Obfuscating LLVM Intermediate Representation Source Code with NSGA-II

in 15th Intl. Conf. on Computational Intelligence in Security for Information Systems (CISIS'22) (2022, September)

With the generalisation of distributed computing paradigms to sustain the surging demands for massive processing and data-analytic capabilities, the protection of the intellectual property tied to the ... [more ▼]

With the generalisation of distributed computing paradigms to sustain the surging demands for massive processing and data-analytic capabilities, the protection of the intellectual property tied to the executed programs transferred onto these remote shared platforms becomes critical. A more and more popular solution to this problem consists in applying obfuscating techniques, in particular at the source code level. Informally, the goal of obfuscation is to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering on the program even with the help of computing resources. This allows to protect software against plagiarism, tampering, or finding vulnerabilities that could be used for different kinds of attacks. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation relying on the reference LLVM compiler infrastructure that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. The problem is defined as a Multi-Objective Combinatorial Optimization (MOCO) problem, where the goal is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo- assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Three different problem flavours are defined and solved with popular NSGA-II genetic algorithm. The promising results show the potential of the proposed technique. [less ▲]

Management of an Academic HPC Research Computing Facility: The ULHPC Experience 2.0

in 6th High Performance Computing and Cluster Technologies Conference (HPCCT 2022) (2022, July)

With the advent of the technological revolution and the digital transformation that made all scientific disciplines becoming computational, the need for High Performance Computing (HPC) has become and a ... [more ▼]

With the advent of the technological revolution and the digital transformation that made all scientific disciplines becoming computational, the need for High Performance Computing (HPC) has become and a strategic and critical asset to leverage new research and business in all domains requiring computing and storage performance. Since 2007, the University of Luxembourg operates a large academic HPC facility which remains the reference implementation within the country. This paper provides a general description of the current platform implementation as well as its operational management choices which have been adapted to the integration of a new liquid-cooled supercomputer, named Aion, released in 2021. The administration of a HPC facility to provide state-of-art computing systems, storage and software is indeed a complex and dynamic enterprise with the soul purpose to offer an enhanced user experience for intensive research computing and large-scale analytic workflows. Most design choices and feedback described in this work have been motivated by several years of experience in addressing in a flexible and convenient way the heterogeneous needs inherent to an academic environment towards research excellence. The different layers and stacks used within the operated facilities are reviewed, in particular with regards the user software management, or the adaptation of the Slurm Resource and Job Management System (RJMS) configuration with novel incentives mechanisms. In practice, the described and implemented environment brought concrete and measurable improvements with regards the platform utilization (+12,64%), jobs efficiency (average Wall-time Request Accuracy improved by 110,81%), the management and funding (increased by 10%). Thorough performance evaluation of the facility is also presented in this paper through reference benchmarks such as HPL, HPCG, Graph500, IOR or IO500. It reveals sustainable and scalable performance comparable to the most powerful supercomputers in the world, including for energy-efficient metrics (for instance, 5,19 GFlops/W (resp. 6,14 MTEPS/W) were demonstrated for full HPL (resp. Graph500) runs across all Aion nodes). [less ▲]

Les NFT en 40 questions: Des réponses claires et détaillées pour comprendre les Non Fungible Tokens

Book published by Dunod - 1st (2022)

Apparus en 2017, les jetons non fongibles ont généré en 2021 plusieurs millions d’euros et impacté de nombreux domaines allant de la mode aux paris hippique en passant par l’art et le sport. L’objectif de ... [more ▼]

Apparus en 2017, les jetons non fongibles ont généré en 2021 plusieurs millions d’euros et impacté de nombreux domaines allant de la mode aux paris hippique en passant par l’art et le sport. L’objectif de ce livre est d’expliquer comment fonctionnent les NFT et de présenter simplement leurs caractéristiques spécifiques. Ce livre est conçu pour répondre non seulement aux questions que vous vous posez sur l’univers des NFT… mais aussi à celles que vous ne vous étiez pas encore posées. Qu’est-ce qu’un contrat intelligent ? Quel a été le premier NFT ? Quels sont les jetons fongibles et non fongibles ? Comment les NFT révolutionnent-ils les titres de propriété ? Que peut-on acheter ou vendre grâce aux NFT ? Comment les NFT sont-ils utilisés dans l’art ? Qu’est-ce que le norme ERC-721 pour les NFT sur Ethereum ? Les réponses à toutes ces questions (et à 33 autres) sont dans ce livre. [less ▲]

