Accelerating Smith-Waterman Alignment Workload with Scalable Vector Computing

2017 ◽  
Author(s):  
Dong-Hyeon Park ◽  
Jonathan Beaumont ◽  
Trevor Mudge
Keyword(s):  
Vector Computing

Lessons Learned from 1-Year Experience with SX-9 and Toward the Next Generation Vector Computing

2009 ◽  
pp. 3-22
Author(s):  
Hiroaki Kobayashi ◽  
Ryusuke Egawa ◽  
Hiroyuki Takizawa ◽  
Koki Okabe ◽  
Akihiko Musa ◽  
...  
Keyword(s):  
Lessons Learned ◽  
Next Generation ◽  
Vector Computing

Parallel Vector Computing Technique for Discovering Communities on the Very Large Scale Web Graph

2003 ◽  
pp. 151-160 ◽  
Author(s):  
Kikuko Kawase ◽  
Minoru Kawahara ◽  
Takeshi Iwashita ◽  
Hiroyuki Kawano ◽  
Masanori Kawazawa
Keyword(s):  
Large Scale ◽  
Computing Technique ◽  
Web Graph ◽  
Vector Computing

The Vector Computing Cloud: Toward a Vector Meta-Computing Environment

2010 ◽  
pp. 75-91
Author(s):  
Ryusuke Egawa ◽  
Manabu Higashida ◽  
Yoshitomo Murata ◽  
Hiroaki Kobayashi
Keyword(s):  
Computing Environment ◽  
Vector Computing

Coordinate descent based ontology sparse vector computing strategy and its applications

2017 ◽  
Vol 22 (S4) ◽  
pp. 10309-10323
Author(s):  
Wei Gao ◽  
Muhammad Shoaib Sardar ◽  
Sohail Zafar ◽  
Zohaib Zahid
Keyword(s):  
Coordinate Descent ◽  
Sparse Vector ◽  
Vector Computing

scholarly journals Graph Learning-Based Ontology Sparse Vector Computing

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1562
Author(s):  
Jianzhang Wu ◽  
Arun Kumar Sangaiah ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Early Stage ◽  
Optimal Solution ◽  
Spectral Graph Theory ◽  
Optimization Techniques ◽  
Ontology Learning ◽  
Structure Information ◽  
Sparse Vector ◽  
Vector Computing ◽  
The Given

The ontology sparse vector learning algorithm is essentially a dimensionality reduction trick, i.e., the key components in the p-dimensional vector are taken out, and the remaining components are set to zero, so as to obtain the key information in a certain ontology application background. In the early stage of ontology data processing, the goal of the algorithm is to find the location of key components through the learning of some ontology sample points, if the relevant concepts and structure information of each ontology vertex with p-dimensional vectors are expressed. The ontology sparse vector itself contains a certain structure, such as the symmetry between components and the binding relationship between certain components, and the algorithm can also be used to dig out the correlation and decisive components between the components. In this paper, the graph structure is used to express these components and their interrelationships, and the optimal solution is obtained by using spectral graph theory and graph optimization techniques. The essence of the proposed ontology learning algorithm is to find the decisive vertices in the graph Gβ. Finally, two experiments show that the given ontology learning algorithm is effective in similarity calculation and ontology mapping in some specific engineering fields.

scholarly journals Investigation of multi-site micro recordings of subthalamic nucleus neurons using machine learning MER with DBS in Parkinson`s – A simulation study

2022 ◽  
Vol 7 (4) ◽  
pp. 287-291
Author(s):  
Venkateshwarla Rama Raju
Keyword(s):  
Data Clustering ◽  
Cell Layer ◽  
Statistical Test ◽  
Electrode Site ◽  
Damping Factor ◽  
Volume Conduction ◽  
Identified Neuron ◽  
Vector Computing ◽  
Spike Bursts ◽  
Electrode Point

Multineural spikes were acquired with a multisite electrode placed in the hippocampus pyramidal cell layer of non-primate anesthetized snitch animals. If the impedance of each electrode-site is relatively low and the distance amongst electrode sites is appropriately miniatured, a spike generated by a neuron is parallelly recorded at multielectrode sites with different amplitudes. The covariance between the spike of the at each electrode-point and a template was computed as a damping-factor due to the volume conduction of the spike from the neuron to electrode-site. Computed damping factors were vectorized and analyzed by simple but elegant hierarchical-clustering using a multidimensional statistical-test. Since a cluster of damping vectors was shown to correspond to an antidromically identified neuron, spikes of distinct neurons are classified by suggesting to the scatterings of damping vectors. Errors in damping vector computing due to partially overlapping spikes were minimized by successively subtracting preceding spikes from raw data. Clustering errors due to complex-spike-bursts (i.e., spikes with variable-amplitudes) were prevented by detecting such bursts and using only the first spike of a burst for clustering.

scholarly journals GPU accelerated computing–from hype to mainstream, the rebirth of vector computing

2009 ◽  
Vol 180 ◽  
pp. 012043 ◽  
Author(s):  
Satoshi Matsuoka ◽  
Takayuki Aoki ◽  
Toshio Endo ◽  
Akira Nukada ◽  
Toshihiro Kato ◽  
...  
Keyword(s):  
Vector Computing
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