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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 599-608
Concurrent data structures are widely used in many software stack levels, ranging from high level parallel scientific applications to low level operating systems. The key issue of these objects is their concurrent use by several computing units (threads or process) so that the design of these structures is much more difficult compared to their sequential counterpart, because of their extremely dynamic nature requiring protocols to ensure data consistency, with a significant cost overhead. At this regard, several studies emphasize a tension between the needs of sequential correctness of the concurrent data structures and scalability of the algorithms, and in many cases it is evident the need to rethink the data structure design, using approaches based on randomization and/or redistribution techniques in order to fully exploit the computational power of the recent computing environments. The problem is grown in importance with the new generation High Performance Computing systems aimed to achieve extreme performance. It is easy to observe that such systems are based on heterogeneous architectures integrating several independent nodes in the form of clusters or MPP systems, where each node is composed by powerful computing elements (CPU core, GPUs or other acceleration devices) sharing resources in a single node. These systems therefore make massive use of communication libraries to exchange data among the nodes, as well as other tools for the management of the shared resources inside a single node. For such a reason, the development of algorithms and scientific software for dynamic data structures on these heterogeneous systems implies a suitable combination of several methodologies and tools to deal with the different kinds of parallelism corresponding to each specific device, so that to be aware of the underlying platform. The present work is aimed to introduce a scalable model to manage a special class of dynamic data structure known as heap based priority queue (or simply heap) on these heterogeneous architectures. A heap is generally used when the applications needs set of data not requiring a complete ordering, but only the access to some items tagged with high priority. In order to ensure a tradeoff between the correct access to high priority items by the several computing units with a low communication and synchronization overhead, a suitable reorganization of the heap is needed. More precisely we introduce a unified scalable model that can be used, with no modifications, to redeploy the items of a heap both in message passing environments (such as clusters and or MMP multicomputers with several nodes) as well as in shared memory environments (such as CPUs and multiprocessors with several cores) with an overhead independent of the number of computing units. Computational results related to the application of the proposed strategy on some numerical case studies are presented for different types of computing environments.
data structures, message passing, multiprocessing systems, operating systems (computers), parallel processing, shared memory systems

G. Laccetti, M. Lapegna and R. Montella, "A Scalable Unified Model for Dynamic Data Structures in Message Passing (Clusters) and Shared Memory (multicore CPUs) Computing environments," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 599-608.
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