A RNN-Based Hyper-Heuristic for Combinatorial Problems

in A RNN-Based Hyper-Heuristic for Combinatorial Problems (2022)

Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to ... [more ▼]

Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to tackle this issue and are mostly relying on Genetic Programming and its variants. Many attempts in the literature have shown that an automatic training mechanism for heuristic learning is possible and can challenge human-based heuristics in terms of gap to optimality. In this work, we introduce a novel approach based on a recent work on Deep Symbolic Regression. We demonstrate that scoring functions can be trained using Recurrent Neural Networks to tackle a well-know combinatorial problem, i.e., the Multi-dimensional Knapsack. Experiments have been conducted on instances from the OR-Library and results show that the proposed modus operandi is an alternative and promising approach to human- based heuristics and classical heuristic generation approaches. [less ▲]

The new AION cluster: Overview, Technical specifications and Capabilities

Presentation (2021, November)

RESIF 3.0: Toward a Flexible & Automated Management of User Software Environment on HPC facility

in ACM Practice and Experience in Advanced Research Computing (PEARC'21) (2021, July)

High Performance Computing (HPC) is increasingly identified as a strategic asset and enabler to accelerate the research and the business performed in all areas requiring intensive computing and large ... [more ▼]

High Performance Computing (HPC) is increasingly identified as a strategic asset and enabler to accelerate the research and the business performed in all areas requiring intensive computing and large-scale Big Data analytic capabilities. The efficient exploitation of heterogeneous computing resources featuring different processor architectures and generations, coupled with the eventual presence of GPU accelerators, remains a challenge. The University of Luxembourg operates since 2007 a large academic HPC facility which remains one of the reference implementation within the country and offers a cutting-edge research infrastructure to Luxembourg public research. The HPC support team invests a significant amount of time (i.e., several months of effort per year) in providing a software environment optimised for hundreds of users, but the complexity of HPC software was quickly outpacing the capabilities of classical software management tools. Since 2014, our scientific software stack is generated and deployed in an automated and consistent way through the RESIF framework, a wrapper on top of Easybuild and Lmod [5] meant to efficiently handle user software generation. A large code refactoring was performed in 2017 to better handle different software sets and roles across multiple clusters, all piloted through a dedicated control repository. With the advent in 2020 of a new supercomputer featuring a different CPU architecture, and to mitigate the identified limitations of the existing framework, we report in this state-of-practice article RESIF 3.0, the latest iteration of our scientific software management suit now relying on streamline Easybuild. It permitted to reduce by around 90% the number of custom configurations previously enforced by specific Slurm and MPI settings, while sustaining optimised builds coexisting for different dimensions of CPU and GPU architectures. The workflow for contributing back to the Easybuild community was also automated and a current work in progress aims at drastically decrease the building time of a complete software set generation. Overall, most design choices for our wrapper have been motivated by several years of experience in addressing in a flexible and convenient way the heterogeneous needs inherent to an academic environment aiming for research excellence. As the code base is available publicly, and as we wish to transparently report also the pitfalls and difficulties met, this tool may thus help other HPC centres to consolidate their own software management stack. [less ▲]

Protection of Personal Data in High Performance Computing Platform for Scientific Research Purposes

in Proc. of the EU Annual Privacy Forum (APF) 2021 (2021, June)

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging ... [more ▼]

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging paradigm of Open Science enables an easier access to expert knowledge and material; however, it also raises some challenges regarding the protection of personal data, considering that part of the research data are personal data thus subjected to the EU’s General Data Protection Regulation (GDPR). This paper investigates the concept of scientific research in the field of data protection, with regard both to the European (GDPR) and national (Luxembourg Data Protection Law) legal framework for the compliance of the HPC technology. Therefore, it focuses on a case study, the HPC platform of the University of Luxembourg (ULHPC), to pinpoint the major data protection issues arising from the processing activities through HPC from the perspective of the HPC platform operators. Our study illustrates where the most problematic aspects of compliance lie. In this regard, possible solutions are also suggested, which mainly revolve around (1) standardisation of procedures; (2) cooperation at institutional level; (3) identification of guidelines for common challenges. This research is aimed to support legal researchers in the field of data protection, in order to help deepen the understanding of HPC technology’s challenges and universities and research centres holding an HPC platform for research purposes, which have to address the same issues. [less ▲]

