Power-Efficient and High-Performance Multi-level Hybrid Nanophotonic Interconnect for Multicores

2010 ◽  
Author(s):  
Randy Wayne Morris Jr. ◽  
Avinash Karanth Kodi
Keyword(s):  
High Performance ◽  
Power Efficient ◽  
Multi Level ◽  
Nanophotonic Interconnect

Multi-level Parallelization of Genotype Imputation on Supercomputers

2020 ◽  
Vol 15 ◽  
Author(s):  
Weiwen Zhang ◽  
Long Wang ◽  
Theint Theint Aye ◽  
Juniarto Samsudin ◽  
Yongqing Zhu
Keyword(s):  
Association Study ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Genome Wide Association Study ◽  
Job Scheduling ◽  
Genotype Imputation ◽  
Job Level ◽  
Multi Level ◽  
High Performance Requirement

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.

scholarly journals Ultracompact and low-power-consumption silicon thermo-optic switch for high-speed data

Nanophotonics ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 937-945
Author(s):  
Ruihuan Zhang ◽  
Yu He ◽  
Yong Zhang ◽  
Shaohua An ◽  
Qingming Zhu ◽  
...  
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
High Speed ◽  
High Performance ◽  
Pulse Amplitude ◽  
Telecommunication Networks ◽  
Low Power Consumption ◽  
Power Efficient ◽  
High Speed Data ◽  
On Chip

AbstractUltracompact and low-power-consumption optical switches are desired for high-performance telecommunication networks and data centers. Here, we demonstrate an on-chip power-efficient 2 × 2 thermo-optic switch unit by using a suspended photonic crystal nanobeam structure. A submilliwatt switching power of 0.15 mW is obtained with a tuning efficiency of 7.71 nm/mW in a compact footprint of 60 μm × 16 μm. The bandwidth of the switch is properly designed for a four-level pulse amplitude modulation signal with a 124 Gb/s raw data rate. To the best of our knowledge, the proposed switch is the most power-efficient resonator-based thermo-optic switch unit with the highest tuning efficiency and data ever reported.

scholarly journals Multi-level characteristics of TiOx transparent non-volatile resistive switching device by embedding SiO2 nanoparticles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sera Kwon ◽  
Min-Jung Kim ◽  
Kwun-Bum Chung
Keyword(s):  
Resistive Switching ◽  
High Performance ◽  
Sio2 Nanoparticles ◽  
Visible Range ◽  
Resistive State ◽  
Switching Device ◽  
Structural Density ◽  
Multi Level ◽  
Switching Characteristics ◽  
Switching Devices

AbstractTiOx-based resistive switching devices have recently attracted attention as a promising candidate for next-generation non-volatile memory devices. A number of studies have attempted to increase the structural density of resistive switching devices. The fabrication of a multi-level switching device is a feasible method for increasing the density of the memory cell. Herein, we attempt to obtain a non-volatile multi-level switching memory device that is highly transparent by embedding SiO2 nanoparticles (NPs) into the TiOx matrix (TiOx@SiO2 NPs). The fully transparent resistive switching device is fabricated with an ITO/TiOx@SiO2 NPs/ITO structure on glass substrate, and it shows transmittance over 95% in the visible range. The TiOx@SiO2 NPs device shows outstanding switching characteristics, such as a high on/off ratio, long retention time, good endurance, and distinguishable multi-level switching. To understand multi-level switching characteristics by adjusting the set voltages, we analyze the switching mechanism in each resistive state. This method represents a promising approach for high-performance non-volatile multi-level memory applications.

scholarly journals Environmental Sound Recognition on Embedded Systems: From FPGAs to TPUs

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2622
Author(s):  
Jurgen Vandendriessche ◽  
Nick Wouters ◽  
Bruno da Silva ◽  
Mimoun Lamrini ◽  
Mohamed Yassin Chkouri ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Machine Learning Techniques ◽  
Sound Recognition ◽  
Learning Approaches ◽  
Environmental Sound ◽  
Embedded Devices ◽  
Power Efficient ◽  
Computationally Intensive ◽  
Environmental Sound Recognition

