Dear Friends,
I am using the following hardware/software:
1. Intel GPU Iris Pro Graphics 5200
2. C++ (Visual Studio 2017) with Intel OpenCL SDK 2.0
3. MATLAB 2018
I have a doubt about my precision limits using this hardware. I know from its documentation that it supports only Compute Capability 1.2, which has more errors rounding floating points than other versions of Compute Capability (eg.: 2.0).
When I compute a Covariance matrix inside GPU (using C++/OpenCL) and compare to the same computation, using the same data and equation, done inside the CPU (using MATLAB), I get a mean error of around 10^(-9).
But when I compute a Matrix Inverse inside GPU and compare to the same computation, inside CPU, the error is around 10^(-2). And this is too big to give the same result at the final end of all computations.
I am using a Gauss-Jordan method to invert a matrix of size around 10(^4) cells.
Anybody has any experience with this situation which could help on how to solve the floating point precision problem?
thank you very much,
best regards,
Joao V. Dornas