Uni.lu HPC Annual Report 2020

Report (2021)

2020 was a challenging year for everyone that will stay in our memory. The pandemic disrupted our economies, societies, and all our best laid-out plans. However, COVID-19 also taught us several lessons ... [more ▼]

2020 was a challenging year for everyone that will stay in our memory. The pandemic disrupted our economies, societies, and all our best laid-out plans. However, COVID-19 also taught us several lessons for the future, in particular the (real) necessity to adapt, to be nimble and to expect the unexpected while supporting cutting-edge excellence in science with the best performing and most flexible tools to unleash research potential. One thing is certain - the strategic developments for accelerated digitalisation and the role that HPC will play to ensure a smarter and more connected University will be in focus in 2021 and the years to come. 2020 was thus a very fruitful and productive year for the ULHPC team which has seen unprecedented changes and challenges. [less ▲]

Edge Computing: An Overview of Framework and Applications

Report (2020)

This report gives an overview of the Edge Computing paradigm and its applications. Indeed, with the advent of the Internet of Things (IoT) era, many electronic devices and sensors produce a vast volume of ... [more ▼]

This report gives an overview of the Edge Computing paradigm and its applications. Indeed, with the advent of the Internet of Things (IoT) era, many electronic devices and sensors produce a vast volume of data which should be processed in a timely manner and this novel computing model is nowadays seen as a pertinent answer to this open challenge. This report thus explains why Edge Computing is needed and how the edge architecture is typically structured. It further presents the technologies that help this cutting-edge model to function properly. Since Edge Computing involves a heterogeneous architecture, it requires to adapt to a few technological recommendations for optimal performance. In this context, this report reviews the latest hardware technology trends tied to Edge Computing developments and points out technical challenges implementing this innovative computing model. In particular, we analyse how High-Performance Computing and CloudComputing infrastructures can be efficiently organised to design an Edge Computing-based framework able to tackle cutting-edge issues solved by Artificial Intelligence techniques. Finally, this report presents selected real-world applications of the Edge Computing paradigm across multiple domains affecting our daily life, i.e., healthcare, smart city and grids, industry 4.0 and public safety [less ▲]

Performance Analysis of Distributed and Scalable Deep Learning

in 20th IEEE/ACM Intl. Symp. on Cluster, Cloud and Internet Computing (CCGrid'20) (2020, May)

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More ... [more ▼]

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive computing applications. DL allows computational models that are composed of multiple processing layers to learn in an automated way representations of data with multiple levels of abstraction, and can deliver higher predictive accuracy when trained on larger data sets. Based on Artificial Neural Networks (ANN), DL is now at the core of state of the art voice recognition systems (which enable easy control over e.g. Internet-of- Things (IoT) smart home appliances for instance), self-driving car engine, online recommendation systems. The ecosystem of DL frameworks is fast evolving, as well as the DL architectures that are shown to perform well on specialized tasks and to exploit GPU accelerators. For this reason, the frequent performance evaluation of the DL ecosystem is re- quired, especially since the advent of novel distributed training frameworks such as Horovod allowing for scalable training across multiple computing resources. In this paper, the scalability evaluation of the reference DL frameworks (Tensorflow, Keras, MXNet, and PyTorch) is performed over up-to-date High Performance Comput- ing (HPC) resources to compare the efficiency of differ- ent implementations across several hardware architectures (CPU and GPU). Experimental results demonstrate that the DistributedDataParallel features in the Pytorch library seem to be the most efficient framework for distributing the training process across many devices, allowing to reach a throughput speedup of 10.11 when using 12 NVidia Tesla V100 GPUs when training Resnet44 on the CIFAR10 dataset. [less ▲]