CONCURRENCY AND COMPUTATION : PRACTICE AND EXPERIENCE Concurrency Computat

This special issue of Concurrency and Computation: Practice and Experience contains revised and extended versions of selected papers presented at the 10th International Conference on Parallel Processing and Applied Mathematics, PPAM 2013, which was held on September 8–11, 2013 in Warsaw, Poland. PPAM is a biennial series of international conferences dedicated to exchanging ideas between researchers involved in parallel and distributed computing, including theory and applications, as well as applied and computational mathematics. The focus of PPAM 2013 was on models, algorithms and software tools that facilitate efficient and convenient use of modern parallel and distributed computing systems, as well as on large-scale applications. PPAM 2013, the jubilee PPAM conference, was organized by the Department of Computer and Information Science of the Czestochowa University of Technology, under the patronage of the Committee of Informatics of the Polish Academy of Sciences, in cooperation with the Polish–Japanese Institute of Information Technology. This meeting gathered the largest number of participants in the history of PPAM conferences—more than 230 participants from 32 countries. A strict reviewing process, with each submission reviewed at least three times, resulted in acceptance of 143 contributed papers with the acceptance rate of 56%. The accepted papers were presented at the regular tracks of the PPAM 2013 conference, as well as during the workshops, which were important and integral components of PPAM meetings. Based on the results of the reviews, selected papers were recommended for a special journal issue. Besides quality, another important goal which influenced the paper selection was a maximum possible thematic consistency of the issue. The authors were contacted after the conference and invited to submit revised and extended versions of their papers. These new versions were reviewed independently by three reviewers. Finally, ten contributions were accepted for publication. They are summarized below. Paper [1] analyzes interaction occurring in the triangle: performance-power-energy for the execution of a pivotal numerical algorithm, the iterative Conjugate Gradient (CG) method, on a diverse collection of parallel multithreaded architectures. They range from general-purpose and digital signal multicore processors to GPUs. This analysis is especially timely in the decade where the power wall has arisen as a major obstacle to build faster processors. An alternative approach to the solution of a sequence of correlated eigenproblems is proposed in paper [2]. The resulting eigensolver is optimized regarding the number of matrix–vector multiplications and parallelized for distributed memory architectures using the Elemental library framework. Numerical results show that the proposed solver achieves excellent scalability and is competitive with current dense linear algebra parallel eigensolvers. Paper [3] focuses on an efficient implementation of the multidimensional Monte-Carlo integration on various distributed-memory parallel computers and clusters of multi-/manycore nodes. In particular, it addresses the issue how to use multiple cores of CPUs together with multiple GPU accelerators within a single node, achieving a reasonable load balancing of available computational resources. The adaptation of the multi-scale fluid solver EULAG to modern GPU platforms is addressed in paper [4], which proposes a method ensuring a comprehensive analysis of the resource consumption, including registers, shared and global memories. This method allows for identifying bottlenecks of the underlying algorithms. In consequence, the two main modules of EULAG have