Reducing Fragmentation on 3D Torus-Based HPC Systems Using Packing-Based Job Scheduling and Job Placement Reconfiguration

2017 ◽  
Author(s):  
Kangkang Li ◽  
Maciej Malawski ◽  
Jarek Nabrzyski
Keyword(s):  
Job Scheduling ◽  
Job Placement

Partnering with Employers to Promote Job Placement for Low-Skill Individuals

2010 ◽  
Author(s):  
Karin Martinson
Keyword(s):  
Job Placement

Job placement and job accommodation for handicapped workers in Taiwan

1999 ◽  
Author(s):  
Chia-Fen Chi
Keyword(s):  
Job Placement ◽  
Job Accommodation

A SURVEY ON ENERGY AWARE JOB SCHEDULING ALGORITHMS IN CLOUD ENVIRONMENT

2016 ◽  
Vol 3 (1) ◽  
pp. 30 ◽  
Author(s):  
NASEERA SHAIK ◽  
JYOTHEESWAI P ◽  
◽  
Keyword(s):  
Job Scheduling ◽  
Scheduling Algorithms ◽  
Cloud Environment ◽  
Energy Aware

The Legal Regulation of Job Placement in the USSR

1967 ◽  
Vol 6 (3) ◽  
pp. 41-49
Author(s):  
Iu. P. Orlovskii
Keyword(s):  
Legal Regulation ◽  
Job Placement

Space sharing job scheduling policies for parallel computers

1995 ◽  
Author(s):  
Ismail Mohamed Ismail
Keyword(s):  
Job Scheduling ◽  
Parallel Computers ◽  
Scheduling Policies

Dependable grid job scheduling mechanism

2010 ◽  
Vol 30 (8) ◽  
pp. 2066-2069
Author(s):  
Yong-cai TAO ◽  
Lei SHI
Keyword(s):  
Job Scheduling ◽  
Grid Job Scheduling

Multi-level Parallelization of Genotype Imputation on Supercomputers

2020 ◽  
Vol 15 ◽  
Author(s):  
Weiwen Zhang ◽  
Long Wang ◽  
Theint Theint Aye ◽  
Juniarto Samsudin ◽  
Yongqing Zhu
Keyword(s):  
Association Study ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Genome Wide Association Study ◽  
Job Scheduling ◽  
Genotype Imputation ◽  
Job Level ◽  
Multi Level ◽  
High Performance Requirement

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.

scholarly journals Power and Performance Evaluation of Memory-Intensive Applications

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4089
Author(s):  
Kaiqiang Zhang ◽  
Dongyang Ou ◽  
Congfeng Jiang ◽  
Yeliang Qiu ◽  
Longchuan Yan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Power Consumption ◽  
Job Scheduling ◽  
Memory System ◽  
Processor Core ◽  
Memory Efficiency ◽  
And Performance ◽  
Reasonable Use ◽  
Server System

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.

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