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Variational inequalities constitute a broad class of problems with applications in a number of fields, including game theory, economics, and machine learning. Today’s practical applications of VIs are becoming increasingly computationally demanding. It is therefore necessary to employ distributed computations to solve such problems in a reasonable time. In this context, workers have to exchange data with each other, which creates a communication bottleneck. There are three main techniques to reduce the cost and the number of communications: the similarity of local operators, the compression of messages and the use of local steps on devices. There is an algorithm that uses all of these techniques to solve the VI problem and outperforms all previous methods in terms of communication complexity. However, this algorithm is limited to unbiased compression. Meanwhile, biased (contractive) compression leads to better results in practice, but it requires additional modifications within an algorithm and more effort to prove the convergence. In this work, we develop a new algorithm that solves distributed VI problems using data similarity, contractive compression and local steps on devices, derive the theoretical convergence of such an algorithm, and perform some experiments to show the applicability of the method.

The problem of centralized planning for data transmission routes in delay tolerant networks is considered. The original problem is extended with additional requirements to nodes storage and communication process. First, it is assumed that the connection between the nodes of the graph is established using antennas. Second, it is assumed that each node has a storage of finite capacity. The existing works do not consider these requirements. It is assumed that we have in advance information about messages to be processed, information about the network configuration at specified time points taken with a certain time periods, information on time delays for the orientation of the antennas for data transmission and restrictions on the amount of data storage on each satellite of the grouping. Two wellknown algorithms — CGR and Earliest Delivery with All Queues are improved to satisfy the extended requirements. The obtained algorithms solve the optimal message routing problem separately for each message. The problem of validation of the algorithms under conditions of lack of test data is considered as well. Possible approaches to the validation based on qualitative conjectures are proposed and tested, and experiment results are described. A performance comparison of the two implementations of the problem solving algorithms is made. Two algorithms named RDTNAS-CG and RDTNAS-AQ have been developed based on the CGR and Earliest Delivery with All Queues algorithms, respectively. The original algorithms have been significantly expanded and an augmented implementation has been developed. Validation experiments were carried to check the minimum «quality» requirements for the correctness of the algorithms. Comparative analysis of the performance of the two algorithms showed that the RDTNAS-AQ algorithm is several orders of magnitude faster than RDTNAS-CG.

One of the most widely used storage solutions in WLCG is a Disk Pool Manager (DPM) developed and supported by SDC/ID group at CERN. Recently DPM went through a massive overhaul to address scalability and extensibility issues of the old code.

New system was called DMLite. Unlike the old DPM that was based on daemons, DMLite is arranged as a library that can be loaded directly by an application. This approach greatly improves performance and transaction rate by avoiding unnecessary inter-process communication via network as well as threading bottlenecks.

DMLite has a modular architecture with its core library providing only the very basic functionality. Backends (storage engines) and frontends (data access protocols) are implemented as plug-in modules. Doubtlessly DMLite wouldn't be able to completely replace DPM without GridFTP as it is used for most of the data transfers in WLCG.

In DPM GridFTP support was implemented in a Data Storage Interface (DSI) module for Globus’ GridFTP server. In DMLite an effort was made to rewrite a GridFTP module from scratch in order to take advantage of new DMLite features and also implement new functionality. The most important improvement over the old version is a redirection capability.

With old GridFTP frontend a client needed to contact SRM on the head node in order to obtain a transfer URL (TURL) before reading or writing a file. With new GridFTP frontend this is no longer necessary: a client may connect directly to the GridFTP server on the head node and perform file I/O using only logical file names (LFNs). Data channel is then automatically redirected to a proper disk node.

This renders the most often used part of SRM unnecessary, simplifies file access and improves performance. It also makes DMLite a more appealing choice for non-LHC VOs that were never much interested in SRM.

With new GridFTP frontend it's also possible to access data on various DMLite-supported backends like HDFS, S3 and legacy DPM.

The paper presents an implementation of branch and bound algorithm employing coarse grained parallelism. The system is based on CBC (COIN-OR branch and cut) open-source MIP solver and inter-process communication capabilities of Erlang. Numerical results show noticeable speedup in comparison to single-threaded CBC instance.