Abbrevation
PPAM
City
Cracow
Country
Poland
Deadline Paper
Start Date
End Date
Abstract

<pre>The PPAM 2015 conference, eleven in a series, will cover topics in parallel and distributed computing, including theory and applications, as well as applied mathematics&#046; The focus will be on models, algorithms, and software tools which facilitate efficient and convenient utilization of modern parallel and distributed computing architectures, as well as on large&#8211;scale applications, including big data problems&#046; PPAM is a biennial conference started in 1994, with the proceedings published by Springer in the Lecture Notes in Computer Sciences series&#046; Next year the PPAM conference will take place in beautiful Cracow, the old capital of Poland, a major national academic, cultural and artistic centre&#046; Topics of interest include (but are not limited) to: &#8211; Parallel/distributed architectures, enabling technologies &#8211; Cluster and cloud computing &#8211; Multi&#8211;core and many&#8211;core parallel computing &#8211; GPU computing &#8211; Heterogeneous/hybrid computing and accelerators &#8211; Parallel/distributed algorithms: numerical and non&#8211;numerical &#8211; Scheduling, mapping, load balancing &#8211; Performance analysis and prediction &#8211; Performance issues on various types of parallel systems &#8211; Autotuning: methods, tools, and applications &#8211; Power and energy aspects of computation &#8211; Parallel/distributed programming &#8211; Tools and environments for parallel/distributed computing &#8211; Security and dependability in parallel/distributed environments &#8211; HPC numerical linear algebra &#8211; HPC methods of solving differential equations &#8211; Evolutionary computing, meta&#8211;heuristics and neural networks &#8211; HPC interval analysis &#8211; Applied Computing in mechanics, material processing, biology and medicine, physics, chemistry, business, environmental modeling, etc&#046; &#8211; Applications of parallel/distributed computing &#8211; Methods and tools for parallel solution of large&#8211;scale problems, including big data applications &#8211; Large&#8211;scale social network analysis<br></pre>