@conference {896,
	title = {Comparing Hybrid CPU-GPU and Native GPU-only Acceleration for Linear Algebra},
	booktitle = {2015 SIAM Conference on Applied Linear Algebra},
	year = {2015},
	month = {2015-10},
	publisher = {SIAM},
	organization = {SIAM},
	address = {Atlanta, GA},
	abstract = {Accelerating dense linear algebra using GPUs admits two models: hybrid CPU-GPU and GPU-only. The hybrid model factors the panel on the CPU while updating the trailing matrix on the GPU, concentrating the GPU on high-performance matrix multiplies. The GPU-only model performs the entire computation on the GPU, avoiding costly data transfers to the CPU. We compare these two approaches for three QR-based algorithms: QR factorization, rank revealing QR, and reduction to Hessenberg.},
	author = {Mark Gates and Stanimire Tomov and Azzam Haidar}
}
