Computational investigations of folded self-avoiding walks related to protein folding

Various subsets of self-avoiding walks naturally appear when investigating existing methods designed to predict the 3D conformation of a protein of interest. Two such subsets, namely the folded and the unfoldable self-avoiding walks, are studied computationally in this article. We show that these two sets are equal and correspond to the whole $n$-step self-avoiding walks for $n\leqslant 14$, but that they are different for numerous $n \geqslant 108$, which are common protein lengths. Concrete counterexamples are provided and the computational methods used to discover them are completely detailed. A tool for studying these subsets of walks related to both pivot moves and proteins conformations is finally presented.Comment: Not yet submitte

DKEMA: GPU-based and dynamic key-dependent efficient message authentication algorithm

International audienceRecently, benefiting from the advancement in the Graphic Processing Unit (GPU) technology, there is an increased interest in implementing and designing new efficient cryptographic schemes. Existing cryptographic algorithms, especially the Message Authentication Algorithms (MAAs) such as Hash Message Authentication Code (HMAC) and Ciphered Message Authentication Code (CMAC), are not designed to benefit from the GPU characteristics, which results in degraded performance of their GPU implementations. This gives rise to a trade-off between the design concept and the performance level. In this paper, a new MAA, called ’DKEMA’, is proposed to better suit the GPU functionality. This scheme is basedon the dynamic key-dependent scheme with one round of substitution and diffusion operations. The experimental results show that the proposed solution is highly effective on Tesla V100 and A100 GPUs, and the throughput is, respectively, more than 400GB/s and 500GB/s. Therefore, DKEMA can be considered as a promising MAA candidate for GPU implementation, achieving the desired cryptographic properties such as high randomness, collision tolerance in addition to message and key avalanche effect. The experimental results show that theproposed solution, based on the dynamic key approach, is immune towards well-known authentication and cryptanalysis attacks. In addition, DKEMA, consisting of one round compression function, presents an enhancement in terms of performance compared to existing algorithms (e.g. AES and SHA)

Java and asynchronous iterative applications: large scale experiments

International audienceThis paper focuses on large scale experiments with Java and asynchronous iterative applications. In those applications, tasks are dependent and the use of distant clusters may be difficult, for example, because of latencies, heterogeneity, and synchronizations. Experiments have been conducted on the Grid'5000 platform using a new version of the Jace environment. We study the behavior of an application (the Poisson problem) with the following experimentation conditions: one and several sites, large number of processors (from 80 to 500), different communication protocols (RMI, sockets and NIO), synchronous and asynchronous model. The results we obtained, demonstrate both the scalability of the Jace environment and its ability to support wide-area deployments and the robustness of asynchronous iterative algorithms in a large scale context

Synchronous and asynchronous solution of a {3D} transport model in a grid computing environment

International audienceNumerical simulation is a common approach to understand many phenomena, usually yielding a computationally intensive problem. To overcome insufficient computer capacity and computational speed, a grid computing environment is a suitable approach. In this paper we focus on the development of parallel algorithms to solve a 3D transport model in such a context. The solver is based on the multisplitting Newton method that provides a coarse-grained scheme. Algorithms are implemented using JACE, a grid-enabled Java Asynchronous Computing Environment. This programming environment allows users to design synchronous and asynchronous parallel iterative algorithms as well. Experiments are carried out on a heterogeneous grid environment in which the behaviour of both parallel iterative algorithms is analysed. The results allow us to draw some conclusions about the use of the programming library JACE and the design of parallel iterative algorithms in a grid computing environment

A deep learning scheme for efficient multimedia IoT data compression

International audienceMultimedia Internet of Things (MIoT) devices and networks will face many power and communication overhead constraints given the volume of multimedia sensed data. Oneclassic approach to overcoming the difficulty of large-scale data is to use lossy compression. However, current lossy compression algorithms require a limited compression rate to maintain acceptable perceived image quality. This is commonly referred to as the image quality-compression ratio trade-off. Motivated by current breakthroughs in computer vision, this article proposes recovering high-quality decompressed images at the application server level using a deep learning-based super-resolution model. As a result, this paper proposes ignoring the trade-off betweenimage quality and size and increasing the reduction size further by using a lossy compressor with downscaling to conserve energy. The experimental study demonstrates that the proposed technique effectively improves the visual quality of compressed and downscaled images. The proposed solution was evaluated on resource-constrained microcontrollers. The obtained results show that the transmission latency and energy consumption can be decreased by up to 10% compared to conventional lossy compression techniques

Using GPU for Multi-Agent Soil Simulation

International audienceMulti-Agent Systems (MAS) can be used to model systems where the global behavior cannot be uniformly represented by standard techniques such as partial differential equations or linear systems because the system elements have their own independent behavior. This is, for instance, the case in complex systems such as daily mobility in a city for example. Depending on the system size the computing power needs for the MAS may be as big as for more traditional linear numerical systems and may need to be parallelized to fully represent real systems. Graphical Processing Units (GPU) have already proven to be an efficient support to execute large linear programs. In this paper we present the use of GPU for the execution of Sworm, a multi-scale MAS system. We show that GPU computing can be efficient in that less regular case and when the agent behavior is simple. We advocate for a wider use of the GPU in Agent Based Models in particular for multi-scale systems with work distribution between the CPU and GPU

Using GPU for Multi-agent Multi-scale Simulations

International audienceMulti-Agent System (MAS) is an interesting way to create models and simulators and is widely used to model complex systems. As the complex systemcommunity tends to build up larger models to fully represent real systems, the need for computing power raise significantly. Thus MAS often lead to long computing intensive simulations. Parallelizing such a simulation is complex and it execution requires the access to large computing resources. In this paper, we present the adaptation of a MAS system, Sworm, to a Graphical Processing Unit.We show that such an adaptation can improve the performance of the simulator and advocate for a more wider use of the GPU in Agent Based Models in particular for simple agents

Quality Studies of an Invisible Chaos-Based Watermarking Scheme with Message Extraction

International audienceThis paper takes place in the field of invisible chaos-based watermarking schemes. It addresses the quality study of an already pyblished algorithm by focusing on three class of properties. Its robustness is experimentally shown against classical attacks on a large set of image instances and image transformations. It correctness and completness are formally proven. Due to this main advantages, this process is fitted for practical use

MCMAS: A toolkit for developing agent-based simulations on many-core architectures

International audienceMulti-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generictoolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. The toolkit provides few famous algorithms as diffusion, path-finding or population dynamics that are frequently used in an agent based models. For further needs, MCMAS is based on a flexible architecture that can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with three models and their performance analysis