Ahmed El Kerim, Pierre Gosselet, Frédéric Magoulès
A novel approach is being developed to introduce a parallel asynchronous implementation of non-intrusive global-local coupling. This study examines scenarios involving numerous patches, including those covering the entire structure. By leveraging asynchronous, the method aims to minimize reliance on communication, handle failures effectively, and address load imbalances. Detailed insights into the methodology are presented, accompanied by a demonstration of its performance through an academic case study.
Todays Information Technology world is cloud-centric. Companies are intrigued to migrate their workload private cloud from on-premise Datacenter to Public cloud to take advantage of the latest innovations. It drives the business growth and competitiveness of the organization. At the same time, it is important for Enterprise Architects to understand the drawbacks and challenges to migrate the workload to Cloud. This paper aims to identify the key factors to migrate the workload to the cloud. It also helps an organization to identify the candidate for cloud migration. An impulsive decision to move to the Cloud may be detrimental to an organization. Also, I will discuss one case study to see the benefits and disadvantages of cloud migration. This will help the organization to maximize its ROI.
We consider blockchain in dynamic networks. We define the Blockchain Decision Problem. It requires miners that maintain the blockchain to confirm whether a particular block is accepted. We establish the necessary conditions for the existence of a solution. We, however, prove that the solution, even under these necessary conditions is, in general, impossible. We then present two algorithms that solve the Blockchain Decision Problem under either the knowledge of the maximum source pool propagation time or the knowledge of the source pool membership. We evaluate the performance of the two algorithms.
The HYDRO mini-application has been successfully used as a research vehicle in previous PRACE projects [6]. In this paper, we evaluate the benefits of the tasking model introduced in recent OpenMP standards [9]. We have developed a new version of HYDRO using the concept of OpenMP tasks and this implementation is compared to already existing and optimized OpenMP versions of HYDRO.
In this work, we review Bachelors Theses done under the supervision of Vitaly Aksenov at ITMO University. This overview contains the short description of six theses: "Development of a Streaming Algorithm for the Decomposition of Graph Metrics to Tree Metrics" by Oleg Fafurin, "Development of Memory-friendly Concurrent Data Structures" by Roman Smirnov, "Theoretical Analysis of the Performance of Concurrent Data Structures" by Daniil Bolotov, "Parallel Batched Interpolation Search Tree" by Alena Martsenyuk, "Parallel Batched Self-adjusting Data Structures" by Vitalii Krasnov, and "Parallel Batched Persistent Binary Search Trees" by Ildar Zinatulin.
The robustness of distributed optimization is an emerging field of study, motivated by various applications of distributed optimization including distributed machine learning, distributed sensing, and swarm robotics. With the rapid expansion of the scale of distributed systems, resilient distributed algorithms for optimization are needed, in order to mitigate system failures, communication issues, or even malicious attacks. This survey investigates the current state of fault-tolerance research in distributed optimization, and aims to provide an overview of the existing studies on both fault-tolerant distributed optimization theories and applicable algorithms.
This paper analyzes the shortcomings of unikernels as a method of deployment for machine learning inferencing applications as well as provides insights and analysis on future work in this space. The findings of this paper advocate for a tool to enable management of dependent libraries in a unikernel to enable a more ergonomic build process as well as take advantage of the inherent security and performance benefits of unikernels.
David Brayford, Christoph Bernau, Wolfram Hesse
et al.
The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify: execution patterns, possible code optimizations and improvements to the system monitoring. We also identify candidates for employing machine learning techniques to predict the performance of similar scientific codes.
We investigate the minimal number of failures that can partition a system where processes communicate both through shared memory and by message passing. We prove that this number precisely captures the resilience that can be achieved by algorithms that implement a variety of shared objects, like registers and atomic snapshots, and solve common tasks, like randomized consensus, approximate agreement and renaming. This has implications for the m&m-model and for the hybrid, cluster-based model.
Yahya Hassanzadeh-Nazarabadi, Nazir Nayal, Shadi Sameh Hamdan
et al.
LightChain is the first Distributed Hash Table (DHT)-based blockchain with a logarithmic asymptotic message and memory complexity. In this demo paper, we present the software architecture of our open-source implementation of LightChain, as well as a novel deployment scenario of the entire LightChain system on a single machine aiming at results reproducibility.
The chapter presents foundations of computing paradigms for realizing emerging IoT applications, especially fog and edge computing, their background, characteristics, architectures and open challenges.
In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.
Supercomputers become faster as hardware and software technologies continue to evolve. Current supercomputers are capable of 1015 floating point operations per second (FLOPS) that called Petascale system. The High Performance Computer (HPC) community is Looking forward to the system with capability of 1018 (FLOPS) that is called Exascale. Having a system to thousand times faster than the previous one produces challenges to the high performance computer (HPC) community. These challenges require innovation in software and hardware. In this paper, the challenges posed for programming at Exascale systems are reviewed and the developments in the main programming models and systems are surveyed.
Blockchain and Distributed ledger Technologies are increasingly becoming key enablers for vital innovation in financial services, manufacturing, government and other industries. One of the biggest challenges though is the level of support for semantics by most of the Block Chain and Distributed Ledger technologies. This paper reviews and categorises common block chain and DLT approaches and introduces a new approach to Blockchain / DLT promising to resolve the semantic problems inherent in other Blockchain / DLT approaches
A distributed semantic graph processing system that provides locality control, indexing, graph query, and parallel processing capabilities is presented.
Recent years have seen an increasing number of cross-cloud architectures, i.e. systems that span across cloud provisioning boundaries. However, the cloud computing world still lacks any standards in terms of programming interfaces, which has a knock-on effect on the costs associated with interoperability and severely limits the flexibility and portability of applications and virtual infrastructures. This paper outlines the different types of cross-cloud systems, and the associated design decisions.