  Driven by the insatiable needs to process ever larger amount of data with
more complex models, modern computer processors and accelerators are beginning
to offer half precision floating point arithmetic support, and extremely
optimized special units such as NVIDIA TensorCore on GPU and Google Tensor
Processing Unit (TPU) that does half precision matrix-matrix multiplication
exceptionally efficiently. In this paper we present a large scale mixed
precision linear least square solver that achieves high accuracy using the low
precision TensorCore GPU. The mixed precision system consists of both
innovative algorithms and implementations, and is shown to be up to 14x faster
than single precision cuSOLVER at QR matrix factorization at large scale with
slightly lower accuracy, and up to 10x faster than double precision direct QR
least square solver with comparable accuracy.
