Running HPC applications on many million cores Cloud

Tomić, Draško; Car, Zlatan; Ogrizović, Dario

doi:10.23919/mipro.2017.7973420

Cited by 8 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-headed printers are able to print faster, but the pre-processing time of the required files grows proportionally with the number of printer heads. Fortunately, inequality (7) and the results from [16] suggests that this time can be significantly shortened by parallel processing these files on high-performance computing (HPC) systems. Further analysis of this problem shows that due to its specific structure, it can be efficiently solved on the Cloud.…”

Section: Discussionmentioning

confidence: 99%

“…However, traditional HPC resources are often difficult to access, and the cost of using them is relatively high, so it is good to consider using such resources on a Cloud system. In general, it is shown that a Cloud system is a suitable for HPC jobs in which no or minor inter-communication [16] is required. The calculation process is performed in two steps.…”

Section: Hpc Cloud For 3d Printingmentioning

confidence: 99%

See 1 more Smart Citation

Improving 3D printing of garments by using HPC Cloud

Tomić

Davidović

Šubic

et al. 2020

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

Self Cite

View full text Add to dashboard Cite

3D garment printing is a technology that is experiencing rapid development, and more and more garments are being printed on 3D printers. Advanced processes such as Selective Laser Sintering and PolyJet 3D printing are used. However, 3D printing of clothes still experiences some disadvantages, of which the biggest is the slow 3D printing speed and usually a small printing area of 3D printers. The novel, large multi-head printers can significantly increase the printing speed, but such printers require an additional step to prepare given 3D models that increase. However, pre-processing time grows exponentially with the number of printer heads, therefore computer resources that exceed the capabilities of a single workstation are required to prepare a printout. This implies the use of HPC resources, and because of its flexibility, lower cost and ease of use, HPC Cloud is optimal platform for such jobs. We have shown that the structure of these jobs fits perfectly in the HPC Cloud environment.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Hpc Cloud For 3d Printingmentioning

confidence: 99%

Improving 3D printing of garments by using HPC Cloud

Tomić

Davidović

Šubic

et al. 2020

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Service providers can manipulate the degree of multitenancy, which enables greater profit by overallocating resources to the users. The problems with HPC in the cloud were reiterated in [12] where authors have put the focus on necessary reductions in communication overhead and virtualization. For the former, they propose the implementation of better load balancing, and using bare-metal containers for the latter.…”

Section: High-performance Computing In the Cloudmentioning

confidence: 99%

Distributing the computation in combinatorial optimization experiments over the cloud

Brčić¹,

Hlupić²,

Katanic³

2017

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Combinatorial optimization is an area of great importance since many of the real-world problems have discrete parameters which are part of the objective function to be optimized. Development of combinatorial optimization algorithms is guided by the empirical study of the candidate ideas and their performance over a wide range of settings or scenarios to infer general conclusions. Number of scenarios can be overwhelming, especially when modeling uncertainty in some of the problem's parameters. Since the process is also iterative and many ideas and hypotheses may be tested, execution time of each experiment has an important role in the efficiency and successfulness. Structure of such experiments allows for significant execution time improvement by distributing the computation. We focus on the cloud computing as a cost-efficient solution in these circumstances. In this paper we present a system for validating and comparing stochastic combinatorial optimization algorithms. The system also deals with selection of the optimal settings for computational nodes and number of nodes in terms of performance-cost tradeoff. We present applications of the system on a new class of project scheduling problem. We show that we can optimize the selection over cloud service providers as one of the settings and, according to the model, it resulted in a substantial cost-savings while meeting the deadline.

show abstract

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Imdoukh

Ahmad

Alfailakawi

2019

Concurrency and Computation

View full text Add to dashboard Cite

Container virtualization is an emerging technology in cloud computing mainly due to its portability and lightweight features. Scheduling is a key task, performed by container management tool, which indirectly affects the characteristics of distributed software system in terms of availability, realizability, scalability, resources utilization, as well as power consumption. However, current schedulers only focus on some of the aforementioned aspects but not all. In this paper, a Many-Objective Genetic Algorithm Scheduler (MOGAS) is proposed to handle all such objectives to realize solutions with better characteristics. The proposed scheduler is compared with the Ant Colony Optimization (ACO)-based scheduler. Based on the proposed objective functions, simulation results show that MOGAS is better than the ACO scheduler in equally distributing tasks by 50%, assigning unique set of tasks per node by 40%, and reducing power consumption by 7%, on average.

show abstract

Running HPC applications on many million cores Cloud

Cited by 8 publications

References 23 publications

Improving 3D printing of garments by using HPC Cloud

Improving 3D printing of garments by using HPC Cloud

Distributing the computation in combinatorial optimization experiments over the cloud

Optimizing scheduling decisions of container management tool using many‐objective genetic algorithm

Contact Info

Product

Resources

About