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Locality-aware task scheduling for homogeneous parallel computing systems

Muhammad Khurram Bhatti, Isil Oz, Sarah Amin, Maria Mushtaq, Umer Farooq, Konstantin Popov, Mats Brorsson
2017 Computing  
This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS).  ...  Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures.  ...  In this paper, we propose a scheduling heuristic, called the LeTS (Locality-aware Task Scheduling) heuristic, for structured parallel programming systems, i.e., systems with explicit data and control dependencies  ... 
doi:10.1007/s00607-017-0581-6 fatcat:kewo2ghebvgt3aepta23hqu7re

Author index

2006 2006 IEEE International Conference on Cluster Computing  
Heterogeneous and Homogeneous Commodity Clusters Vasupongayya, Sangsuree Multi-Objective Models for Scheduling Jobs on Parallel Computer Systems Vaughan, Courtenay A Simple Synchronous Distributed-Memory  ...  Kernel-Level Measurement for Integrated Parallel Performance Views: the KTAU Project Manzanares, Adam Energy-Aware Duplication Strategies for Scheduling Precedence- Constrained Parallel Tasks on Clusters  ... 
doi:10.1109/clustr.2006.311921 fatcat:vmbbimypuze7ncjqfonu4po5l4

High performance scheduling of mixed-mode DAGs on heterogeneous multicores [article]

Agnes Rohlin, Henrik Fahlgren, Miquel Pericas
2019 arXiv   pre-print
To achieve high performance we analyze various schemes for heterogeneous scheduling, including both criticality-aware and performance-only schemes, and extend them with task molding to dynamically adjust  ...  the amount of resources used for each task.  ...  This scheme allows for locality-aware and interference-free scheduling as tasks can be bound within a TAO and scheduled onto adjacent cores.  ... 
arXiv:1901.05907v2 fatcat:3j2mcdgimbd2hpp4f4remfjeey

A Comprehensive View of MapReduce Aware Scheduling Algorithms in Cloud Environments

Hadi Yazdanpanah, Amin Shouraki, Abbas Ali
2015 International Journal of Computer Applications  
It is best suited for embarrassingly parallel and data-intensive tasks.  ...  This paper tries to illustrate and analyze the overview of thirteen different aware scheduling algorithms with different techniques and approaches for MapReduce in Hadoop and their scheduling issues and  ...  Locality Aware Scheduling In [22] , the authors propose locality-aware scheduling algorithm (LaSA) to enhance data locality assignment in Hadoop scheduler and increases performance of dataintensive computing  ... 
doi:10.5120/ijca2015906395 fatcat:m5fax4qzcbfgrhk6p7p2csxpw4

Improving Map Reduce Performance in Heterogeneous Distributed System using HDFS EnvironmentA Review

Shraddha Thakkar
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
Unfortunately, both the homogeneity and data locality assumptions are not satisfied in virtualized data centers.  ...  Map Reduce is widely used for short jobs requiring low response time. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature.  ...  Data Locality Aware Task Scheduling Method for Heterogeneous Environments In this research, the work is built upon the method to improve data locality of Map reduce in homogeneous computing environments  ... 
doi:10.17762/ijritcc2321-8169.150301 fatcat:edtbl476lrhgbmacp2c6ouwbde

On Exploiting Energy-Aware Scheduling Algorithms for MDE-Based Design Space Exploration of MP2SoC

Manel Ammar, Mouna Baklouti, Maxime Pelcat, Karol Desnos, Mohamed Abid
2016 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)  
Massively Parallel Multi-Processors System-on-Chip (MP2SoC) architectures have been widely deployed to run challenging high-performance computations.  ...  However, the ever greater demand for energy efficiency fosters energy budgeting in MP2SoC systems.  ...  The proposed schedule before duplicating reduces the schedule length by allowing encode mb and decode mb tasks running in parallel on four computing nodes.  ... 
doi:10.1109/pdp.2016.110 dblp:conf/pdp/AmmarBPDA16 fatcat:2kjcsbunfnhsnjf3anke6pl7fm

Table of Contents

2006 2006 IEEE International Conference on Cluster Computing  
Robust task scheduling in non-deterministic heterogeneous computing systems Zhiao Shi, Emmanuel Jeannot, Jack Dongarra Session C-Posters Parallelizing Lattice Gauge Theory Models on Commodity Clusters  ...  in Web Services Hao Wang, Yizhu Tong, Hong Liu, Taoying Liu Session C-7A: Performance Analysis Energy-Aware Duplication Strategies for Scheduling Precedence Constrained Parallel Tasks on Clusters Ziliang  ... 
doi:10.1109/clustr.2006.311830 fatcat:gdfo4gyd6nagxelrvylj5yldae

A Survey On Data-Centric And Data-Aware Techniques For Large Scale Infrastructures

