I'm trying to develop an intuition for how feasible/scalable machine learning algorithms are. The dominant cost is always matrix multiplications, but there seem to be no readily Google-able resource for explaining how to do back of the envelope calculations for matrix multiplication.
The specs of the machine I'm using: it has a 2.8GHz Ivy Bridge quad core processor with 8 Mb shared L3 cache, 5 GT/s bus speed, and 16 GB RAM. A stack overflow entry says Ivy Bridge has 8 DP flops/second throughput. How do I combine all these numbers to reach a rough estimate in seconds of how long it would take to multiply two double precision matrices of given sizes, assuming the matrices and their product can be stored in RAM all simultaneously?