numba | 易学教程

Numba `cache=True ` has no effect

阅读更多关于 Numba `cache=True ` has no effect

问题 I write below code to test cache feature of numba import numba import numpy as np import time @numba.njit(cache=True) def sum2d(arr): M, N = arr.shape result = 0.0 for i in range(M): for j in range(N): result += arr[i,j] return result a=np.random.random((1000,100)) print(time.time()) sum2d(a) print(time.time()) print(time.time()) sum2d(a) print(time.time()) Though, there are some cache files generated in pycache folder, the timing is always the same like 1576855294.8787484 1576855295.5378428

How to use Numba to speed up sparse linear system solvers in Python that are provided in scipy.sparse.linalg?

阅读更多关于 How to use Numba to speed up sparse linear system solvers in Python that are provided in scipy.sparse.linalg?

问题 I wish to speed up the sparse system solver part of my code using Numba. Here is what I have up till now: # Both numba and numba-scipy packages are installed. I am using PyCharm IDE import numba import numba_scipy # import other required stuff @numba.jit(nopython=True) def solve_using_numba(A, b): return sp.linalg.gmres(A, b) # total = the number of points in the system A = sp.lil_matrix((total, total), dtype=float) # populate A with appropriate data A = A.tocsc() b = np.zeros((total, 1),

How to generalize fast matrix multiplication on GPU using numba

阅读更多关于 How to generalize fast matrix multiplication on GPU using numba

问题 Lately I've been trying to get into programming for GPUs in Python using the Numba library. I have been reading up on it on their website using the tutorial there and currently I'm stuck on their example, which can be found here: https://numba.pydata.org/numba-doc/latest/cuda/examples.html. I'm attempting to generalize the example for the fast matrix multiplication a bit (which is of the form A*B=C). When testing I noticed that matrices with dimensions that are not perfectly divisible by the

CUDA GPU processing: TypeError: compile_kernel() got an unexpected keyword argument 'boundscheck'

阅读更多关于 CUDA GPU processing: TypeError: compile_kernel() got an unexpected keyword argument 'boundscheck'

问题 Today I started working with CUDA and GPU processing. I found this tutorial: https://www.geeksforgeeks.org/running-python-script-on-gpu/ Unfortunately my first attempt to run gpu code failed: from numba import jit, cuda import numpy as np # to measure exec time from timeit import default_timer as timer # normal function to run on cpu def func(a): for i in range(10000000): a[i]+= 1 # function optimized to run on gpu @jit(target ="cuda") def func2(a): for i in range(10000000): a[i]+= 1 if _

How to use numba in Colaboratory

阅读更多关于 How to use numba in Colaboratory

问题 Anybody tried to use numba in google collaboratory? I just can not figure out how to set it up in this environment. At the moment, I'm stuck with the error library nvvm not found . 回答1: Copy this code into cell. It works for me. !apt-get install nvidia-cuda-toolkit !pip3 install numba import os os.environ['NUMBAPRO_LIBDEVICE'] = "/usr/lib/nvidia-cuda-toolkit/libdevice" os.environ['NUMBAPRO_NVVM'] = "/usr/lib/x86_64-linux-gnu/libnvvm.so" from numba import cuda import numpy as np import time

How do I know the maximum number of threads per block in python code with either numba or tensorflow installed?

阅读更多关于 How do I know the maximum number of threads per block in python code with either numba or tensorflow installed?

问题 Is there any code in python with either numba or tensorflow installed? For example, if I would like to know the GPU memory info, I can simply use: from numba import cuda gpus = cuda.gpus.lst for gpu in gpus: with gpu: meminfo = cuda.current_context().get_memory_info() print("%s, free: %s bytes, total, %s bytes" % (gpu, meminfo[0], meminfo[1])) in numba. But I can not find any code that gives me the maximum threads per block info. I would like the code to detect the maximum number of threads

用矩阵来处理数据—降维打击

阅读更多关于用矩阵来处理数据—降维打击

“同四维跌落到三维一样，三维空间也会向二维空间跌落，由一个维度蜷缩到微观中。那一小片二维空间的面积——它只有面积——会迅速扩大，这又引发了更大规模的跌落……我们现在就处在向二维跌落的空间中，最终，整个太阳系将跌落到二维，也就是说，太阳系将变成一副厚度为零的画。” 01 — 维度上面引用了三体的一段话，想探讨一下维度的事情。处理数据的时候，我们是习惯了将相同的性质的数据分门别类。有个很有意思的事情，既然大部分人认为四维是指时间，那么为什么不能说每一种分类方式就是一个维度。举个例子：注册规划师考四门，那就好比四个维度，有一百个注册规划师组成的列队，按他们原理、相关、法规、实务的分数进行排名，那就是四组数据；四组数据只能形成100行4列的二维矩阵；假设加上注册建筑师、注册结构师、注册暖通等等，又可以整理成一个三维矩阵；再按士、农、工、商等去区分，升级成一个四维矩阵。因为有太多的数据在我们面前，还有一句话叫“选择大于努力”。最近的研究项里其实就是要在大量数据中找到最理想的那组数据，因此我写了不少代码，在数据的运算过程中又发现，如果要在合理的时间内做出计算，那还得提高计算的速度，那就得用到numba、cpython等。 02 — 数据扩充选址大于努力的话，其实很多时候就是在做排列组合题。之前听东吴相对论里头有讲到，如果生活中有太多的事情压积在一起，那就先列举100件