Use of SIMD-based data parallelism to speed up sieving in integer-factoring algorithms

Sengupta, Binanda; Das, Abhijit

doi:10.1016/j.amc.2016.08.019

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…The algorithm has been extensively tested and continues to securely protect information [4]. Due to the potential availability of almost limitless processing power in the future through quantum computers, many feel that RSA may not be an acceptable encryption method in the foreseeable future [5][6][7][8]. Progress is being made in the applications of quantum physics to encryption technology.…”

Section: Introductionmentioning

confidence: 99%

Data Encryption Device Using Radioactive Decay and a Hybrid Quantum Encryption Algorithm

Anthony¹,

Karthik²,

Dennis³

et al. 2017

Int J Electron Device Phys

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Quantum computers are the future in computing as data encoded in modern computers is limited by the space to store it. Quantum computers encode data in atoms that don't follow classical physics but rather quantum physics. Quantum physics provide an advantage in computing, as it allows data to be processed exponentially faster. However, the increase in computing speeds of such magnitude poses a potential risk to modern day encryption standards. Thus, to protect the transfer of data one must look towards developing innovative ways of encryption that shield it from the speed of quantum computers. This paper discusses a method to secure data by using radioactive decay in conjunction with an encryption algorithm. The main purpose of this paper is to develop and implement an encryption device that can be interfaced with a computer system. The device then utilizes the randomness of radioactive decay as a seed used in the encryption algorithm.

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Section: Introductionmentioning

confidence: 99%

Data Encryption Device Using Radioactive Decay and a Hybrid Quantum Encryption Algorithm

Anthony¹,

Karthik²,

Dennis³

et al. 2017

Int J Electron Device Phys

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show abstract

“…SIMD uses data-level parallelism, not task parallelism or concurrency (Sengupta, B., & Das, A., 2017). When using SIMD, data is shared between processing elements and executes the same instruction on them.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Parallelism in Unity With Job System Tool

Eyniyev¹

2022

AzJHPC

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Every day the gaming industry is developing faster and faster. Especially the popularity of games increased during the pandemic, as many people were forced to stay at home for a long time and play games in their free time. With the increasing popularity of games, the requirements for the games themselves also increase. As a result, modern games require large computer resources. However, not all people can afford to buy computers with the most expensive video cards, many expensive CPUs, and many RAMs and other components. In order to make games accessible to users with weaker devices, developers have few options. At the moment, there are two main ways to solve this problem. The first is to host games on the cloud, and the second is to optimize games for weaker devices. To optimize games, you can use various methods, including a package Job System for Unity. The Job System is one of the main components for working with threads in an Entity Component System (ECS). Also, in the future, in games with the Unity engine, parallelization between weak devices and High-Performance Computing (HPC) technologies will be combined and implemented using cloud technologies. In the future, when this happens, it will help developers, gamers, and even companies providing cloud services.

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“…A study by Corrigan-Gibbs et al realized the privacy information communication system of in-memory computing. A study by Sengupta et al [13] used SIMD-based data parallelism to speed up sieving in integer-factoring algorithms. Ifeanyi et al [14] presented a comprehensive survey fault tolerance mechanisms for the high-performance framework.…”

Section: Introductionmentioning

confidence: 99%

A performance optimization strategy based on degree of parallelism and allocation fitness

Ying

Ban

2018

J Wireless Com Network

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With the emergence of big data era, most of the current performance optimization strategies are mainly used in a distributed computing framework with disks as the underlying storage. They may solve the problems in traditional disk-based distribution, but they are hard to transplant and are not well suitable for performance optimization especially for an in-memory computing framework on account of different underlying storage and computation architecture. In this paper, we first give the definition of the resource allocation model, parallelism degree model, and allocation fitness model on the basis of the theoretical analysis of Spark architecture. Second, based on the model presented, we propose a strategy embedded in the evaluation model which is easy to perform. The optimization strategy selects the worker with a lower load that satisfies requirements to assign the latter tasks, and the worker with a higher load may not be assigned tasks. The experiments consisting of four variance jobs are conducted to verify the effectiveness of the presented strategy.

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Use of SIMD-based data parallelism to speed up sieving in integer-factoring algorithms

Cited by 6 publications

References 19 publications

Data Encryption Device Using Radioactive Decay and a Hybrid Quantum Encryption Algorithm

Data Encryption Device Using Radioactive Decay and a Hybrid Quantum Encryption Algorithm

Optimizing Parallelism in Unity With Job System Tool

A performance optimization strategy based on degree of parallelism and allocation fitness

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