In recent years, Environmental Sound Recognition (ESR) has become a relevant capability for urban monitoring applications. The techniques for automated sound recognition often rely on machine learning approaches, which have increased in complexity in order to achieve higher accuracy. Nonetheless, such machine learning techniques often have to be deployed on resource and power-constrained embedded devices, which has become a challenge with the adoption of deep learning approaches based on Convolutional Neural Networks (CNNs). Field-Programmable Gate Arrays (FPGAs) are power efficient and highly suitable for computationally intensive algorithms like CNNs. By fully exploiting their parallel nature, they have the potential to accelerate the inference time as compared to other embedded devices. Similarly, dedicated architectures to accelerate Artificial Intelligence (AI) such as Tensor Processing Units (TPUs) promise to deliver high accuracy while achieving high performance. In this work, we evaluate existing tool flows to deploy CNN models on FPGAs as well as on TPU platforms. We propose and adjust several CNN-based sound classifiers to be embedded on such hardware accelerators. The results demonstrate the maturity of the existing tools and how FPGAs can be exploited to outperform TPUs.

Power-efficient and high-performance block I/O framework for mobile virtualization systems

2016 ◽  
Vol 73 (4) ◽  
pp. 1307-1321 ◽  
Author(s):  
Kihong Lee ◽  
DongWoo Lee ◽  
Sungkil Lee ◽  
Young Ik Eom
Keyword(s):  
High Performance ◽  
Power Efficient

Analyzing the Robustness of HPC Applications Using a Fine-Grained Soft Error Fault Injection Tool

2016 ◽  
pp. 277-305
Author(s):  
Qiang Guan ◽  
Nathan DeBardeleben ◽  
Sean Blanchard ◽  
Song Fu ◽  
Claude H. Davis IV ◽  
...  
Keyword(s):  
High Performance ◽  
Fault Injection ◽  
Soft Errors ◽  
Small Degree ◽  
Soft Error ◽  
Power Efficient ◽  
Fine Grained ◽  
Different Characteristics ◽  
The Impact ◽  
Performance Computing

As the high performance computing (HPC) community continues to push towards exascale computing, HPC applications of today are only affected by soft errors to a small degree but we expect that this will become a more serious issue as HPC systems grow. We propose F-SEFI, a Fine-grained Soft Error Fault Injector, as a tool for profiling software robustness against soft errors. We utilize soft error injection to mimic the impact of errors on logic circuit behavior. Leveraging the open source virtual machine hypervisor QEMU, F-SEFI enables users to modify emulated machine instructions to introduce soft errors. F-SEFI can control what application, which sub-function, when and how to inject soft errors with different granularities, without interference to other applications that share the same environment. We demonstrate use cases of F-SEFI on several benchmark applications with different characteristics to show how data corruption can propagate to incorrect results. The findings from the fault injection campaign can be used for designing robust software and power-efficient hardware.

scholarly journals Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries

BMC Genomics ◽  
2019 ◽  
Vol 20 (S11) ◽  
Author(s):  
Shuai Zeng ◽  
Zhen Lyu ◽  
Siva Ratna Kumari Narisetti ◽  
Dong Xu ◽  
Trupti Joshi
Keyword(s):  
Knowledge Base ◽  
Data Storage ◽  
High Performance ◽  
Data Retrieval ◽  
Omics Data ◽  
Web Interface ◽  
Web Resource ◽  
Privilege Management ◽  
Multi Level ◽  
Comprehensive Framework

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

A nanophotonic interconnect for high-performance many-core computation

2008 ◽  
Author(s):  
R. G. Beausoleil ◽  
M. Fiorentino ◽  
J. Ahn ◽  
N. Binkert ◽  
A. Davis ◽  
...  
Keyword(s):  
High Performance ◽  
Many Core ◽  
Nanophotonic Interconnect
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