Silvina Caíno-Lores, Jesús Carretero
2016 Zenodo  
As a result, we present an overview of locality-oriented techniques, which are grouped in four main categories: application development, task scheduling, in-memory computing and storage platforms.  ...  In this survey we analyse the different mechanisms that have been proposed to provide data locality for large scale high-performance and high-throughput systems.  ...  There is a need for further research in parallel file systems and storage architectures to support data awareness.  ... 
doi:10.5281/zenodo.1112257 fatcat:4l7qjgwdcrffddnwgb4dd3miuu

Scheduling Data-Intensive Tasks on Heterogeneous Many Cores

Pinar Tözün, Helena Kotthaus
2019 IEEE Data Engineering Bulletin  
Scheduling various data-intensive tasks over the processing units of a server has been a heavily studied but still challenging effort.  ...  tasks.  ...  The authors would like to thank Jens Teubner, Philippe Bonnet, and Danica Porobic for providing valuable feedback.  ... 
dblp:journals/debu/TozunK19 fatcat:ej47kfvucfhwpgyhmpvnxc3lqq

Load balancing strategy for multicore systems

E. Chovancova, J. Mihal'ov
2015 2015 13th International Conference on Emerging eLearning Technologies and Applications (ICETA)  
High performance computing including parallel and distributing systems now focus on power efficient execution.  ...  Soft-wares are written for multicore platform that distribute the workload amongst multiple identical or different cores. This functionality is called thread-level parallelism.  ...  Rishma Sadaf for her review and valuable comments.  ... 
doi:10.1109/iceta.2015.7558473 fatcat:ffoxzd7j6jc4loehhunoppfhwe

Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments [article]

B. Thirumala Rao, L. S. S. Reddy
2012 arXiv   pre-print
In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also.  ...  Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds.  ...  Delay scheduling is a solution that temporarily relaxes fairness to improve locality by asking jobs to wait for a scheduling opportunity on a node with local data.  ... 
arXiv:1207.0780v1 fatcat:xrnqojq3ljay3ihxk2pkhefkne

Heterogeneous Phase-Level Scheduling With Jobs Execution Scheduling Algorithm To Enhance Job Execution And Resource Management In Mapreduce

M.Sneha Priya, Mrs.R. Rebekha
2016 International Journal Of Engineering And Computer Science  
To achieve this parallel processing, the jobs are split into 3 phases. Each phase is provided with resources for parallel and fast execution of jobs.  ...  If the resources are provided in a homogeneous way, it takes more time to complete a task.Now Heterogeneous phase level scheduling algorithm with Jobs Execution Scheduling is used to split the resources  ...  Furthermore, Yarn generally outperforms the Fair scheduler for large workloads, because it is more resource-aware. The locality of tasks are shown in Figure 16 (a).  ... 
doi:10.18535/ijecs/v5i6.26 fatcat:gvuryzcu4bdpzgv55extxmc5qa

A Study on Big Data Hadoop Map Reduce Job Scheduling

N Deshai, S Venkataramana, I Hemalatha, G P. S. Varma
2018 International Journal of Engineering & Technology  
To design an effective algorithm is a key factor for selecting nodes are important, to optimize and acquire high performance in Big data.  ...  An efficient and useful survey, overview, advantages and disadvantages of these scheduling algorithms provided also identified throughout this paper.  ...  - Homogenous Aware Both YES Heterogeneous, Scheduler Dynamic Deadline Constraint Scheduler Dynamic Not in real time Homogenous Static Learning Scheduler Both YES Homogenous Heterogeneous, Dynamic COSHH  ... 
doi:10.14419/ijet.v7i3.31.18202 fatcat:kx6i4qwmrrhx5keybtovmnwexy

A Task-level Pipelined Many-SIMD Augmented Reality Processor with Congestion-aware Network-on-Chip Scheduler

Gyeonghoon Kim, Donghyun Kim, Seongwook Park, Youchang Kim, Kyuho Lee, Injoon Hong, Kyeongryeol Bong, Hoi-Jun Yoo
2015 IEEE Micro  
In addition, the multicore employs a congestion-aware network-on-chip scheduler for 2D-mesh network-on-chip to support massive internal data transaction caused by task-level pipeline.  ...  To enable a real-time operation of the proposed augmented reality, task-level pipelined multicore architecture with DLP/TLP optimized SIMD processing elements is implemented.  ...  Also, an on-line congestion-aware scheduler (CAS) for 2D-mesh network-on-chip (NoC) architecture is implemented for low power consumption.  ... 
doi:10.1109/mm.2015.2 fatcat:kd5kg5ndxne3hksvlxzkrq2nne


Dr N Sandhya
2017 International Journal of Advanced Research in Computer Science  
We have researched a resourceful and appropriate scheduling scheme called as hybrid job-driven scheduling scheme (JoSS) which source higher map and reduce data-locality.  ...  This model splits the job into several map perform tasks and reduce perform tasks at run time. It also accomplishes these tasks in parallel on a MapReduce cluster.  ...  For those several jobs which run very slow for Hadoop mapreduce, resource aware scheduling methodology was enhanced by these schedulers.  ... 
doi:10.26483/ijarcs.v8i9.5210 fatcat:4riinqfyn5glrkz6s4vsenyv3q
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