基准测试(python 与 C++ 使用 BLAS)和(numpy)
Benchmarking (python vs. c++ using BLAS) and (numpy)(基准测试(python 与 C++ 使用 BLAS)和(numpy))
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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.由于性能是一个问题，我做了一些基准测试，想知道我采用的方法是否合法.
可以这么说，我有三个参赛者，想用简单的矩阵乘法来测试他们的表现.参赛选手是:
- 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.
场景
我为不同的维度实现了矩阵乘法i.i 从 5 运行到 500，增量为 5，矩阵 m1 和 m2 设置如下:
m1 = numpy.random.rand(i,i).astype(numpy.float32)m2 = numpy.random.rand(i,i).astype(numpy.float32)
1.麻木
使用的代码如下所示:
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
带功能
_blaslib = ctypes.cdll.LoadLibrary("libblas.so")def Mul(m1, m2, i, r):no_trans = c_char("n")n = c_int(i)一 = c_float(1.0)零 = 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:
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++代码自然要长一点，所以我把信息减到最少.
我用
加载函数
void* handle = dlopen("libblas.so", RTLD_LAZY);void* Func = dlsym(handle, "sgemm_");
我用 gettimeofday 测量时间是这样的:
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 是运行 20 次的循环.我计算过去的时间
double CalcTime(timeval start, timeval end){双倍系数 = 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:
问题
- 您认为我的方法公平吗，或者我可以避免一些不必要的开销?
- 您是否希望结果会显示 c++ 和 python 方法之间存在如此巨大的差异?两者都使用共享对象进行计算.
- 既然我更愿意在我的程序中使用 python，那么在调用 BLAS 或 LAPACK 例程时我可以做些什么来提高性能?
下载
可以在此处下载完整的基准测试.(J.F.塞巴斯蒂安使该链接成为可能^^)
解决方案
我已经运行了你的基准测试
一>.我机器上的 C++ 和 numpy 没有区别:
<块引用>
你认为我的方法公平吗，或者我可以避免一些不必要的开销?
由于结果没有差异，所以看起来很公平.
<块引用>
您是否期望结果会显示 c++ 和 python 方法之间存在如此巨大的差异?两者都使用共享对象进行计算.
没有
<块引用>
既然我更愿意在我的程序中使用 python，那么在调用 BLAS 或 LAPACK 例程时我可以做些什么来提高性能?
确保 numpy 在您的系统上使用优化版本的 BLAS/LAPACK 库.
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.
I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are:
- 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.
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
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, 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))
the test code looks like this:
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++, 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_");
I measure the time with gettimeofday like this:
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);
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;
}
Results
The result is shown in the plot below:
Questions
- Do you think my approach is fair, or are there some unnecessary overheads I can avoid?
- 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.
- Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?
Download
The complete benchmark can be downloaded here. (J.F. Sebastian made that link possible^^)
解决方案
I've run your benchmark. There is no difference between C++ and numpy on my machine:
Do you think my approach is fair, or are there some unnecessary overheads I can avoid?
It seems fair due to there is no difference in results.
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.
No.
Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?
Make sure that numpy uses optimized version of BLAS/LAPACK libraries on your system.
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