Deterministic 1-k routing on meshes with applications to worm-hole routing

In $1$-$k$ routing each of the $n^2$ processing units of an $n \times n$ mesh connected computer initially holds $1$ packet which must be routed such that any processor is the destination of at most $k$ packets. This problem reflects practical desire for routing better than the popular routing of permutations. $1$-$k$ routing also has implications for hot-potato worm-hole routing, which is of great importance for real world systems. We present a near-optimal deterministic algorithm running in \sqrt{k} \cdot n / 2 + \go{n} steps. We give a second algorithm with slightly worse routing time but working queue size three. Applying this algorithm considerably reduces the routing time of hot-potato worm-hole routing. Non-trivial extensions are given to the general $l$-$k$ routing problem and for routing on higher dimensional meshes. Finally we show that $k$-$k$ routing can be performed in \go{k \cdot n} steps with working queue size four. Hereby the hot-potato worm-hole routing problem can be solved in \go{k^{3/2} \cdot n} steps

Sample sort on meshes

This paper provides an overview of lower and upper bounds for mesh-connected processor networks. Most attention goes to routing and sorting problems, but other problems are mentioned as well. Results from 1977 to 1995 are covered. We provide numerous results, references and open problems. The text is completed with an index. This is a worked-out version of the author's contribution to a joint paper with Grammatikakis, Hsu and Kraetzl on multicomputer routing, submitted to JPDC

A powerful heuristic for telephone gossiping

A refined heuristic for computing schedules for gossiping in the telephone model is presented. The heuristic is fast: for a network with n nodes and m edges, requiring R rounds for gossiping, the running time is O(R n log(n) m) for all tested classes of graphs. This moderate time consumption allows to compute gossiping schedules for networks with more than 10,000 PUs and 100,000 connections. The heuristic is good: in practice the computed schedules never exceed the optimum by more than a few rounds. The heuristic is versatile: it can also be used for broadcasting and more general information dispersion patterns. It can handle both the unit-cost and the linear-cost model. Actually, the heuristic is so good, that for CCC, shuffle-exchange, butterfly de Bruijn, star and pancake networks the constructed gossiping schedules are better than the best theoretically derived ones. For example, for gossiping on a shuffle-exchange network with 2^{13} PUs, the former upper bound was 49 rounds, while our heuristic finds a schedule requiring 31 rounds. Also for broadcasting the heuristic improves on many formerly known results. A second heuristic, works even better for CCC, butterfly, star and pancake networks. For example, with this heuristic we found that gossiping on a pancake network with 7! PUs can be performed in 15 rounds, 2 fewer than achieved by the best theoretical construction. This second heuristic is less versatile than the first, but by refined search techniques it can tackle even larger problems, the main limitation being the storage capacity. Another advantage is that the constructed schedules can be represented concisely

Towards practical permutation routing on meshes

We consider the permutation routing problem on two-dimensional $n \times n$ meshes. To be practical, a routing algorithm is required to ensure very small queue sizes $Q$, and very low running time $T$, not only asymptotically but particularly also for the practically important $n$ up to $1000$. With a technique inspired by a scheme of Kaklamanis/Krizanc/Rao, we obtain a near-optimal result: $T = 2 \cdot n + {\cal O}(1)$ with $Q = 2$. Although $Q$ is very attractive now, the lower order terms in $T$ make this algorithm highly impractical. Therefore we present simple schemes which are asymptotically slower, but have $T$ around $3 \cdot n$ for {\em all} $n$ and $Q$ between 2 and 8

Vertex labeling and routing in expanded Apollonian networks

We present a family of networks, expanded deterministic Apollonian networks, which are a generalization of the Apollonian networks and are simultaneously scale-free, small-world, and highly clustered. We introduce a labeling of their vertices that allows to determine a shortest path routing between any two vertices of the network based only on the labels.Comment: 16 pages, 2 figure

Near-Optimal Online Multiselection in Internal and External Memory

We introduce an online version of the multiselection problem, in which q selection queries are requested on an unsorted array of n elements. We provide the first online algorithm that is 1-competitive with Kaligosi et al. [ICALP 2005] in terms of comparison complexity. Our algorithm also supports online search queries efficiently. We then extend our algorithm to the dynamic setting, while retaining online functionality, by supporting arbitrary insertions and deletions on the array. Assuming that the insertion of an element is immediately preceded by a search for that element, we show that our dynamic online algorithm performs an optimal number of comparisons, up to lower order terms and an additive O(n) term. For the external memory model, we describe the first online multiselection algorithm that is O(1)-competitive. This result improves upon the work of Sibeyn [Journal of Algorithms 2006] when q > m, where m is the number of blocks that can be stored in main memory. We also extend it to support searches, insertions, and deletions of elements efficiently

Towards a sustainable Dunaliella salina microalgal biorefinery for 9-cis β-carotene production

Valorisation of the efficacy of 9-cis beta-carotene in treating atherosclerosis, psoriasis, and inhibiting atherogenesis and retinitis pigmentosa is becoming increasingly urgent, but supplies of 9-cis beta-carotene are scarce and this compound is difficult to synthesise chemically, unlike the much more common all-trans form. Innovative products, processes and services in an algal biorefinery that rely on renewable biological resources instead of fossil fuel alternatives offer the potential to lower the energy costs of traditional chemical processes and reduce carbon emissions, water usage and waste. In 2013, the European Commission supported development of 4 microalgal biorefinery projects to assess the potential for innovative approaches to tackle the major challenges intrinsic to the development of the algae biorefineries. One of these was the D-Factory (KBBE.2013.3.2-02) which sought to evaluate requirements for sustainable, industrial-scale production of Dunaliella salina and extraction of its carotenoids, especially 9-cis beta-carotene in a CO2 microalgae biorefinery. Here we present findings of the D-Factory project and propose a way forward for industrial-scale production of 9-cis beta-carotene using biotechnology based on Dunaliella salina biomass. Cultivation improvements are able to deliver more than double the current levels of productivity, with increased sustainability, whilst the use of natural hyper-accumulating carotenogenic strains combined with the use of red light to boost production of the beta-carotene pathway, will increase the relative concentration of 9-cis beta-carotene in extracts of carotenoids with consequent improvements in downstream processing. These developments pave the way for acquiring data for a Medicine Licence and prepare the market for entry of novel 9-cis beta-carotene products

External Selection

Sequential selection has been solved in linear time by Blum e.a. Running this algorithm on a problem of size $N$ with $N > M$, the size of the main-memory, results in an algorithm that reads and writes \go{N} elements, while the number of comparisons is also bounded by \go{N}. This is asymptotically optimal, but the constants are so large that in practice sorting is faster for most values of $M$ and $N$. This paper provides the first detailed study of the external selection problem. A randomized algorithm of a conventional type is close to optimal in all respects. Our deterministic algorithm is more or less the same, but first the algorithm builds an index structure of all the elements. This effort is not wasted: the index structure allows the retrieval of elements so that we do not need a second scan through all the data. This index structure can also be used for repeated selections, and can be extended over time. For a problem of size $N$, the deterministic algorithm reads $N + o(N)$ elements and writes only $o(N)$ elements and is thereby optimal to within lower-order terms