod.get_function("_Z6myRandPf")
import numpy as np
import pycuda.autoinit
frcuMemAllocPitch ( CUdeviceptr* dptr,
size_t* pPitch,
size_t WidthInBytes,
size_t Height,
unsigned int ElementSizeBytes )
cuda.mem_alloc_pkernel_code_template = """
__global__ void MatrixMulKernel(float *a,float *b,float *c){
int tx = threadIdx.x;
int ty = threadIdx.y;
float Pvalue = 0;
for(int i=0; i<%(N)s; ++i){
float Aelement = a[ty * %(N)s + i]shared[tid] = values[tid];
BLOCK_SIZE = N
dest[ty * img_size + tx] += a[ty * img_size + tx_kernel] / ((float) kernel_size);
dest[ty * img_size + tx_kernel] += a[ty * img_size + tx] / ((float) kernel_size);
atomicAdd(&(dest[typip install pipwin
pipwin install pycuda
$ conda install cudatoolkit
$ cuda-memcheck python ./idontthinkso.py
========= CUDA-MEMCHECK
========= Error: process didn't terminate successfully
========= Fatal UVM CPU fault due to invalid operation
========= during write access to address 0x703bc1000
=======Community Discussions
Trending Discussions on pycuda
QUESTION
I've trained a quantized model (with help of quantized-aware-training method in pytorch). I want to create the calibration cache to do inference in INT8 mode by TensorRT. When create calib cache, I get the following warning and the cache is not created:
...ANSWER
Answered 2022-Mar-14 at 21:20If the ONNX model has Q/DQ nodes in it, you may not need calibration cache because quantization parameters such as scale and zero point are included in the Q/DQ nodes. You can run the Q/DQ ONNX model directly in TensorRT execution provider in OnnxRuntime (>= v1.9.0).
QUESTION
The title says it all, but here is my problem in more detail: I'm implementing a finite elements solver in python + pycuda that should run on distributed systems.
To hide the communication latency, I'm trying to overlap computation and communication (with 2 separate streams). My problem is that the kernels used for the communication (on one stream) are executed at the end of the main computation kernel (see pic below).
My question is: how can I tell my GPU to first execute the communication kernels?
I'm using a RTX2060M, so stream priority is supported, and the presence of the attribute STREAM_PRIORITIES_SUPPORTED in pycuda makes me think that it's possible to set stream priorities from pycuda.
ANSWER
Answered 2022-Feb-28 at 12:09It appears that at the date of writing (February 2022), PyCUDA has not implemented stream creation with priorities. So while what you want to do can be done with the CUDA driver API (which PyCUDA uses), that feature is not presently exposed in PyCUDA.
QUESTION
import math # all the libraries i import
import numpy as np
!pip install pycuda
import pycuda.gpuarray as gpu
import pycuda.cumath as cm
import pycuda.autoinit
import pycuda.driver as drv
from pycuda.compiler import SourceModule
ANSWER
Answered 2021-Dec-23 at 23:06This is not how you use cumath.
cumath functions like exp take an array argument, and perform the work on that array. There is no need for the doubly-nested for-loops.
so:
math.exp takes an argument and raises e to the power of that argument.
cumath.exp takes an input array, and returns an array of the same shape, where each element of the returned array is e raised to the power of the corresponding element in the input array.
Here is a trivial example:
QUESTION
Can I use the built-in vector type float3 that exists in Cuda documentation with Numba Cuda? I know that is possible to use with PyCuda, for example, a kernel like:
...ANSWER
Answered 2021-Oct-06 at 04:28Can I use the built-in vector type float3 that exists in Cuda documentation with Numba Cuda?
No, you cannot.
Numba CUDA Python inherits a small subset of supported types from Numba's nopython mode. But that is all. There are a lot of native CUDA features which are not exposed by Numba (at October 2021). Textures, video SIMD instructions and vector types are amongst them.
QUESTION
I am new to PyCUDA and trying to implement the Odd-even sort using PyCUDA.
I managed to run it successfully on arrays whose size is limited by 2048 (using one thread block), but as soon as I tried to use multiple thread blocks, the result was no longer correct. I suspected this might be a synchronization problem but had no idea how to fix it.
...ANSWER
Answered 2021-Aug-03 at 01:44Assembling comments into an answer:
odd-even sort can't be easily/readily extended beyond a single threadblock (because it requires synchronization) CUDA
__syncthreads()only synchronizes at the block level. Without synchronization, CUDA specifies no particular order to thread execution.for serious sorting work, I recommend a library implementation such as cub. If you want to do this from python I recommend cupy.
CUDA has a sample code that demonstrates odd-even sorting at the block level, but because of the sync issue it chooses a merge method to combine results
it should be possible to write an odd-even sort kernel that only does a single swap, then call this kernel in a loop. The kernel call itself acts as a device-wide synchronization point.
alternatively, it should be possible to do the work in a single kernel launch using cooperative groups grid sync.
none of these methods are likely to be faster than a good library implementation (which won't depend on odd-even sorting to begin with).
QUESTION
I was wondering if anyone could help me with this problem that has been plaguing me.
I am currently using Qt Creator with verion 5.11.3 Qt on Ubuntu to build a project. Every time I try to build I get the error "gl.h: No such file or directory".
The error occurs next to the line in my code that says "#include
I have ran the following code as well and it did not change the outcome
...ANSWER
Answered 2021-Jul-26 at 18:58Install the OpenGL dev support:
QUESTION
I write a python program and in that program I need to check if a given value is in a column of the given dataset. To do so I need to iterate over each row and to check equality for the column in each row. It takes a lot of time therefore I want to run it in GPU. I have experience in CUDA C/C++ but not in PyCuda to parallelize it. Could anyone can help me to solve this problem?
...ANSWER
Answered 2021-Jul-15 at 20:34The motivation for this approach is a means to get out of the df.iterrows paradigm due to its relatively low speed. While it might be possible to split into a dask dataframe and execute some kind of parallel apply function, I think that a vectorised approach is acceptably quick due to Numpy/Pandas vectorised operation performance advantages (depicted below).
The way I interpret this code is basically "In the prop column if the variable temp is in a list in that column, set the prop column to 's'".
QUESTION
I have seen many ways to generate an array of random numbers. but I want to generate a single random number. Is there any function as rand() in c++. I don't want a series of random numbers. I just need to generate a random number inside the kernel. is there any builtin function to generate random numbers? I have tried the given code below, but it not working.
...ANSWER
Answered 2021-Jun-29 at 09:39you can import random in python . and use random.randint(). to generate random number in specified range by defining range in function. exrandom.randint(0,50)
QUESTION
i've recently been trying out PyCuda.
I currently want to do somthing very simple, allocate some memory. Im assuming i have some fundamental misunderstanding because this is quite a simple task. My understanding is that with the code below i am create a 2d Cuda array 512 wide, 160 high and an elementsize of 1 byte.
Heres some test code below.
...ANSWER
Answered 2021-Jun-11 at 11:02Quoting from the CUDA driver API documentation
QUESTION
when I try to run an example of Matrix multiplication by pycuda.
...ANSWER
Answered 2021-May-14 at 08:46I think you are mixing syntaxes, % with .format string substituions. Check here for a nice summary: https://pyformat.info/
Now I spot the error (line 11): %[M]s --> %(M)s
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install pycuda
You can use pycuda like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
