PEGASUS DA framework for distributed program execution control based on application global states monitoring

2018

Self Cite

Summary The paper is concerned with discrete event‐driven simulation, which is a well‐known technique used for modeling and simulating complex parallel systems. The focus of the paper is on distributed simulation, in which multiple simulated event queues are processed in parallel according to the Time Warp approach. With this approach, parallel simulation based on event queues is allowed to progress in an optimistic way until event correlation errors appear in parallel simulation branches what results in simulation rollbacks. In the absence of synchronization between simulator parallel queues, massive processing rollbacks can strongly slow down simulation. The paper presents a distributed optimistic event‐driven simulation control based on simulator global state monitoring. A systematic control of the simulator global states prevents excessive rollbacks in the Time Warp simulation. Each simulation event queue reports its progress to a global synchronizer which monitors the global simulation state based on virtual timestamps of recently processed events. Based on the global state, the synchronizer checks the simulation progress and sends control signals which asynchronously slow down servicing too advanced queues. The paper describes the principles of the proposed approach and experimental results of its basic program implementation. Comparisons to existing parallel simulation methods are provided.

Section: Global State Driven Program Execution Controlmentioning

confidence: 99%

“…They work in parallel to the networks used for application computational data communication. For more details on the properties of the PEGASUS DA framework, see other works …”

Section: Global State Driven Program Execution Controlmentioning

confidence: 99%

Section: Global State Driven Program Execution Controlmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Application global state monitoring in optimization of parallel event‐driven simulation

Masko

2018

Self Cite

10th international conference on Parallel Processing and Applied Mathematics, PPAM 2013

Wyrzykowski

2014

Self Cite

in Warsaw, Poland.PPAM is a biennial series of international conferences dedicated to exchanging ideas between researchers involved in parallel and distributed computing, including theory and applications, as well as applied and computational mathematics. The focus of PPAM 2013 was on models, algorithms and software tools that facilitate efficient and convenient use of modern parallel and distributed computing systems, as well as on large-scale applications.PPAM 2013, the jubilee PPAM conference, was organized by the Department of Computer and Information Science of the Czestochowa University of Technology, under the patronage of the Committee of Informatics of the Polish Academy of Sciences, in cooperation with the Polish-Japanese Institute of Information Technology.This meeting gathered the largest number of participants in the history of PPAM conferences-more than 230 participants from 32 countries. A strict reviewing process, with each submission reviewed at least three times, resulted in acceptance of 143 contributed papers with the acceptance rate of 56%. The accepted papers were presented at the regular tracks of the PPAM 2013 conference, as well as during the workshops, which were important and integral components of PPAM meetings.Based on the results of the reviews, selected papers were recommended for a special journal issue. Besides quality, another important goal which influenced the paper selection was a maximum possible thematic consistency of the issue. The authors were contacted after the conference and invited to submit revised and extended versions of their papers. These new versions were reviewed independently by three reviewers. Finally, ten contributions were accepted for publication. They are summarized below.Paper [1] analyzes interaction occurring in the triangle: performance-power-energy for the execution of a pivotal numerical algorithm, the iterative Conjugate Gradient (CG) method, on a diverse collection of parallel multithreaded architectures. They range from general-purpose and digital signal multicore processors to GPUs. This analysis is especially timely in the decade where the power wall has arisen as a major obstacle to build faster processors.An alternative approach to the solution of a sequence of correlated eigenproblems is proposed in paper [2]. The resulting eigensolver is optimized regarding the number of matrix-vector multiplications and parallelized for distributed memory architectures using the Elemental library framework. Numerical results show that the proposed solver achieves excellent scalability and is competitive with current dense linear algebra parallel eigensolvers.Paper [3] focuses on an efficient implementation of the multidimensional Monte-Carlo integration on various distributed-memory parallel computers and clusters of multi-/manycore nodes. In particular, it addresses the issue how to use multiple cores of CPUs together with multiple GPU accelerators within a single node, achieving a reasonable load balancing of available computational resources.The adapt...

Distributed Application Global States Monitoring in PEGASUS DA Applied to Parallel Graph Partitioning

Smyk

2020

Self Cite

Summary This paper presents how parallel advanced graph partitioning algorithms can be designed and improved with the use of global application states monitoring of distributed programs. The proposed algorithms have been implemented inside a novel distributed program design framework Program Execution Governed by Asynchronous Supervision of States in Distributed Applications (PEGASUS DA). This framework provides system support for the design of execution control in distributed applications based on automated global state monitoring. Two strategies for the control design of advanced parallel/distributed graph partitioning algorithms are presented and discussed. In the first one, the parallel algorithm control runs on top of the popular graph partitioning METIS tool. The second control strategy is based on genetic programming so that partitioning primitives and the overall algorithmic control can be freely designed by the user. Advanced partitioning methods have been conveniently embedded inside the global state monitoring driven PEGASUS DA framework which controls partitioning distributed actions at the level of processes and threads. The use of the framework allowed easy design and testing of different graph optimization strategies. The presented graph partitioning methods are illustrated by experiments with benchmark graphs. The experiments have comparatively assessed the obtained graph partitioning quality (visible improvement has been observed) and have identified benefits of the proposed approach for programmers.