Magdalena Ryczkowska

Magdalena Ryczkowska
Nicolaus Copernicus University | umk · Department of Parallel and Distributed Calculations


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Skills and Expertise


Publications (8)
Computations based on graphs are very common problems but complexity, increasing size of analyzed graphs and a huge amount of communication make this analysis a challenging task. In this paper, we present a comparison of two parallel BFS (Breath-First Search) implementations: MapReduce run on Hadoop infrastructure and in PGAS (Partitioned Global Ad...
Full-text available
In this report we present PCJ (Parallel Computing in Java) as a novel tool for scalable data processing in Java. PCJ library is Java library based on PGAS (Partitioned Global Address Space) programming paradigm and allows for easy and feasible development of computational applications including BigData processing.
Conference Paper
Graph processing is used in many fields of science such as sociology, risk prediction or biology. Although analysis of graphs is important it also poses numerous challenges especially for large graphs which have to be processed on multicore systems. In this paper, we present PGAS (Partitioned Global Address Space) version of the level-synchronous B...
Graph-based computations are used in many applications. Increasing size of analyzed data and its complexity make graph analysis a challenging task. In this paper we present performance evaluation of Java implementation of Graph500 benchmark. It has been developed with the help of the PCJ (Parallel Computations in Java) library for parallel and dist...
Full-text available
With the wide adoption of the multicore and multiprocessor systems the parallel programming became a very important element of the computer science. The programming of the multicore systems is still complicated and far to be easy. The difficulties are caused, amongst others, by the parallel tools, libraries and programming models which are not easy...


Cited By


Project (1)
Project focuses on the ease of use and programmability of Java for distributed heterogeneous computing in order to make it exploitable by the huge user base of mainstream computing. Based on the previous work (PCJ library, we will introduce and transparently expose parallelism in Java, with minimal change to the specifics of the language thus allowing programmers to focus on the application. We have demonstrated power and scalability of the PCJ library for the parallel systems and we will extend it for the cases where communication cost and latency could be higher. We will extend existing solution with the capability of running on the heterogeneous systems including GPU and mobile devices. The user will obtain possibility to execute computational intensive parts of the application on the multiple GPUs. Since our solution is based on Java it can be easily run on the mobile devices. Within project we will extend library capabilities with the optimised communication and scheduling mechanism necessary to use fully such devices. We will utilize potential of parallel Java library to process distribute data. The existing solution benefits from the parallel I/O performed by the multiple JVMs. We will use this solution to optimize process of data distribution and storage including streaming od the large data sets. We will address dependability and resilience by adding fault tolerance mechanisms to the parallel Java library including fault detection and rescheduling of the application execution. The mechanism will extend capabilities of the existing PCJ library and will be transparent to the users. We will show the applicability of our framework for distributed heterogeneous systems by a set of selected, key applications including data-intensive Big Data applications. Our potential success will create solution for Java programming that will be attractive to a wide mainstream user base and will thus have a game-changing influence on the European computing industry. We assembled a carefully selected team with complementary focuses and the right degree of overlap. Most of the partners have worked in close collaboration in previous (EU) projects with remarkable success. We believe this to become a key pilot project that can open the way for future research which will have a profound impact on mainstream computing.