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Scalable computing with parallel tasks
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers, 2009Recent and future parallel clusters and supercomputers use SMPs and multi-core processors as basic nodes, providing a huge amount of parallel resources. These systems often have hierarchically structured interconnection networks combining computing resources at different levels, starting with the interconnect within multi-core processors up to the ...
Jörg Dümmler +2 more
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Elastic Tasks: Unifying Task Parallelism and SPMD Parallelism with an Adaptive Runtime
2015In this paper, we introduce elastic tasks, a new high-level parallel programming primitive that can be used to unify task parallelism and SPMD parallelism in a common adaptive scheduling framework. Elastic tasks are internally parallel tasks and can run on a single worker or expand to take over multiple workers.
Agrawal, Kunal +2 more
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Adaptive Parallelism for OpenMP Task Parallel Programs
2000We present a system that allows task parallel OpenMP programs to execute on a network of workstations (NOW) with a variable number of nodes. Such adaptivity, generally called adaptive parallelism, is important in a multi-user NOW environment, enabling the system to expand the computation onto idle nodes or withdraw from otherwise occupied nodes.
Alex Scherer +2 more
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Task Allocation by Parallel Evolutionary Computing
Journal of Parallel and Distributed Computing, 1997In this paper we will investigate the applicability of parallel evolutionary algorithms to the task allocation problem?a long standing problem in parallel computing. Three different evolutionary optimization strategies, genetic algorithms, simulated annealing, and steepest descent, are formulated in a parallel generic framework. In order to enhance the
Schoneveld, A. +2 more
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Integrating task and data parallelism
Proceedings of the 1993 ACM/IEEE conference on Supercomputing - Supercomputing '93, 1993The increased computational power of massively parallel computers and high bandwidth low latency computer networks will make a wide range of previously unpractical problems feasible. This will inevitably result in the need to develop parallel software whose complexity far exceeds that of parallel programs being developed today.
Foster, Ian, Kesselman, Carl
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Complexity of Scheduling Parallel Task Systems
SIAM Journal on Discrete Mathematics, 1989Summary: One of of the assumptions made in classical scheduling theory is that a task is always executed by one processor at a time. With the advances in parallel algorithms, this assumption may not be valid for future task systems. In this paper, a new model of task systems is studied, the so- called Parallel Task System, in which a task can be ...
Jianzhong Du, Joseph Y.-T. Leung
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Evolution of a Parallel Task Combinator
2013The development of experimental software is rarely straightforward. If you start making something you don't understand yet, it is very unlikely you get it right at the first try. The iTask system has followed this predictably unpredictable path. In this system, where combinator functions are used to construct interactive workflow support systems, the ...
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Parallelization using task parallel library with task-based programming model
2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014In order to reduce the complexity of traditional multithreaded parallel programming, this paper explores a new task-based parallel programming using the Microsoft .NET Task Parallel Library (TPL). Firstly, this paper proposes a custom data partitioning optimization method to achieve an efficient data parallelism, and applies it to the matrix ...
Xinhong Hei +4 more
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Scheduling interval ordered tasks in parallel
Journal of Algorithms, 1993Summary: We present the first NC algorithm for scheduling \(n\) unit length tasks on \(m\) identical processors for the case where the precedence constraint is an interval order. Our algorithm runs on a priority concurrent read, concurrent write parallel random acces machine in \(O(\log^2n)\) time with \(O(n^5)\) processors, or in \(O(\log^3n)\) time ...
Sivaprakasam Sunder, Xin He 0005
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2015 IEEE International Conference on Data Mining, 2015
In this paper, we develop parallel algorithms for a family of regularized multi-task methods which can model task relations under the regularization framework. Since those multi-task methods cannot be parallelized directly, we use the FISTA algorithm, which in each iteration constructs a surrogate function of the original problem by utilizing the ...
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In this paper, we develop parallel algorithms for a family of regularized multi-task methods which can model task relations under the regularization framework. Since those multi-task methods cannot be parallelized directly, we use the FISTA algorithm, which in each iteration constructs a surrogate function of the original problem by utilizing the ...
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