High performance error correcting code of the high-dimensional discrete torus knot

2002 ◽  
Author(s):  
M. Hata ◽  
E. Yamaguchi ◽  
Y. Hamasuna ◽  
T. Ishizaka ◽  
I. Takumi
Keyword(s):  
High Performance ◽  
Error Correcting Code ◽  
High Dimensional ◽  
Torus Knot ◽  
Performance Error ◽  
Discrete Torus

Distributed Based Serial Regression Multiple Imputation for High Dimensional Multivariate Data in Multicore Environment of Cloud

2021 ◽  
pp. 2572-2589
Author(s):  
Lavanya K ◽  
L.S.S. Reddy ◽  
B. Eswara Reddy
Keyword(s):  
Multiple Imputation ◽  
High Performance ◽  
Multivariate Data ◽  
Dynamic Structure ◽  
High Dimensional ◽  
Multiple Imputations ◽  
Pertinent Data ◽  
Parallel Input ◽  
Novel Applications ◽  
Balance Mechanism

Multiple imputations (MI) are predominantly applied in such processes that are involved in the transaction of huge chunks of missing data. Multivariate data that follow traditional statistical models undergoes great suffering for the inadequate availability of pertinent data. The field of distributed computing research faces the biggest hurdle in the form of insufficient high dimensional multivariate data. It mainly deals with the analysis of parallel input problems found in the cloud computing network in general and evaluation of high-performance computing in particular. In fact, it is a tough task to utilize parallel multiple input methods for accomplishing remarkable performance as well as allowing huge datasets achieves scale. In this regard, it is essential that a credible data system is developed and a decomposition strategy is used to partition workload in the entire process for minimum data dependence. Subsequently, a moderate synchronization and/or meager communication liability is followed for placing parallel impute methods for achieving scale as well as more processes. The present article proposes many novel applications for better efficiency. As the first step, this article suggests distributed-oriented serial regression multiple imputation for enhancing the efficiency of imputation task in high dimensional multivariate normal data. As the next step, the processes done in three diverse with parallel back ends viz. Multiple imputation that used the socket method to serve serial regression and the Fork Method to distribute work over workers, and also same work experiments in dynamic structure with a load balance mechanism. In the end, the set of distributed MI methods are used to experimentally analyze amplitude of imputation scores spanning across three probable scenarios in the range of 1:500. Further, the study makes an important observation that due to the efficiency of numerous imputation methods, the data is arranged proportionately in a missing range of 10% to 50%, low to high, while dealing with data between 1000 and 100,000 samples. The experiments are done in a cloud environment and demonstrate that it is possible to generate a decent speed by lessening the repetitive communication between processors.

scholarly journals GPU empowered pipelines for calculating genome-wide kinship matrices with ultra-high dimensional genetic variants and facilitating 1D and 2D GWAS

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Wenchao Zhang ◽  
Xinbin Dai ◽  
Shizhong Xu ◽  
Patrick X Zhao
Keyword(s):  
Genetic Variants ◽  
High Performance ◽  
Genome Wide Association Study ◽  
Graphics Processing Unit ◽  
High Dimensional ◽  
Processing Unit ◽  
Kinship Matrix ◽  
Matrix Operations ◽  
Genome Wide ◽  
Matrix Calculation

Abstract Genome-wide association study (GWAS) is a powerful approach that has revolutionized the field of quantitative genetics. Two-dimensional GWAS that accounts for epistatic genetic effects needs to consider the effects of marker pairs, thus quadratic genetic variants, compared to one-dimensional GWAS that accounts for individual genetic variants. Calculating genome-wide kinship matrices in GWAS that account for relationships among individuals represented by ultra-high dimensional genetic variants is computationally challenging. Fortunately, kinship matrix calculation involves pure matrix operations and the algorithms can be parallelized, particular on graphics processing unit (GPU)-empowered high-performance computing (HPC) architectures. We have devised a new method and two pipelines: KMC1D and KMC2D for kinship matrix calculation with high-dimensional genetic variants, respectively, facilitating 1D and 2D GWAS analyses. We first divide the ultra-high-dimensional markers and marker pairs into successive blocks. We then calculate the kinship matrix for each block and merge together the block-wise kinship matrices to form the genome-wide kinship matrix. All the matrix operations have been parallelized using GPU kernels on our NVIDIA GPU-accelerated server platform. The performance analyses show that the calculation speed of KMC1D and KMC2D can be accelerated by 100–400 times over the conventional CPU-based computing.

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