Proposal of an analytical solution for the load imbalance problem in parallel systems

2004 ◽  
Author(s):  
M. Nour ◽  
S. Shaheen ◽  
G.F. El-Kabbany ◽  
N. Hegazy
Keyword(s):  
Analytical Solution ◽  
Parallel Systems ◽  
Imbalance Problem ◽  
Load Imbalance

ON PROCESSING MULTI-JOINS IN PARALLEL SYSTEMS

1991 ◽  
Vol 01 (02) ◽  
pp. 157-164
Author(s):  
KIAN-LEE TAN ◽  
HONGJUN LU
Keyword(s):  
Completion Time ◽  
Parallel Systems ◽  
Parallel Execution ◽  
Performance Study ◽  
Data Skew ◽  
Load Imbalance

In parallel systems, a number of joins from one or more queries can be executed either serially or in parallel. While serial execution assigns all processors to execute each join one after another, parallel execution distributes the joins to clusters formed by certain numbers of processors and executes them concurrently. However, data skew may result in load imbalance among processors executing the same join and some clusters may be overloaded with more expensive joins. As a result, the completion time will be much longer than what is expected. In this paper, we propose an algorithm to further minimize the completion time of concurrently executed multiple joins. For this algorithm, all the joins to be executed concurrently are decomposed into a set of tasks that are ordered according to decreasing task size. These tasks are dynamically acquired by available processors during execution. Our performance study shows that the proposed algorithm outperforms previously proposed approaches, especially when the number of processors increases, the relations are highly skewed and relation sizes are large.

scholarly journals Using Power Demand and Residual Load Imbalance in the Load Balancing to Save Energy of Parallel Systems

2017 ◽  
Vol 108 ◽  
pp. 695-704 ◽  
Author(s):  
Edson Luiz Padoin ◽  
Víctor Martínez ◽  
Philippe O.A. Navaux ◽  
Jean-François Méhaut
Keyword(s):  
Load Balancing ◽  
Parallel Systems ◽  
Power Demand ◽  
Load Imbalance ◽  
Save Energy

scholarly journals A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

2017 ◽  
Vol 3 ◽  
pp. e140 ◽  
Author(s):  
Rui Fan ◽  
Ke Xu ◽  
Jichang Zhao
Keyword(s):  
Betweenness Centrality ◽  
Network Science ◽  
Computational Cost ◽  
Dijkstra Algorithm ◽  
Weighted Networks ◽  
Imbalance Problem ◽  
Load Imbalance ◽  
Fast Solution ◽  
High Computational Cost

Betweenness, a widely employed centrality measure in network science, is a decent proxy for investigating network loads and rankings. However, its extremely high computational cost greatly hinders its applicability in large networks. Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of the betweenness centrality (BC) for large weighted networks. We parallelize the traditional Dijkstra algorithm by selecting more than one frontier vertex each time and then inspecting the frontier vertices simultaneously. By combining the parallel SSSP algorithm with the parallel BC framework, our GPU-based betweenness algorithm achieves much better performance than its CPU counterparts. Moreover, to further improve performance, we integrate the work-efficient strategy, and to address the load-imbalance problem, we introduce a warp-centric technique, which assigns many threads rather than one to a single frontier vertex. Experiments on both realistic and synthetic networks demonstrate the efficiency of our solution, which achieves 2.9× to 8.44× speedups over the parallel CPU implementation. Our algorithm is open-source and free to the community; it is publicly available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPUs in personal computers and servers, our solution will offer unprecedented opportunities for exploring betweenness-related problems and will motivate follow-up efforts in network science.

On the load imbalance problem of I/O forwarding layer in HPC systems

2017 ◽  
Author(s):  
Jie Yu ◽  
Guangming Liu ◽  
Wenrui Dong ◽  
Xiaoyong Li ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Imbalance Problem ◽  
Load Imbalance

scholarly journals Reducing Call Blocks in Cellular Network with Non-Uniform Traffic Conditions

2014 ◽  
Vol 39 (4) ◽  
pp. 301-318
Author(s):  
Abhijit Sharma ◽  
Pratik Sharma ◽  
Rituparna Chaki ◽  
Uma Bhattacharya
Keyword(s):  
Blocking Probability ◽  
Heavy Traffic ◽  
Cost Effective ◽  
Call Blocking ◽  
Traffic Demand ◽  
Call Blocking Probability ◽  
Traffic Conditions ◽  
Imbalance Problem ◽  
Load Imbalance ◽  
Consequent Reduction

Abstract In cellular networks, while the motivation behind all basic channel assignment strategies is the better utilization of the available frequency spectrum with the consequent reduction of the call blocking probability in each cell, very few of them deal with the problem of non-uniform traffic demand in different cells which may lead to a gross imbalance in the system performance. Mobile users in hot cells (the cells with heavy traffic loads) may suffer from low throughput due to the load imbalance problem. In this paper, we propose a cost effective and simple load balancing scheme that can effectively reduce the overall call blocking. A common set of channels are determined dynamically which can be used simultaneously in all the cells. Cell tiers with different radii are used to cope with the interference introduced by using same set of channels simultaneously in all cells. The performance of the proposed scheme is presented in terms of call blocking probability and channel utilization. Simulation results show that the proposed scheme can reduce the call blocking significantly in highly congested cell.

fMRI of Implicit and Explicit Sequence Learning: Evidence for Parallel Systems

2000 ◽  
Author(s):  
Joanna Salidas ◽  
Daniel B. Willingham ◽  
John D. E. Gabrieli
Keyword(s):  
Sequence Learning ◽  
Parallel Systems ◽  
Implicit And Explicit
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