We present several fast, practical linear-complexity scheduling algorithms that enable provision of various quality-of-service (QoS) guarantees in an input-queued switch with no speedup. Specii-cally, our algorithms provide per-virtual-circuit transmission rate and cell delay guarantees using a credit-based bandwidth reservation scheme. Our algorithms also provide approximate max-min-fair shari...

In order to simulate tsunamis, the Chair of Scientific Computing of the TUM developed the Shallow Water Equations Teaching Code, which uses an augmented Riemann solver, amongst others. A characteristic of the augmented Riemann solver is its varying processing time, which depends on its input parameters like the water height or the bathymetry. Due to this variation, when the simulation is calcul...

We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints. Sufficient conditions are provided, under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. We further extend the proposed algorithm to an asynchronous parallel variant with a near linear spe...

Hardware implementation of data compression algorithms is receiving increasing attention due to exponentially expanding network traffic and digital data storage usage. In this paper, we propose several serial one-dimensional and parallel two-dimensional systolic-arrays for Lempel–Ziv data compression. A VLSI chip implementing our optimal linear array is fabricated and tested. The proposed array...