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Mobility Solutions for the Telecom Industry
Published in Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu, A Comprehensive Guide to Enterprise Mobility, 2016
Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu
The mobile communications industry has witnessed tremendous changes in technology, adoption, and services since its introduction to the present state (from the zeroth generation mobile phone (0G) of 1945, the analog-only first-generation mobile phone [1G] of the 1980s, the 2G, 2.5G, and 3G to the 4G long-term evolution phones of the present day). Although the mobile phone was initially invented as a means of transmitting voice over a wireless network, today it has transformed into not only a device for voice and data communication over a wireless network, gaming, media, and entertainment but also a powerful extension of desktop computing devices (personal computers). From an era in which processor clock speeds of kilohertz and memory of a few bytes were considered as the highest performers, telecommunications has reached an era where processor clock speeds of even gigahertz and gigabytes of memory are treated as insufficient for a high-performance smartphone. Today, single-core processors for smartphone devices are an outdated design; most of the smartphones are running on dual-core, power-efficient processors.
FPGAs and Their Role in the Design of Electronic Systems
Published in Juan José Rodríguez Andina, Eduardo de la Torre Arnanz, María Dolores Valdés Peña, FPGAs, 2017
Juan José Rodríguez Andina, Eduardo de la Torre Arnanz, María Dolores Valdés Peña
Is there any possibility to face these challenges? Yes, using parallelism. Computer architectures based on single-core processors are no longer providing better performance. Different smart uses of parallel processing are leading the main trends in HPC, as can be seen in the discussion by Kaeli and Akodes (2011). Actually, strictly speaking, taking the most advantage of parallelism does not just mean achieving the highest possible performance using almost unlimited computing resources but also achieving the best possible performance–resources and performance–energy trade-offs. This is the goal in the area of embedded systems, where resources and the energy budget are limited.
State-of-the-Art and Challenges
Published in Hamidreza Ahmadian, Roman Obermaisser, Jon Perez, Distributed Real-Time Architecture for Mixed-Criticality Systems, 2018
H. Ahmadian, M. Coppola, M. Faugére, D. Gracia Pérez, M. Grammatikakis, I. Martinez
In order to satisfy the previously described performance requirements, alternative approaches need to be explored. Multi-core processors are the most promising alternative to the single-core architectures used in the past. Theoretically, multi-core would satisfy the new performance requirements. In addition, they are widely applicable as the current software applications can be reused.
Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method
Published in Engineering Optimization, 2018
Guangyuan Kan, Xiaoyan He, Liuqian Ding, Jiren Li, Yang Hong, Depeng Zuo, Minglei Ren, Tianjie Lei, Ke Liang
It has been widely proved that parallel computing is an effective way to accelerate the optimization method. The parallel algorithms must be implemented on parallel computing devices such as multi-core central processing units (CPUs) and many-core GPUs. Before 2005, most processors were single-core devices, and hardware producers improved the performance by boosting the clock speed of the processor or by adopting instruction-level parallelism. In those days, the ‘free lunch’ Moore’s law appeared. However, the performance of a single-core processor can no longer follow Moore’s law. Therefore, hardware producers switched to integrating multiple processors into one chip and processors started to support vectorization. Multi-core technology appeared. However, the increasing computational burden has exceeded the computational power of the multi-core CPU and new parallel devices are urgently needed. In 2007, the NVIDIA Corporation proposed the CUDA and NVIDIA GPU. New-generation high-performance computing devices composed of multi-core CPUs and many-core GPUs have become mainstream. This new hardware and the corresponding software programming technologies are named heterogeneous parallel computing. The heterogeneous computing system is much more powerful and energy efficient than the traditional CPU-based supercomputer. It features two concepts: heterogeneity and parallelism. Heterogeneity indicates that the computing platform is constituted of multiple types of device with different hardware architectures, such as the popular X86 CPU + NVIDIA GPU. Parallelism indicates that the programming technology is a parallel one.