Multi-core processors: A new way forward and challenges

Roy, A.; Xu, Jian; Chowdhury, Masud H.

doi:10.1109/icm.2008.5393510

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…On the other hand, as stated by Amdahl's Law, the performance of parallel computing is limited by its serial components [29,30]. Although multicore CPUs offer outstanding instruction execution speed with reduced power consumption, optimizing performance of individual processors and then incorporating them by interconnection between processors and access to shared resources on a single die is a non-trivial task [31,32].…”

Section: Central Processing Units (Cpus) 211 Evolution Of Cpu Archite...mentioning

confidence: 99%

Hardware Architectures for Real-Time Medical Imaging

et al. 2021

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Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

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Section: Central Processing Units (Cpus) 211 Evolution Of Cpu Archite...mentioning

confidence: 99%

Hardware Architectures for Real-Time Medical Imaging

et al. 2021

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“…In recent years, the technology of multi-core processing (Blake et al, 2009;Roy et al, 2008;Geer, 2005) has been widely used in many computational fields including general-purpose, embedded, network, digital signal processing. In the hardware, the multicore processor is a single computing component consisting of two or more independent processing units that each of it can perform different operations at the same time.…”

Section: Multi-core Technologymentioning

confidence: 99%

The Effect of RSA Exponential Key Growth on the Multi-Core Computational Resource

Mohamed¹,

Tuama²,

Makhtar³

et al. 2016

American Journal of Engineering and Applied Sciences

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Cryptography has been widely used as a mean to secure message communication. A cryptosystem is made up of a publicly available algorithm and a secretly kept key. The algorithm is responsible for transforming the original message into something unintelligible. The result of losing the key or cracked algorithm can be catastrophic, where all secret communications will be known to adversaries. One way to find the key is by brute-force attacks which try every possible combination of keys. The only way to prevent this is by having the key of sufficiently large enough such that finding the right key cannot be made in a reasonable time frame. However, large key size imposes extra computational works which result in larger energy consumption and thus more heat dissipation to the environment. Therefore, the selection of key size does not only depends on the required security level, but also factors such as the ability of the processor and the available memory resources. The advent of multi-core technology promises some improvements in the utilization of computational resources. Many reports support the idea that multi-core technology brought a significant improvement over the single core technology. In this study, we investigate this hypothesis on the RSA cryptosystem in relation to the key size. Earlier studies reported multi-core efficiency in normal applications, but the question arises if multi-core architecture remains superior to a single core architecture when dealing with applications involving large integers. From our experimentation, we observe that the higher the number of cores, the better the performance of the encryption and decryption processes. The quad-core technology can smoothly handle operations involving 8192 bits key.

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“…However, according to Pollack’s rule, performance increase by microarchitecture alone is roughly proportional to square root of increase in complexity [16], thus the performance of a single processor core does not scale linearly with the number of logic on the core. As the transistor size shrinks, the leakage current becomes larger [17]. And with higher integrated density, power dissipation becomes the bottleneck of the architecture [16], [17].…”

Section: Overview and Backgroundmentioning

confidence: 99%

“…As the transistor size shrinks, the leakage current becomes larger [17]. And with higher integrated density, power dissipation becomes the bottleneck of the architecture [16], [17]. Alternatively, performance boost could be achieved by increasing the clock speed, or the frequency at which the processor operates at.…”

Section: Overview and Backgroundmentioning

confidence: 99%

Real-Time Agent-Based Modeling Simulation with in-Situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing

Seekhao

Shung

JáJá

et al. 2016

2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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We present an efficient and scalable scheme for implementing agent-based modeling (ABM) simulation with In Situ visualization of large complex systems on heterogeneous computing platforms. The scheme is designed to make optimal use of the resources available on a heterogeneous platform consisting of a multicore CPU and a GPU, resulting in minimal to no resource idle time. Furthermore, the scheme was implemented under a client-server paradigm that enables remote users to visualize and analyze simulation data as it is being generated at each time step of the model. Performance of a simulation case study of vocal fold inflammation and wound healing with 3.8 million agents shows 35× and 7× speedup in execution time over single-core and multi-core CPU respectively. Each iteration of the model took less than 200 ms to simulate, visualize and send the results to the client. This enables users to monitor the simulation in real-time and modify its course as needed.

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Multi-core processors: A new way forward and challenges

Cited by 11 publications

References 16 publications

Hardware Architectures for Real-Time Medical Imaging

Hardware Architectures for Real-Time Medical Imaging

The Effect of RSA Exponential Key Growth on the Multi-Core Computational Resource

Real-Time Agent-Based Modeling Simulation with in-Situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing

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