基准测试（使用BLAS的python vs. c ++）和（numpy） [英] Benchmarking (python vs. c++ using BLAS) and (numpy)
问题描述
我想编写一个广泛使用BLAS和LAPACK线性代数功能的程序。因为表演是一个问题，我做了一些基准测试，想知道，如果我采取的方法是合法的。
I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would like know, if the approach I took is legitimate.
我可以说，三个参赛者，它们的性能具有简单的矩阵矩阵乘法。参赛者是：
I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are:
- Numpy，仅使用
dot的功能。 - Python通过共享对象调用BLAS功能。
- C ++，通过共享对象调用BLAS功能。
- Numpy, making use only of the functionality of
dot. - Python, calling the BLAS functionalities through a shared object.
- C++, calling the BLAS functionalities through a shared object.
场景
我为不同尺寸实现了矩阵矩阵乘法 i 。 i 运行从5到500，增量为5，基数 m1 和 m2 设置如下：
Scenario
I implemented a matrix-matrix multiplication for different dimensions i. i runs from 5 to 500 with an increment of 5 and the matricies m1 and m2 are set up like this:
m1 = numpy.random.rand(i,i).astype(numpy.float32)
m2 = numpy.random.rand(i,i).astype(numpy.float32)
1。 Numpy
使用的代码如下：
1. Numpy
The code used looks like this:
tNumpy = timeit.Timer("numpy.dot(m1, m2)", "import numpy; from __main__ import m1, m2")
rNumpy.append((i, tNumpy.repeat(20, 1)))
2。 Python通过共享对象调用BLAS
使用函数
2. Python, calling BLAS through a shared object
With the function
_blaslib = ctypes.cdll.LoadLibrary("libblas.so")
def Mul(m1, m2, i, r):
no_trans = c_char("n")
n = c_int(i)
one = c_float(1.0)
zero = c_float(0.0)
_blaslib.sgemm_(byref(no_trans), byref(no_trans), byref(n), byref(n), byref(n),
byref(one), m1.ctypes.data_as(ctypes.c_void_p), byref(n),
m2.ctypes.data_as(ctypes.c_void_p), byref(n), byref(zero),
r.ctypes.data_as(ctypes.c_void_p), byref(n))
测试代码如下所示：
r = numpy.zeros((i,i), numpy.float32)
tBlas = timeit.Timer("Mul(m1, m2, i, r)", "import numpy; from __main__ import i, m1, m2, r, Mul")
rBlas.append((i, tBlas.repeat(20, 1)))
3 。 c ++，通过共享对象调用BLAS
现在，c ++代码自然就有点长了，所以我将信息减少到最小。
I load该函数带有
3. c++, calling BLAS through a shared object
Now the c++ code naturally is a little longer so I reduce the information to a minimum.
I load the function with
void* handle = dlopen("libblas.so", RTLD_LAZY);
void* Func = dlsym(handle, "sgemm_");
我用 gettimeofday 测量时间：
gettimeofday(&start, NULL);
f(&no_trans, &no_trans, &dim, &dim, &dim, &one, A, &dim, B, &dim, &zero, Return, &dim);
gettimeofday(&end, NULL);
dTimes[j] = CalcTime(start, end);
其中 j 。我计算的时间通过
where j is a loop running 20 times. I calculate the time passed with
double CalcTime(timeval start, timeval end)
{
double factor = 1000000;
return (((double)end.tv_sec) * factor + ((double)end.tv_usec) - (((double)start.tv_sec) * factor + ((double)start.tv_usec))) / factor;
}
结果
结果如下图所示：
Results
The result is shown in the plot below:
- 您认为我的做法是公平的，或者有一些不必要的开销，我可以避免？
- 你会期望结果会显示这么巨大的差异之间的c ++和python方法？
- 因为我宁愿在我的程序中使用python，在调用BLAS或LAPACK例程时，我该怎么做才能提高性能？
下载
完整的基准测试可以下载此处。 （JF Sebastian使该链接可能^^）
Download
The complete benchmark can be downloaded here. (J.F. Sebastian made that link possible^^)
推荐答案
我运行了您的基准。在我的机器上C ++和numpy之间没有区别：
I've run your benchmark. There is no difference between C++ and numpy on my machine:
你认为我的方法是公平的，还是有一些不必要的开销？ b $ b
Do you think my approach is fair, or are there some unnecessary overheads I can avoid?
由于结果没有差异，它似乎是公平的。
It seems fair due to there is no difference in results.
你会期望结果会在c ++和python方法之间显示如此巨大的差异吗？两者都使用共享对象进行计算。
Would you expect that the result would show such a huge discrepancy between the c++ and python approach? Both are using shared objects for their calculations.
否。
因为我宁愿在我的程序中使用python，在调用BLAS或LAPACK例程时，我该怎么做才能提高性能？
Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?
确保numpy在您的系统上使用BLAS / LAPACK库的优化版本。
Make sure that numpy uses optimized version of BLAS/LAPACK libraries on your system.
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