Many data-parallel applications, including emerging media applications, have regular structures that can easily be expressed as a series of arithmetic kernels operating on data st...
Ujval J. Kapasi, William J. Dally, Scott Rixner, P...
We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic t...
—It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training...
Stride-based prefetching mechanisms exploit regular streams of memory accesses to hide memory latency. While these mechanisms are effective, they can be improved by studying the p...
We present quantitative models for the selection pressure of cellular evolutionary algorithms structured in two dimensional regular lattices. We derive models based on probabilisti...
Mario Giacobini, Enrique Alba, Andrea Tettamanzi, ...