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Tail queues: A multi-threaded matching architecture

Concurrency and Computation: Practice and Experience

Dosanjh, Matthew G.; Grant, Ryan; Schonbein, William W.; Bridges, Patrick G.

As we approach exascale, computational parallelism will have to drastically increase in order to meet throughput targets. Many-core architectures have exacerbated this problem by trading reduced clock speeds, core complexity, and computation throughput for increasing parallelism. This presents two major challenges for communication libraries such as MPI: the library must leverage the performance advantages of thread level parallelism and avoid the scalability problems associated with increasing the number of processes to that scale. Hybrid programming models, such as MPI+X, have been proposed to address these challenges. MPI THREAD MULTIPLE is MPI's thread safe mode. While there has been work to optimize it, it largely remains non-performant in most implementations. While current applications avoid MPI multithreading due to performance concerns, it is expected to be utilized in future applications. One of the major synchronous data structures required by MPI is the matching engine. In this paper, we present a parallel matching algorithm that can improve MPI matching for multithreaded applications. We then perform a feasibility study to demonstrate the performance benefit of the technique.

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TYPE Journal Article YEAR 2020
DOIOSTIScopus

Scalable preconditioning of block-structured linear algebra systems using ADMM

Computers and Chemical Engineering

Rodriguez, Jose S.; Laird, Carl; Zavala, Victor M.

We study the solution of block-structured linear algebra systems arising in optimization by using iterative solution techniques. These systems are the core computational bottleneck of many problems of interest such as parameter estimation, optimal control, network optimization, and stochastic programming. Our approach uses a Krylov solver (GMRES) that is preconditioned with an alternating method of multipliers (ADMM). We show that this ADMM-GMRES approach overcomes well-known scalability issues of Schur complement decomposition in problems that exhibit a high degree of coupling. The effectiveness of the approach is demonstrated using linear systems that arise in stochastic optimal power flow problems and that contain up to 2 million total variables and 4000 coupling variables. We find that ADMM-GMRES is nearly an order of magnitude faster than Schur complement decomposition. Moreover, we demonstrate that the approach is robust to the selection of the augmented Lagrangian penalty parameter, which is a key advantage over the direct use of ADMM.

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TYPE Journal Article YEAR 2020
DOIOSTIScopus
Results 1476–1500 of 9,998
Results 1476–1500 of 9,998