Mobile Erlang computations to enhance performance, resource usage and reliability

A software solution consists of multiple autonomous computations (i.e., execution threads) that execute concurrently (or apparently concurrently) over one or more locations to achieve a specific goal. Centralized solutions execute all computations on the same lo- cation while decentralized solutions disperse computations across different locations to increase scalability, enhance performance and reliability. Every location affects its executing computations both directly (e.g., the lack of a resource may prohibit a computation from progressing) and indirectly (e.g., an over- loaded location may slow down a computation). In a distributed environment, application developers have the luxury of executing each computation over its best-fitting location; the location (a) upon which the computation can achieve the best performance and (b) which guarantees the computation’s livelihood. Ideally, the decision to execute a computation over a location instead of another also load-balances the use of available resources such that it has the least impact over other computations (e.g., a computation should not execute over an already overloaded location further slowing down its computations). Application developers can only execute computations over their best-fitting location if their distributed programming language provides abstractions that allow them to control the locality of computations both before they are started and during their execution. In the rest of this document, section 2 briefly justifies why these two forms of locality control are required and section 3 outlines the issues that arise, and will be tackled in the talk to be held at CSAW 2014, by them.peer-reviewe

Distributed Computing in the Asynchronous LOCAL model

The LOCAL model is among the main models for studying locality in the framework of distributed network computing. This model is however subject to pertinent criticisms, including the facts that all nodes wake up simultaneously, perform in lock steps, and are failure-free. We show that relaxing these hypotheses to some extent does not hurt local computing. In particular, we show that, for any construction task $T$ associated to a locally checkable labeling (LCL), if $T$ is solvable in $t$ rounds in the LOCAL model, then $T$ remains solvable in $O(t)$ rounds in the asynchronous LOCAL model. This improves the result by Casta\~neda et al. [SSS 2016], which was restricted to 3-coloring the rings. More generally, the main contribution of this paper is to show that, perhaps surprisingly, asynchrony and failures in the computations do not restrict the power of the LOCAL model, as long as the communications remain synchronous and failure-free

Improving the scalability of parallel N-body applications with an event driven constraint based execution model

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing.Comment: 11 figure