Powerful Multi-core Processors Research Articles

Learning in AI Processor

AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.

Information-flow control on ARM and POWER multicore processors

Information-flow control on ARM and POWER multicore processors

Threats and Solutions to Mobile Devices

Mobile devices have now surpassed personal computers (PC) in terms of popularity. Smartphones now come with powerful multi-core processors, loaded with considerable amounts of memory and are capable of carrying out complex operations with relative ease. However, this increase in technology has meant that it has now become susceptible to some of the same problems that PC's face. In this paper, I will talk about the malware, virus and other security problems facing mobile devices and their possible solutions.

PHAST: Protein-like heteropolymer analysis by statistical thermodynamics

PHAST is a software package written in standard Fortran, with MPI and CUDA extensions, able to efficiently perform parallel multicanonical Monte Carlo simulations of single or multiple heteropolymeric chains, as coarse-grained models for proteins. The outcome data can be straightforwardly analyzed within its microcanonical Statistical Thermodynamics module, which allows for computing the entropy, caloric curve, specific heat and free energies. As a case study, we investigate the aggregation of heteropolymers bioinspired on Aβ25−33 fragments and their cross-seeding with IAPP20−29 isoforms. Excellent parallel scaling is observed, even under numerically difficult first-order like phase transitions, which are properly described by the built-in fully reconfigurable force fields. Still, the package is free and open source, this shall motivate users to readily adapt it to specific purposes.Program summaryProgram Title: PHASTProgram Files doi:http://dx.doi.org/10.17632/ggds2grzw9.1Licensing provisions: GNU GPL version 3Programming language: FORTRAN 90, MPICH 3.0.4, CUDA 8.0Nature of the problem: Nowadays powerful multicore processors (CPUs) and Graphical Processing Units (GPUs) became much popular and cost-effective, so enabling thermostatistical studies of complex molecular systems to be performed even in personal computers. PHAST provides not only an easily-reconfigurable parallel program for Monte Carlo simulations of linear heteropolymers, as coarse-grained models of proteins, but also permits the automatized microcanonical thermodynamic analysis of those systems.Solution method: PHAST has three main modules for complementary tasks as: to map any .pdb file-sequence to its inner lexicon by using a configurable hydrophobic scale, while the main simulational module performs parallel Monte Carlo simulations in the multicanonical (MUCA) ensemble and, the analysis module which extracts microcanonical observables from MUCA weights.External routines/libraries: cuRAND (CUDA), Grace-5.1.22 or higher.

Evaluating the Potential of Low Power Systems for Headphone-based Spatial Audio Applications

Embedded architectures have been traditionally designed tailored to perform a dedicated (specialized) function, and in general feature a limited amount of processing resources as well as exhibit very low power consumption. In this line, the recent introduction of systems-on-chip (SoC) composed of low power multicore processors, combined with a small graphics accelerator (or GPU), presents a notable increment of the computational capacity while partially retaining the appealing low power consumption of embedded systems. This paper analyzes the potential of these new hardware systems to accelerate applications that integrate spatial information into an immersive audiovisual virtual environment or into video games. Concretely, our work discusses the implementation and performance evaluation of a headphone-based spatial audio application on the Jetson TK1 development kit, a board equipped with a SoC comprising a quad-core ARM processor and an NVIDIA “Kepler” GPU. Our implementations exploit the hardware parallelism of both types of architectures by carefully adapting the underlying numerical computations. The experimental results show that the accelerated application is able to move up to 300 sound sources simultaneously in real time on this platform.

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In recent years there has been a breakthrough in the evolution of hardware, companies like Intel have developed powerful multi-core processors (Core i3, i5, i7, etc.), in order to improve the execution times of processes. However, the software has not had the same growth in programming languages have emerged that make programming languages through IDEs (Integrated Development Environments) that improve the interaction between instructions, statements, functions, procedures of a program and especially the environment and interface programmer; but not the maximization of the hardware capacity that as the computer equipment with multiple processors. So the research question arises: How to improve performance of applications developed in Visual C ++ using parallel programming techniques with Intel Parallel Studio XE tool? The aim of the study was to determine the impact of the applying the techniques of parallelism in algorithms systems and storage instructions implemented in the c ++ language tools Parallel Studio XE, compared to sorting algorithms and storage instructions with programming techniques traditional. The study is quantitative, with a quasi-experimental approach compares two development scenarios. The stage controller (sequential programming) and the experimental stage (parallel programming) under visual studio 2010 IDE with visual C++ language, incorporating libraries developed by Intel through its package Parallel Studio Xe (Padua, 2011). Parallel programming is used to accelerate the resolution of problems of high computational cost, which aim to reduce the execution time of the sequential starting time t (n) using p processors to reduce runtime as follows: t (n)/p. This runtime depends on several variables: input size, compiler, machine, and the programmer among others.

PII: S0045-7825(02)00548-0

PHAST is a software package written in standard Fortran, with MPI and CUDA extensions, able to efficiently perform parallel multicanonical Monte Carlo simulations of single or multiple heteropolymeric chains, as coarse-grained models for proteins. The outcome data can be straightforwardly analyzed within its microcanonical Statistical Thermodynamics module, which allows for computing the entropy, caloric curve, specific heat and free energies. As a case study, we investigate the aggregation of heteropolymers bioinspired on Aβ25−33 fragments and their cross-seeding with IAPP20−29 isoforms. Excellent parallel scaling is observed, even under numerically difficult first-order like phase transitions, which are properly described by the built-in fully reconfigurable force fields. Still, the package is free and open source, this shall motivate users to readily adapt it to specific purposes.Program summaryProgram Title: PHASTProgram Files doi: http://dx.doi.org/10.17632/ggds2grzw9.1 Licensing provisions: GNU GPL version 3Programming language: FORTRAN 90, MPICH 3.0.4, CUDA 8.0Nature of the problem: Nowadays powerful multicore processors (CPUs) and Graphical Processing Units (GPUs) became much popular and cost-effective, so enabling thermostatistical studies of complex molecular systems to be performed even in personal computers. PHAST provides not only an easily-reconfigurable parallel program for Monte Carlo simulations of linear heteropolymers, as coarse-grained models of proteins, but also permits the automatized microcanonical thermodynamic analysis of those systems.Solution method: PHAST has three main modules for complementary tasks as: to map any .pdb file-sequence to its inner lexicon by using a configurable hydrophobic scale, while the main simulational module performs parallel Monte Carlo simulations in the multicanonical (MUCA) ensemble and, the analysis module which extracts microcanonical observables from MUCA weights.External routines/libraries: cuRAND (CUDA), Grace-5.1.22 or higher.