Yunanistan' ın Türkiye' ye yönelik birleşme stratejisinde Avrupalılaşmanın etkisi

137 pagesTürk ve Yunan taraflarının yeniden yakınlaşmasında her iki tarafın da adımlar attığı bilinmektedir. Ancak bu çalışma tekrar yakınlaşma için Yunan tarafının çabaları ile sınırlı tutulmuştur. Yunan dış politikasının Avrupalılaşması ve Türkiye'ye karşı Yunanistan'ın Birleşme Stratejisi üzerine çok mürekkep harcandı ve çok sayıda literatür oluşturuldu. Bu tezin amacı, bu ikisi arasındaki bağlantıyı kurmaktır. Avrupalılaşma, Avrupa Birliği üyelik stratejisini nasıl etkiliyor ve nasıl şekil veriyor Avrupa Birliği ailesine üye olmayan ve revizyonist istekleri olduğu düşünülen bir ülkenin bu stratejiyi benimsemede Avrupalılaşmanın etkisi nasıldır Yunan dış politikasına şekil verenlerin gözünden hali hazırdaki strateji, güvenlik endişeleri yönünden neden uygun bir çözüm olarak görülüyor Bu politikanın stratejik önemi olduğunu göz önünde bulundurarak, bu kararın arasındaki göstergelerin vurgulanması ve stratejinin nasıl yorumlandığının ve izlendiğinin gösterilmesi tezin amacıdır.Although it is assumed that the improved atmosphere in Greek-Turkish relations since 1999 is composed of the initiatives undertaken by both sides the paper self-consciously restricts its case-study to the contribution to this evolution on behalf of the Greek side. Europeanization of Greek Foreign Policy and Greeces Engagement Strategy towards Turkey during the last decade constitute topics in the name of which a lot of literature has been developed. Aim of this thesis is to find out and establish a link between them. How is Europeanization able to influence and shape the formulation of an EU member state strategy How can it contribute to the adoption of a strategy especially in cases where the targeted state does not belong to the EU family and is perceived to hold revisionist aspirations Why does the currently employed strategy seem according to the todays Greek Foreign Policy Makers the proper solution to deal with their security concerns Assuming that the adoption of this policy is of strategic importance the author is interested to highlight the indicator behind this decision and to illustrate the way this strategy was interpreted and pursued

Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures

We introduce Arbor, a performance portable library for simulation of large networks of multi-compartment neurons on HPC systems. Arbor is open source software, developed under the auspices of the HBP. The performance portability is by virtue of back-end specific optimizations for x86 multicore, Intel KNL, and NVIDIA GPUs. When coupled with low memory overheads, these optimizations make Arbor an order of magnitude faster than the most widely-used comparable simulation software. The single-node performance can be scaled out to run very large models at extreme scale with efficient weak scaling. HPC, GPU, neuroscience, neuron, softwareComment: PDP 2019 27th Euromicro International Conference on Parallel, Distributed and Network-based Processin

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Key discourses behind the maritime disputes

Presentation given at the online conference Talking the Cyprus Issue Togehter : Maritime Disputes in the Eastern Mediterranean - Session

Optimizing the sparse matrix-vector multiplication kernel for modern multicore computer architectures

This thesis focuses on the optimization of the Sparse Matrix-Vector Multiplication kernel (SpMV) for modern multicore architectures. We perform an in-depth performance analysis of the kernel and identified its major performance bottlenecks. This allows us to propose an advanced storage format for sparse matrices, the Compressed Sparse eXtended (CSX) format, which targets specifically the minimization of the memory footprint of the sparse matrix. This format provides significant improvements in the performance of the SpMV kernel in a variety of matrices and multicore architectures, maintaining considerable performance stability. Finally, we investigate the performance of the SpMV kernel from an energy-efficiency perspective, in order to identify the execution configurations that lead to optimal performance-energy tradeoffs.Η διατριβή αυτή εστιάζει στην βελτιστοποίηση του υπολογιστικού πυρήνα Πολλαπλασιασμού Αραιού Πίνακα με Διάνυσμα (SpMV) στις σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών. Πραγματοποιούμε μία εις βάθος ανάλυση του πυρήνα και αναδεικνύουμε τα κυριώτερα προβλήματα επίδοσης. Αυτό μας επιτρέπει να προτείνουμε ένα προχωρημένο σχήμα αποθήκευσης αραιών πινάκων, το σχήμα Compressed Sparse eXtended (CSX), το οποίο εστιάζει στοχευμένα στην ελαχιστοποίηση του μεγέθους αναπαράστασης του αραιού πίνακα. Το σχήμα αυτό μπορεί να παράσχει σημαντική βελτίωση της επίδοσης του SpMV σε μία πληθώρα διαφορετικών πινάκων και πολυπύρηνων αρχιτεκτονικών, διατηρώντας μία σημαντική σταθερότητα στην επίδοση. Τέλος, μελετάμε την επίδοση του SpMV υπό το πρίσμα της ενεργειακής του απόδοσης, με σκοπό την αναγνώριση των ρυθμίσεων εκτέλεσης που οδηγούν σε βέλτιστους συμβιβασμούς επίδοσης-κατανάλωσης ενέργειας

