最有效的方法来计算矩阵的每个元素的指数

Question

I'm migrating from Matlab to C + GSL and I would like to know what's the most efficient way to calculate the matrix B for which:

B[i][j] = exp(A[i][j])

where i in [0, Ny] and j in [0, Nx].

Notice that this is different from matrix exponential:

B = exp(A)

which can be accomplished with some unstable/unsupported code in GSL (linalg.h).

I've just found the brute force solution (couple of 'for' loops), but is there any smarter way to do it?

EDIT

Results from the solution post of Drew Hall

All the results are from a 1024x1024 for(for) loop in which in each iteration two double values (a complex number) are assigned. The time is the averaged time over 100 executions .

• Results when taking into account the {Row,Column}-Major mode to store the matrix:
  • 226.56 ms when looping over the row in the inner loop in Row-Major mode (case 1).
  • 223.22 ms when looping over the column in the inner loop in Row-Major mode (case 2).
  • 224.60 ms when using the gsl_matrix_complex_set function provided by GSL (case 3).

Source code for case 1 :

for(i=0; i<Nx; i++)
{
    for(j=0; j<Ny; j++)
    {
        /* Operations to obtain c_value (including exponentiation) */
        matrix[2*(i*s_tda + j)] = GSL_REAL(c_value);
        matrix[2*(i*s_tda + j)+1] = GSL_IMAG(c_value);
    }
}

Source code for case 2 :

for(i=0; i<Nx; i++)
{
    for(j=0; j<Ny; j++)
    {
        /* Operations to obtain c_value (including exponentiation) */
        matrix->data[2*(j*s_tda + i)] = GSL_REAL(c_value);
        matrix->data[2*(j*s_tda + i)+1] = GSL_IMAG(c_value);
    }
}

Source code for case 3 :

for(i=0; i<Nx; i++)
{
    for(j=0; j<Ny; j++)
    {
        /* Operations to obtain c_value (including exponentiation) */
        gsl_matrix_complex_set(matrix, i, j, c_value);
    }
}

Answer 1

There's no way to avoid iterating over all the elements and calling exp() or equivalent on each one. But there are faster and slower ways to iterate.

In particular, your goal should be to mimimize cache misses. Find out if your data is stored in row-major or column-major order, and be sure to arrange your loops such that the inner loop iterates over elements stored contiguously in memory, and the outer loop takes the big stride to the next row (if row major) or column (if column major). Although this seems trivial, it can make a HUGE difference in performance (depending on the size of your matrix).

Once you've handled the cache, your next goal is to remove loop overhead. The first step (if your matrix API supports it) is to go from nested loops (M & N bounds) to a single loop iterating over the underlying data (M N bound). You'll need to get a raw pointer to the underlying memory block (that is, a double rather than a double**) to do this.

Finally, throw in some loop unrolling (that is, do 8 or 16 elements for each iteration of the loop) to further reduce the loop overhead, and that's probably about as quick as you can make it. You'll probably need a final switch statement with fall-through to clean up the remainder elements (for when your array size % block size != 0).

Answer 2

不，除非有一些我没有听说过的奇怪的数学怪癖，你几乎只需要用两个for循环遍历元素。

Answer 3

If you just want to apply exp to an array of numbers, there's really no shortcut. You gotta call it (Nx * Ny) times. If some of the matrix elements are simple, like 0, or there are repeated elements, some memoization could help.

However, if what you really want is a matrix exponential (which is very useful), the algorithm we rely on is DGPADM . It's in Fortran, but you can use f2c to convert it to C. Here's the paper on it.

Answer 4

Since the contents of the loop haven't been shown, the bit that calculates the c_value we don't know if the performance of the code is limited by memory bandwidth or limited by CPU. The only way to know for sure is to use a profiler, and a sophisticated one at that. It needs to be able to measure memory latency, ie the amount of time the CPU has been idle waiting for data to arrive from RAM.

If you are limited by memory bandwidth, there's not a lot you can do once you're accessing memory sequentially. The CPU and memory work best when data is fetched sequentially. Random accesses hit the throughput as data is more likely to have to be fetched into cache from RAM. You could always try getting faster RAM.

If you're limited by CPU then there are a few more options available to you. Using SIMD is one option, as is hand coding the floating point code (C/C++ compiler aren't great at FPU code for many reasons). If this were me, and the code in the inner loop allows for it, I'd have two pointers into the array, one at the start and a second 4/5ths of the way through it. Each iteration, a SIMD operation would be performed using the first pointer and scalar FPU operations using the second pointer so that each iteration of the loop does five values. Then, I'd interleave the SIMD instructions with the FPU instructions to mitigate latency costs. This shouldn't affect your caches since (at least on the Pentium) the MMU can stream up to four data streams simultaneously (ie prefetch data for you without any prompting or special instructions).