Accurate inner product for vectors of middle to large dimension

The inner product is one of the most elementary algebraic operations and the basis for a large number of numerical applications and computations that are performed using binary floating-point arithmetic on computers. Depending on the condition of the input data, straight forward implementations of the inner product are very inaccurate and of limited use for verified computations. To overcome this issue, many algorithms have been developed in the past with different strengths and weaknesses. This Master’s Thesis introduces new algorithms for summation and inner product computation, that make use of the Fused Multiply-Add (FMA) instruction, which will be part of upcoming state of the art computer instruction sets. The proposed algorithms scale well for vector lengths of about 10³ elements and more.