Hi Alexei, you wrote:

I did some performance tests with uBLAS and MTL. Here are some results for those who is interested. The following tests were run: 1) uBLAS dense matrix multiplication: - row_major * row_major: ures.assign(prod(ur, ur)) - row_major * column_major: ures.assign(prod(ur, uc)) - column_major * row_major: ures.assign(prod(uc, ur)) 2) MTL dense matrix multiplication: - row_major * row_major: mult(mr, mr, mres) - row_major * column_major: mult(mr, mc, mres) - column_major * row_major: mult(mc, mr, mres) 3) C matrix multiplication (basic multiplication algorithm for C array). 4) uBLAS sparse matrix multiplication (20% of non-zera elements): - row_major * row_major: ures.assign(prod(ur, ur)) - row_major * column_major: ures.assign(prod(ur, uc)) - column_major * row_major: ures.assign(prod(uc, ur)) ures - is dense matrix. 5)MTL sparse marix multiplication (20% of non-zera elements): - row_major * row_major: mult(mr, mr, mres) - row_major * column_major: mult(mr, mc, mres) - column_major * row_major: mult(mc, mr, mres) mres - is dense matrix.

All tests were run on Windows2000, I used gcc 3.2 with -O3 optimization flag. boost 1.29 was used.

Here are some results: 1) uBLAS and MTL have approximately the same performance for dense matrix mult. uBLAS is a bit faster with small matrices (< 50-100), MTL is faster with large ones (>100). 2) When working with C array multiplication it is 5-6 times faster than both uBLAS and MTL. (!!!)

This is too much abstraction penalty for uBLAS. Did you define -DNDEBUG (enabling expression templates and disabling bounds and type checks)?

3) If I use my own simple mult funcion to mutiply uBLAS or MTL matrices:

template <typename Mat> void mat_mat_mult(const Mat& m1, const Mat& m2, Mat& res, int rank) { for(int i = 0; i < rank; ++i) for(int j = 0; j < rank; ++j) { res(i, j) = 0; for(int k = 0; k < rank; ++k) res(i, j) += m1(i, k) * m2(k, j); } }

It works 2 times faster than native uBLAS and MTL implementations of multiplication. (Iterators overhead?)

uBLAS normally doesn't use iterators when multiplying dense matrices.

4) Dense matrix performance doesn't depend on row orientation neither for uBLAS nor for MTL.

Maybe if your matrices fit into the cache, otherwise one probably should use blocked operations.

5) MTL sparse matrix mult doesn't depend on row orientation and in any case much (2 times) faster than the best case for uBLAS. 6) uBLAS gives best performance for sparse matrices in row_major * column_major case (it is 3 times faster than column_major * row_major). 7) When I tryed latest uBLAS from boost cvs. It worked much (up to 2 times) slower for sparse matrices than 1.29.

There are more debug runtime checks for sparse matrices now (see the remark regarding NDEBUG).

I would need to do other tests to make any real conclusions, but preliminary it seams to me that abstraction penalty for both uBLAS and MTL is too big.

Best regards Joerg