Automated enumeration of block cipher differentials: An optimized branch-and-bound GPU framework

Block ciphers are prevalent in various security protocols used daily such as TLS, OpenPGP, and SSH. Their primary purpose is the protection of user data, both in transit and at rest. One of the de facto methods to evaluate block cipher security is differential cryptanalysis. Differential cryptanalysis observes the propagation of input patterns (input differences) through the cipher to produce output patterns (output differences). This probabilistic propagation is known as a differential; the identification of which is a measure of a block cipher’s security margins. This paper introduces an optimized GPU-based branch-and-bound framework for differential search. We optimize search efficiency by parallelizing all branch-and-bound operations, completing the entire search on the GPU without communicating with the CPU. The meet-in-the-middle (MITM) approach is also adopted for further performance gains. We analyze the financial and computational costs of the proposed framework using Google Cloud VM to showcase its practicality. When optimized for performance, we can attain up to 90x speedup while saving up to 47% of the running cost as compared to a single CPU core. When optimized for cost, the proposed framework can save up to 83% of financial costs while retaining a speedup of up to 40x. As a proof of concept, the proposed framework was then applied on 128-bit TRIFLE-BC, 64-bit PRESENT, and 64-bit GIFT. Notably, we identified the best differentials for PRESENT (16 rounds) and 64-bit GIFT (13 rounds) to date, with estimated probabilities of $2^{-61.7964}$ and $2^{-60.66}$ respectively. Although the differential results for TRIFLE-BC were incremental, the proposed framework was able to construct differentials for 43 rounds that consisted of approximately 5.8x more individual trails than previous work, making it one of the most efficient approaches for larger block ciphers

Optimisation massivement multi-tâche sur grappes de calcul hétérogènes – Application aux problèmes de permutation

Branch-and-Bound (B&B) is a frequently used tree-search exploratory method for the exact resolution of combinatorial optimization problems (COPs). However, in practice, only small problem instances can be solved on a sequential computer, as B&B generates often generates a huge amount of subproblems to be evaluated. In order to solve large COPs, we revisit the design and implementation of massively parallel B&B on top of large heterogeneous clusters, integrating multi-core CPUs, many-core processors and GPUs.For the efficient storage and management of subproblems an original data structure (IVM) dedicated to permutation problems is used. Because of the highly irregular and unpredictable shape of the B&B tree, dynamic load balancing between parallel exploration processes is one of the main issues addressed in this thesis. Based on a compact encoding of the search space in the form of intervals, work stealing strategies for multi-core and GPU are proposed, as well as hierarchical approaches for load balancing in distributed memory multi-CPU/multi-GPU systems. Three permutation problems, the Flowshop Scheduling Problem (FSP), the Quadratic Assignment Problem (QAP) and the n-Queens puzzle problem are used as test-cases.The resolution, in 9 hours, of a FSP instance with an estimated sequential execution time of 22 years demonstrates the scalability of the proposed algorithms on a cluster composed of 36 GPUs.L'algorithme Branch-and-Bound (B&B) est une méthode de recherche arborescente fréquemment utilisé pour la résolution exacte de problèmes d'optimisation combinatoire (POC). Néanmoins, seules des petites instances peuvent être effectivement résolues sur une machine séquentielle, le nombre de sous-problèmes à évaluer étant souvent très grand. Visant la resolution de POC de grande taille, nous réexaminons la conception et l'implémentation d'algorithmes B&B massivement parallèles sur de larges plateformes hétérogènes de calcul, intégrant des processeurs multi-coeurs, many-cores et et processeurs graphiques (GPUs). Pour une représentation compacte en mémoire des sous-problèmes une structure de données originale (IVM), dédiée aux problèmes de permutation est utilisée. En raison de la forte irrégularité de l'arbre de recherche, l'équilibrage de charge dynamique entre processus d'exploration parallèles occupe une place centrale dans cette thèse. Basés sur un encodage compact de l'espace de recherche sous forme d'intervalles, des stratégies de vol de tâches sont proposées pour processeurs multi-core et GPU, ainsi une approche hiérarchique pour l'équilibrage de charge dans les systèmes multi-GPU et multi-CPU à mémoire distribuée. Trois problèmes d'optimisation définis sur l'ensemble des permutations, le problème d'ordonnancement Flow-Shop (FSP), d'affectation quadratique (QAP) et le problème des n-dames sont utilisés comme cas d'étude. La resolution en 9 heures d'une instance du FSP dont le temps de résolution séquentiel est estimé à 22 ans demontre la capacité de passage à l'échelle des algorithmes proposés sur une grappe de calcul composé de 36 GPUs

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Operational research:methods and applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Operational Research: methods and applications

This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes