Both grid and cloud are used to organize large scale calculations and data processing on remote computers. Grid which became a basic computing infrastructure for the Large Hadron Collider experiments provides unified technical solutions for sharing and merging distributed heterogeneous computing resources within big collaboration groups. Cloud became popular among data centers and computing service providers because of flexibility, manageability and efficient hardware utilization. Both approaches share common ideology “computing as a service”, so one can expect additional benefits from their integration. The paper describes our approach to the integration. We propose to use cloud within grid sites for acceleration of application deployment and easy support of multiple virtual organizations by grid sites. The cloud in grid approach has been implemented and tested in Ukrainian National Grid, a part of European Grid Infrastructure.

У статті наведено результати діяльності з впровадження в НАН України європейських принципів відкритої науки — нового підходу до організації наукового процесу на основі поширення знань з використанням цифрових технологій і нових засобів спільної роботи дослідників. Побудована в НАН України централізована інфраструктура відкритої науки сприятиме підвищенню продуктивності наукових досліджень, глибшій інтеграції українських вчених у європейський дослідницький простір, ефективнішому пропагуванню отриманих науковцями НАН України наукових результатів і ширшому залученню міжнародних грантів.

Introduction. In machine learning (ML) and artificial intelligence (AI) works, the emphasis is usually on the quality of classification or the accuracy of parameter estimation. If the focus is on performance, then it is also mainly about the performance of the model's training phase. However, with the proliferation of AI applications in real-world problems, the problem of ensuring high data processing performance with ready models becomes more important. By its nature, this problem is fundamentally different from the one of model training: the latter deals with intensive calculations and the former with simple calculations, but large flows of data (files) coming from the network or file system for processing. That is, the typical task of parallel processing with intensive input-output. Besides, in terms of application, the AI module that performs classification, evaluation, or other data processing is a "black box": the cost of developing and training the model, as well as the risks of failure, are too high to handle such tasks in a non-professional manner. Therefore, performance optimization primarily involves the selection and balancing of system parameters. Cloud systems with their flexibility, manageability and easy scaling are the ideal platforms for such tasks. Consider in more detail the task of investigating the factors which affect performance on a single, but notable, pattern recognition sample of a subset of ImageNet image collection [1] classified by the 50-layer deep learning neural network ResNet-50 [2]. The purpose of the paper is to experimentally investigate the factors that influence the performance of a ready-to-use neural network model application in GPU cloud systems of various architectures. Results. Overheads related to microservices and distributed architectures, memory, network, batch size, synchronous and asynchronous interactions are estimated. The complex nonlinear nature of the influence of the system parameters in various combinations is demonstrated.

Introduction. Sparse Distributed Memory (SDM) and Binary Sparse Distributed Representations (Binary Sparse Distributed Representations, BSDR), as two phenomenological approaches to biological memory modelling, have many similarities. The idea of ??their integration into a hybrid semantic storage model with SDM as a low-level cleaning memory (brain cells) for BSDR, which is used as an encoder of high-level symbolic information, is natural. A hybrid semantic store should be able to store holistic data (for example, structures of interconnected and sequential key-value pairs) in a neural network. A similar design has been proposed several times since the 1990s. However, the previously proposed models are impractical due to insufficient scalability and/or low storage density. The gap between SDM and BSDR can be bridged by the results of a third theory related to sparse signals: Compressive Sensing or Sampling (CS). In this article, we focus on the highly efficient parallel implementation of the CS-SDM hybrid memory model for graphics processing units on the NVIDIA CUDA platform, analyze the computational complexity of CS-SDM operations for the case of parallel implementation, and offer optimization techniques for conducting experiments with big sequential batches of vectors. The purpose of the paper is to propose an efficient software implementation of sparse-distributed memory for preserving semantics on modern graphics processing units. Results. Parallel algorithms for CS-SDM operations are proposed, their computational complexity is estimated, and a parallel implementation of the CS-SDM hybrid semantic store is given. Optimization of vector reconstruction for experiments with sequential data batches is proposed. Conclusions. The obtained results show that the design of CS-SDM is naturally parallel and that its algorithms are by design compatible with the architecture of systems with massive parallelism. The conducted experiments showed high performance of the developed implementation of the SDM memory block.