A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction which is defined as a process of mapping data from high-dimensional space into low-dimensional. One of the most popular methods for handling this problem is multidimensional scaling. Due to the technological advances, the dimensionality of the input data as well as the amount of processed data is increasing steadily but the requirement of processing these data within a reasonable time frame still remains an open problem. Recent development in graphics hardware allows to perform generic parallel computations on powerful hardware and provides an opportunity to solve many time-constrained problems in both graphical and non-graphical domain. The purpose of this survey is to describe and analyze recent implementations of multidimensional scaling algorithms on graphics processing units and present the applicability of these algorithms on such architectures based on the experimental results which show a decrease of execution time for multi-level approaches

Molecular dynamics simulations through GPU video games technologies

Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD). MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU). A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations

Bringing UMAP Closer to the Speed of Light with GPU Acceleration

The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning. While many algorithms can be ported to a GPU in a simple and direct fashion, such efforts have resulted in inefficient and inaccurate versions of UMAP. We show a number of techniques that can be used to make a faster and more faithful GPU version of UMAP, and obtain speedups of up to 100x in practice. Many of these design choices/lessons are general purpose and may inform the conversion of other graph and manifold learning algorithms to use GPUs. Our implementation has been made publicly available as part of the open source RAPIDS cuML library (https://github.com/rapidsai/cuml)

xploring Genetic Interactions: from Tools Development with Massive Parallelization on GPGPU to Multi-Phenotype Studies on Dyslexia

Over a decade, genome-wide association studies (GWASs) have provided insightful information into the genetic architecture of complex traits. However, the variants found by GWASs explain just a small portion of heritability. Meanwhile, as large scale GWASs and meta-analyses of multiple phenotypes are becoming increasingly common, there is a need to develop computationally efficient models/tools for multi-locus studies and multi-phenotype studies. Thus, we were motivated to focus on the development of tools serving for epistatic studies and to seek for analysis strategy jointly analyzed multiple phenotypes. By exploiting the technical and methodological progress, we developed three R packages. SimPhe was built based on the Cockerham epistasis model to simulate (multiple correlated) phenotype(s) with epistatic effects. Another two packages, episcan and gpuEpiScan, simplified the calculation of EPIBALSTER and epiHSIC and were implemented with high performance, especially the package based on Graphics Processing Unit (GPU). The two packages can be employed by epistasis detection in both case-control studies and quantitative trait studies. Our packages might help drive down costs of computation and increase innovation in epistatic studies. Moreover, we explored the gene-gene interactions on developmental dyslexia, which is mainly characterized by reading problems in children. Multivariate meta-analysis was performed on genome-wide interaction study (GWIS) for reading-related phenotypes in the dyslexia dataset, which contains nine cohorts from different locations. We identified one genome-wide significant epistasis, rs1442415 and rs8013684, associated with word reading, as well as suggestive genetic interactions which might affect reading abilities. Except for rs1442415, which has been reported to influence educational attainment, the genetic variants involved in the suggestive interactions have shown associations with psychiatric disorders in previous GWASs, particularly with bipolar disorder. Our findings suggest making efforts to investigate not just the genetic interactions but also multiple correlated psychiatric disorders