Optimizing the Sparse Matrix-Vector Multiplication Kernel for modern multicore computer architectures

203 σ.Η διατριβή αυτή εστιάζει στην βελτιστοποίηση του υπολογιστικού πυρήνα Πολλαπλασιασμού Αραιού Πίνακα με Διάνυσμα (SpMV) στις σύγχρονες πολυπύρηνες αρχιτεκτονικές υπολογιστών. Πραγματοποιούμε μία εις βάθος ανάλυση του πυρήνα και αναδεικνύουμε τα κυριώτερα προβλήματα επίδοσης. Αυτό μας επιτρέπει να προτείνουμε ένα προχωρημένο σχήμα αποθήκευσης αραιών πινάκων, το σχήμα Compressed Sparse eXtended (CSX), το οποίο εστιάζει στοχευμένα στην ελαχιστοποίηση του μεγέθους αναπαράστασης του αραιού πίνακα. Το σχήμα αυτό μπορεί να παράσχει σημαντική βελτίωση της επίδοσης του SpMV σε μία πληθώρα διαφορετικών πινάκων και πολυπύρηνων αρχιτεκτονικών, διατηρώντας μία σημαντική σταθερότητα στην επίδοση. Τέλος, μελετάμε την επίδοση του SpMV υπό το πρίσμα της ενεργειακής του απόδοσης, με σκοπό την αναγνώριση των ρυθμίσεων εκτέλεσης που οδηγούν σε βέλτιστους συμβιβασμούς επίδοσης-κατανάλωσης ενέργειας.This thesis focuses on the optimization of the Sparse Matrix-Vector Multiplication kernel (SpMV) for modern multicore architectures. We perform an in-depth performance analysis of the kernel and identified its major performance bottlenecks. This allows us to propose an advanced storage format for sparse matrices, the Compressed Sparse eXtended (CSX) format, which targets specifically the minimization of the memory footprint of the sparse matrix. This format provides significant improvements in the performance of the SpMV kernel in a variety of matrices and multicore architectures, maintaining considerable performance stability. Finally, we investigate the performance of the SpMV kernel from an energy-efficiency perspective, in order to identify the execution configurations that lead to optimal performance-energy tradeoffs.Βασίλειος Κ. Καρακάση

A

comparative study of blocking storage methods for sparse matrices on multicore architecture

CSX: An Extended Compression Format for SpMV on Shared Memory Systems

The Sparse Matrix-Vector multiplication (SpMV) kernel scales poorly on shared memory systems with multiple processing units due to the streaming nature of its data access pattern. Previous research has demonstrated that an effective strategy to improve the kernel’s performance is to drastically reduce the data volume involved in the computations. Since the storage formats for sparse matrices include metadata describing the structure of non-zero elements within the matrix, we propose a generalized approach to compress metadata by exploiting substructures within the matrix. We call the proposed storage format Compressed Sparse eXtended (CSX). In our implementation we employ runtime code generation to construct specialized SpMV routines for each matrix. Experimental evaluation on two shared memory systems for 15 sparse matrices demonstrates significant performance gains as the number of participating cores increases. Regarding the cost of CSX construction, we propose several strategies which trade performance for preprocessing cost making CSX applicable both to online and offline preprocessing