AVES: A high performance computer cluster array for the INTEGRAL satellite scientific data analysis

2012 ◽  
Vol 34 (1) ◽  
pp. 105-121 ◽  
Author(s):  
Memmo Federici ◽  
Bruno Luigi Martino ◽  
Pietro Ubertini
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Scientific Data ◽  
Computer Cluster ◽  
Scientific Data Analysis ◽  
High Performance Computer

Health benefits of avicularin in the medicine against cancerous disorders and other complications: Biological importance, therapeutic benefit and analytical aspects of the medicine

2021 ◽  
Vol 17 ◽  
Author(s):  
Kanika Patel ◽  
Dinesh Kumar Patel
Keyword(s):  
Data Analysis ◽  
Scientific Data ◽  
Lespedeza Cuneata ◽  
Pharmacological Activities ◽  
Medicinal Uses ◽  
Biological Importance ◽  
Anti Cancer ◽  
Scientific Data Analysis ◽  
Different Types ◽  
Anti Inflammatory

Background: Herbal drugs and their derived phytochemicals have been used in medicine for the preparation of different types of pharmaceutical products. Pure phytochemicals including flavonoids, alkaloids and terpenoids have been used in medicine for the treatment of different types of human disorders including cancerous disorders. Flavonoids have been well known in medicine for their anti-viral, anti-bacterial, anti-inflammatory, anti-diabetic, anti-cancer, anti-aging and cardioprotective potential. Avicularin, also called quercetin-3-α-l-arabino furanoside, is a pure flavonoid, a class of phytochemicals, found to be present in Lindera erythrocarpa and Lespedeza cuneata. Avicularin has been well known in medicine for its anti-cancer properties. Methods: In the present work, scientific data of avicularin have been collected from different databases such as Google, PubMed, Science Direct, Google Scholar and Scopus and summarized with reference to medicinal importance, pharmacological activities and analytical aspects of avicularin. The present review summarized the health beneficial properties of avicularin in medicine through data analysis of various scientific research works. Further analytical progress in medicine for the qualitative and quantitative analysis of avicularin in medicine has been also discussed in the present work. Results: Scientific data analysis of different literature work revealed the biological importance of flavonoid class of phytochemical ‘avicularin’ in medicine. Scientific data analysis revealed that avicularin was found to be present in the Lindera erythrocarpa, Lespedeza cuneata, Rhododendron schlipenbachii and Psidium guajava. Avicularin has been well known in medicine for its anti-inflammatory, anti-allergic, anti-oxidant, anti-tumor and hepatoprotective activities. Avicularin protects cardiomyocytes and hepatocytes against oxidative stress-induced apoptosis and induces cytotoxicity in cancer lines and tumor tissues. Avicularin has positive influence on human hepatocellular carcinoma and inhibits intracellular lipid accumulation. The role of avicularin in rheumatoid arthritis has been also established with its underlying molecular mechanisms in the scientific work. Recent interest in avicularin has focused on pharmacological investigations for its anti-cancer activity in the medicine. Conclusion: The present work signified the biological importance of avicularin in medicine through its medicinal uses, pharmacological activities and analytical aspects in the biological system.

scholarly journals Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

2020 ◽  
Vol 245 ◽  
pp. 09014
Author(s):  
Chao Jiang ◽  
David Ojika ◽  
Sofia Vallecorsa ◽  
Thorsten Kurth ◽  
Prabhat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Heterogeneous Computing ◽  
Scientific Data ◽  
Great Promise ◽  
University Of Florida ◽  
Computing Platform ◽  
Scientific Data Analysis ◽  
The University ◽  
Heterogeneous Computing Platform

AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.

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