Hi, I am not sure whether it fits into the scope of the Multiprecision Library. In my graduation work I implemented a small library that did transformations on expression templates. The expression templates supported matrix operations. You could write something like result = A*b + C*d; where A and C are matrices and b and d are vector. If nothing is specified the expression is evaluated at the assignment. But one could also write: result[gmp<1024>] = A*b + C*d; result[k_fold<6>] = A*b + C*d; or even result[accurate] = A*b + C*d; gmp stands for the GMP backend, k_fold for k - fold precision of the source type. Implemented by k fold accumulations of errors. Accurate sum refers to a algorithm invented and published by S.M Rump T. Ogita et al in a paper called "Accurate Floating Point Summation". The accurate implementation executes the summations by traversing capturing only the values with relevant mantiassas in the current exponent regions, until no further values are contributed. As a result, "normal" input performs nearly as fast as a recursive sum. Ill conditioned input takes as many iterations as needed to reach a result in the source precision. Well, in my tests k_fold<8> performed better than gmp, and accurate similar to k_fold<4> execution. So in my opinion if the range of 64 and 32 floating point is detailed enough tuning the calculations that are prone to ill conditioned floating point summations, is usually better than using a library that does floating point calculations with a higher precision. regards Andreas Pokorny