pycuda

Using Pycuda Multiple Threads

由 家住魔仙堡 提交于 2019-12-10 12:16:07
问题 I'm trying to run multiple threads on GPUs using the Pycuda example MultipleThreads. When I run my python file, I get the following error message: (/root/anaconda3/) root@109c7b117fd7:~/pycuda# python multiplethreads.py Exception in thread Thread-5: Traceback (most recent call last): File "/root/anaconda3/lib/python3.6/threading.py", line 916, in _bootstrap_inner self.run() File "multiplethreads.py", line 22, in run test_kernel(self.array_gpu) File "multiplethreads.py", line 36, in test

Parallel programming approach to solve pandas problems

由 我与影子孤独终老i 提交于 2019-12-08 12:00:41
问题 I have a dataframe of the following format. df A B Target 5 4 3 1 3 4 I am finding the correlation of each column (except Target) with the Target column using pd.DataFrame(df.corr().iloc[:-1,-1]) . But the issue is - size of my actual dataframe is (216, 72391) which atleast takes 30 minutes to process on my system. Is there any way of parallerize it using a gpu ? I need to find the values of similar kind multiple times so can't wait for the normal processing time of 30 minutes each time. 回答1:

Efficient method to check for matrix stability in CUDA

由 时光总嘲笑我的痴心妄想 提交于 2019-12-07 18:38:53
问题 A number of algorithms iterate until a certain convergence criterion is reached (e.g. stability of a particular matrix). In many cases, one CUDA kernel must be launched per iteration. My question is: how then does one efficiently and accurately determine whether a matrix has changed over the course of the last kernel call? Here are three possibilities which seem equally unsatisfying: Writing a global flag each time the matrix is modified inside the kernel. This works, but is highly

How to profile PyCuda code in Linux?

由 痞子三分冷 提交于 2019-12-06 09:46:21
I have a simple (tested) pycuda app and am trying to profile it. I've tried NVidia's Compute Visual Profiler, which runs the program 11 times, then emits this error: NV_Warning: Ignoring the invalid profiler config option: fb0_subp0_read_sectors Error : Profiler data file '/home/jguy/proj/gpu/tdbp/pyArch/temp_compute_profiler_0_0.csv' does not contain profiler output.This can happen when: a) Profiling is disabled during the entire run of the application. b) The application does not invoke any kernel launches or memory transfers. c) The application does not release resources (contexts, events,

Element wise function on pycuda::complex array

由 不羁岁月 提交于 2019-12-06 09:21:11
问题 I want to run a function on a large, 2D complex array (eventually 2* 12x2 *12 datapoints). However, pycuda does not work as expected. The ElementWise function doesn't work at 2d arrays, so I used the SourceModule function with block sizes. The problem is now that the C code on the GPU does not give the same result as the numpy calculation on the CPU. Very large and strange numbers are resulting. I'm using the following code. What's going wrong? #!/usr/bin/env python #https://github.com

driver.Context.synchronize()- what else to take into consideration — -a clean-up operation failed

由 夙愿已清 提交于 2019-12-05 19:14:29
I have this code here (modified due to the answer). Info 32 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads ptxas info : Used 46 registers, 120 bytes cmem[0], 176 bytes cmem[2], 76 bytes cmem[16] I don't know what else to take into consideration in order to make it work for different combinations of points "numPointsRs" and "numPointsRp" When ,for example, i run the code with Rs=10000 and Rp=100000 with block=(128,1,1),grid=(200,1) its fine. My computations: 46 registers*128threads=5888 registers . My card has limit 32768registers,so 32768/5888=5 +some => 5 block/SM (my card has

How do I feed a 2-dimensional array into a kernel with pycuda?

由 懵懂的女人 提交于 2019-12-05 07:01:41
问题 I have created a numpy array of float32s with shape (64, 128) , and I want to send it to the GPU. How do I do that? What arguments should my kernel function accept? float** myArray ? I have tried directly sending the array as it is to the GPU, but pycuda complains that objects are being accessed... 回答1: Two dimensional arrays in numpy/PyCUDA are stored in pitched linear memory in row major order by default. So you only need to have a kernel something like this: __global__ void kernel(float* a

How do I pass a 2-dimensional array into a kernel in pycuda?

由 耗尽温柔 提交于 2019-12-05 04:17:28
问题 I found an answer here, but it is not clear if I should reshape the array. Do I need to reshape the 2d array into 1d before passing it to pycuda kernel? 回答1: There is no need to reshape a 2D gpuarray in order to pass it to a CUDA kernel. As I said in the answer you linked to, a 2D numpy or PyCUDA array is just an allocation of pitched linear memory, stored in row major order by default. Both have two members which tell you everything that you need to access an array - shape and strides . For

Element wise function on pycuda::complex array

由 让人想犯罪 __ 提交于 2019-12-04 17:16:09
I want to run a function on a large, 2D complex array (eventually 2* 12x2 *12 datapoints). However, pycuda does not work as expected. The ElementWise function doesn't work at 2d arrays, so I used the SourceModule function with block sizes. The problem is now that the C code on the GPU does not give the same result as the numpy calculation on the CPU. Very large and strange numbers are resulting. I'm using the following code. What's going wrong? #!/usr/bin/env python #https://github.com/lebedov/scikits.cuda/blob/master/demos/indexing_2d_demo.py """ Demonstrates how to access 2D arrays within a

How do I pass a 2-dimensional array into a kernel in pycuda?

由 蹲街弑〆低调 提交于 2019-12-03 20:57:20
I found an answer here , but it is not clear if I should reshape the array. Do I need to reshape the 2d array into 1d before passing it to pycuda kernel? There is no need to reshape a 2D gpuarray in order to pass it to a CUDA kernel. As I said in the answer you linked to, a 2D numpy or PyCUDA array is just an allocation of pitched linear memory, stored in row major order by default. Both have two members which tell you everything that you need to access an array - shape and strides . For example: In [8]: X=np.arange(0,15).reshape((5,3)) In [9]: print X.shape (5, 3) In [10]: print X.strides (12
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