The Square Kilometre Array will be an amazing instrument for pulsar astronomy. While the full SKA will be sensitive enough to detect all pulsars in the Galaxy visible from Earth, already with SKA1, pulsar searches will discover enough pulsars to increase the currently known population by a factor of four, no doubt including a range of amazing unknown sources. Real time processing is needed to deal with the 60 PB of pulsar search data collected per day, using a signal processing pipeline required to perform more than 10 POps. Here we present the suggested design of the pulsar search engine for the SKA and discuss challenges and solutions to the pulsar search venture.The Square Kilometre Array (SKA) will be an excellent telescope for discovering and timing pulsars (see e.g. Keane et al., 2015;Baffa 2014; and contribution by E. Keane in these proceedings). Already the first stage of the telescope (SKA1) will more than quadruple the known population of pulsars, and once the full SKA is finished, it will be sensitive enough to find all pulsars in the Galaxy that are beaming towards Earth. Many of these will be exciting sources, encompassing all different types of pulsars, such as millisecond pulsars (MSPs), rotating radio transients, young pulsars, magnetars and pulsars in binary systems. In particular, the SKA will have excellent sensitivity to highly relativistic double neutron star systems as well as pulsars orbiting black holes. To enable the discovery of all these sources, it is essential to have a fast and stable search pipeline installed at the telescope. Here we outline our proposed search strategy, address the 1

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The memory requirements of digital signal processing and multimedia applications have grown steadily over the last several decades. From embedded systems to supercomputers, the design of computing platforms involves a balance between processing elements and memory sizes to avoid the memory wall. This paper presents an algorithm based on both dataflow and approximate computing approaches in order to find a good balance between the memory requirements of an application and the quality of the result. The designer of the computing system can use these evaluations early in the design process to make hardware and software design decisions. The proposed method does not require any modification in the algorithm's computations, but optimizes how data are fetched from and written to memory. We show in this paper how the proposed algorithm saves 23.4% of memory for the full SKA SDP signal processing computing pipeline, and up to 68.75% for a wavelet transform in embedded systems.

Many networked systems involve multiple modes of transport. Such systems are called multimodal, and examples include logistic networks, biomedical phenomena and telecommunication networks. Existing techniques for determining minimal paths in multimodal networks have either required heuristics or else application-specific constraints to obtain tractable problems, removing the multimodal traits of the network during analysis. In this paper weighted colored-edge graphs are introduced for modeling multimodal networks, where colors represent the modes of transportation. Minimal paths are selected using a partial order that compares the weights in each color, resulting in a Pareto set of minimal paths. Although the computation of minimal paths is theoretically intractable and [Formula: see text]-complete, the approach is shown to be tractable through experimental analyses without the need to apply heuristics or constraints.

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