Source-to-source compilation of loop programs for manycore processors

It is widely accepted today that the end of microprocessor performance growth based on increasing clock speeds and instruction-level parallelism (ILP) demands new ways of exploiting transistor densities. Manycore processors (most commonly known as GPGPUs or simply GPUs) provide a viable solution to this performance scaling bottleneck through large numbers of lightweight compute cores and memory hierarchies that rely primarily on software for their efficient utilization. The widespread proliferation of this class of architectures today is a clear indication that exposing and managing parallelism on a large scale as well as efficiently orchestrating on-chip data movement is becoming an increasingly critical concern for high-performance software development. In such a computing landscape performance portability -- the ability to exploit the power of a variety of manycore chips while minimizing the impact on software development and productivity -- is perhaps one of the most important and challenging objectives for our research community. This thesis is about performance portability for manycore processors and how source-to-source compilation can help us achieve it. In particular, we show that for an important set of loop-programs, performance portability is attainable at low cost through compile-time polyhedral analysis and optimization and parametric tiling for run-time performance tuning. In other words, we propose and evaluate a source-to-source compilation path that takes affine loop-programs as input and produces parametrically tiled parallel code amenable to run-time tuning across different manycore platforms and devices -- a very useful and powerful property if we seek performance portability because it decouples the compiler from the performance tuning process. The produced code relies on a platform-independent run-time environment, called Avelas, that allows us to formulate a robust and portable code generation algorithm. Our experimental evaluation shows that Avelas induces low run-time overhead and even substantial speed-ups for wavefront-parallel programs compared to a state-of-the-art compile-time scheme with no run-time support. We also claim that the low overhead of Avelas is a strong indication that it can also be effective as a general-purpose programming model for manycore processors as we demonstrate for a set of ParBoil benchmarks.Open Acces

Maximizing the Strong Triadic Closure in Split Graphs and Proper Interval Graphs

In social networks the Strong Triadic Closure is an assignment of the edges with strong or weak labels such that any two vertices that have a common neighbor with a strong edge are adjacent. The problem of maximizing the number of strong edges that satisfy the strong triadic closure was recently shown to be NP-complete for general graphs. Here we initiate the study of graph classes for which the problem is solvable. We show that the problem admits a polynomial-time algorithm for two unrelated classes of graphs: proper interval graphs and trivially-perfect graphs. To complement our result, we show that the problem remains NP-complete on split graphs, and consequently also on chordal graphs. Thus we contribute to define the first border between graph classes on which the problem is polynomially solvable and on which it remains NP-complete

Cluster Deletion on Interval Graphs and Split Related Graphs

In the Cluster Deletion problem the goal is to remove the minimum number of edges of a given graph, such that every connected component of the resulting graph constitutes a clique. It is known that the decision version of Cluster Deletion is NP-complete on (P_5-free) chordal graphs, whereas Cluster Deletion is solved in polynomial time on split graphs. However, the existence of a polynomial-time algorithm of Cluster Deletion on interval graphs, a proper subclass of chordal graphs, remained a well-known open problem. Our main contribution is that we settle this problem in the affirmative, by providing a polynomial-time algorithm for Cluster Deletion on interval graphs. Moreover, despite the simple formulation of the algorithm on split graphs, we show that Cluster Deletion remains NP-complete on a natural and slight generalization of split graphs that constitutes a proper subclass of P_5-free chordal graphs. Although the later result arises from the already-known reduction for P_5-free chordal graphs, we give an alternative proof showing an interesting connection between edge-weighted and vertex-weighted variations of the problem. To complement our results, we provide faster and simpler polynomial-time algorithms for Cluster Deletion on subclasses of such a generalization of split graphs

Adenocarcinoma of the third portion of the duodenum in a man with CREST syndrome

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Parameterized Aspects of Strong Subgraph Closure

Motivated by the role of triadic closures in social networks, and the importance of finding a maximum subgraph avoiding a fixed pattern, we introduce and initiate the parameterized study of the Strong F-closure problem, where F is a fixed graph. This is a generalization of Strong Triadic Closure, whereas it is a relaxation of F-free Edge Deletion. In Strong F-closure, we want to select a maximum number of edges of the input graph G, and mark them as strong edges, in the following way: whenever a subset of the strong edges forms a subgraph isomorphic to F, then the corresponding induced subgraph of G is not isomorphic to F. Hence the subgraph of G defined by the strong edges is not necessarily F-free, but whenever it contains a copy of F, there are additional edges in G to destroy that strong copy of F in G. We study Strong F-closure from a parameterized perspective with various natural parameterizations. Our main focus is on the number k of strong edges as the parameter. We show that the problem is FPT with this parameterization for every fixed graph F, whereas it does not admit a polynomial kernel even when F =P_3. In fact, this latter case is equivalent to the Strong Triadic Closure problem, which motivates us to study this problem on input graphs belonging to well known graph classes. We show that Strong Triadic Closure does not admit a polynomial kernel even when the input graph is a split graph, whereas it admits a polynomial kernel when the input graph is planar, and even d-degenerate. Furthermore, on graphs of maximum degree at most 4, we show that Strong Triadic Closure is FPT with the above guarantee parameterization k - mu(G), where mu(G) is the maximum matching size of G. We conclude with some results on the parameterization of Strong F-closure by the number of edges of G that are not selected as strong

Biogeography pattern of the marine angiosperm Cymodocea nodosa in the eastern Mediterranean Sea related to the quaternary climatic changes

Acknowledgments This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. The authors would like to thank M. Malandrakis and A. Lolas for their contribution to sampling.Peer reviewedPublisher PD