README.md

In ecrc/exageostatR-dev: R package demonstrates the R / C Language interface for ExaGeoStat

ExaGeoStat-R

ExaGeoStat-R is an R-Wrapper for ExaGeoStat framework, a parallel high performance unified framework for geostatistics on manycore systems.

Getting Started

Installation

Software dependencies

1. Portable Hardware Locality (hwloc).
2. NLopt.
3. GNU Scientific Library (GSL).
4. StarPU.
5. Chameleon.
6. Hicma.
7. Stars-H. An easy installation of the above packages is available by using build-deps.sh

Install latest ExaGeoStat-R version hosted on GitHub(parallel installation)

install.packages("devtools")
library("devtools")
Sys.setenv(PKG_CONFIG_PATH=paste(Sys.getenv("PKG_CONFIG_PATH"),paste(.libPaths(),"exageostat/lib/pkgconfig",sep='/',collapse=':'),sep=':'))
Sys.setenv(MKLROOT="/opt/intel/mkl")
install_git(url="https://github.com/ecrc/exageostatR-dev")

Install latest ExaGeoStat-R version hosted on GitHub (sequential installation)

install.packages("devtools")
library("devtools")
Sys.setenv(PKG_CONFIG_PATH=paste(Sys.getenv("PKG_CONFIG_PATH"),paste(.libPaths(),"exageostat/lib/pkgconfig",sep='/',collapse=':'),sep=':'))
Sys.setenv(MKLROOT="/opt/intel/mkl")
Sys.setenv(MAKE="make -j 1")
install_git(url="https://github.com/ecrc/exageostatR-dev")

Get the latest ExaGeoStat-R release hosted on GitHub

1. Download exageostat_1.0.0.tar.gz from release
2. Use R to install exageostat_1.0.0.tar.gz

install.packages(repos=NULL, "exageostat_1.0.0.tar.gz")
library(exageostat)

Features of ExaGeoStat-R

Operations:

1. Generate synthetic spatial datasets (i.e., locations & environmental measurements).
2. Maximum likelihood evaluation using dense matrices.
3. Maximum likelihood evaluation using compressed matrices based on Tile Low-Rank(TLR).
4. Maximum likelihood evaluation using matrices based on Diagonal Super-Tile(DST).

More information

A more detailed description of the underlying ExaGeoStat software package can be found. here

R Examples

1. Test Generating Z vector using random (x, y) locations with exact MLE computation.

library("exageostat")                                          #Load ExaGeoStat-R lib.
seed          = 0                                             #Initial seed to generate XY locs.
sigma_sq      = 1                                             #Initial variance.
beta          = 0.1                                           #Initial smoothness.
nu            = 0.5                                           #Initial range.
dmetric       = 0                                             #0 --> Euclidean distance, 1--> great circle distance.
n             = 1600                                          #n*n locations grid.
#theta_out[1:3]                  = -1.99
exageostat_init(hardware = list (ncores=2, ngpus=0, ts=320, pgrid=1, qgrid=1))#Initiate exageostat instance
#Generate Z observation vector
data          = simulate_data_exact(sigma_sq, beta, nu, dmetric, n, seed) #Generate Z observation vector
#Estimate MLE parameters (Exact)
result        = exact_mle(data, dmetric, optimization = list(clb = c(0.001, 0.001, 0.001), cub = c(5, 5,5 ), tol = 1e-4, max_iters = 20))

#print(result)
#Finalize exageostat instance
exageostat_finalize()

1. Test Generating Z vector using random (x, y) locations with TLR MLE computation.

library("exageostat")                                           #Load ExaGeoStat-R lib.
seed            = 0                                             #Initial seed to generate XY locs.
sigma_sq        = 1                                             #Initial variance.
beta            = 0.03                                          #Initial smoothness.
nu              = 0.5                                           #Initial range.
dmetric         = 0                                             #0 --> Euclidean distance, 1--> great circle distance.
n               = 900                                           #n*n locations grid.
tlr_acc         = 7                                             #Approximation accuracy 10^-(acc)
tlr_maxrank     = 450                                           #Max Rank

#Initiate exageostat instance
exageostat_init(hardware = list (ncores=2, ngpus=0, ts=320, lts=600,  pgrid=1, qgrid=1))#Initiate exageostat instance
#Generate Z observation vector
data         = simulate_data_exact(sigma_sq, beta, nu, dmetric, n, seed) #Generate Z observation vecto
#Estimate MLE parameters (TLR approximation)
result       = tlr_mle(data, tlr_acc, tlr_maxrank,  dmetric, optimization = list(clb = c(0.001, 0.001, 0.001), cub = c(5, 5,5 ), tol = 1e-4, max_iters = 20))
#print(result)
#Finalize exageostat instance
exageostat_finalize()

1. Test Generating Z vector using random (x, y) locations with DST MLE computation.

library("exageostat")                                           #Load ExaGeoStat-R lib.
seed            = 0                                             #Initial seed to generate XY locs.
sigma_sq        = 1                                             #Initial variance.
beta            = 0.03                                          #Initial smoothness.
nu              = 0.5                                           #Initial range.
dmetric         = 0                                             #0 --> Euclidean distance, 1--> great circle distance.
n               = 900                                           #n*n locations grid.
dst_thick       = 3                                             #Number of used Diagonal Super Tile (DST).
#Initiate exageostat instance
exageostat_init(hardware = list (ncores=4, ngpus=0, ts=320, lts=0,  pgrid=1, qgrid=1))
#Generate Z observation vector
data      = simulate_data_exact(sigma_sq, beta, nu, dmetric, n, seed) #Generate Z observation vecto
#Estimate MLE parameters (DST approximation)
result       = dst_mle(data, dst_thick, dmetric, optimization = list(clb = c(0.001, 0.001, 0.001), cub = c(5, 5,5 ), tol = 1e-4, max_iters = 20))
#print(result)
#Finalize exageostat instance
exageostat_finalize()

1. Test Generating Z vector using given (x, y) locations with exact MLE computation.

library("exageostat")                                                   #Load ExaGeoStat-R lib.
sigma_sq        = 1                                                     #Initial variance.
beta            = 0.1                                                   #Initial smoothness.
nu              = 0.5                                                   #Initial range.
dmetric         = 0                                                     #0 --> Euclidean distance, 1--> great circle distance.
n               = 1600                                                  #n*n locations grid.
x               = rnorm(n = 1600, mean = 39.74, sd = 25.09)     #x measurements of n locations.
y               = rnorm(n = 1600, mean = 80.45, sd = 100.19)    #y measurements of n locations.
#Initiate exageostat instance
exageostat_init(hardware = list (ncores=2, ngpus=0, ts=320, lts=0,  pgrid=1, qgrid=1))#Initiate exageostat instance
#Generate Z observation vector based on given locations
data          = simulate_obs_exact( x, y, sigma_sq, beta, nu, dmetric)
#Estimate MLE parameters (Exact)
result        = exact_mle(data, dmetric, optimization = list(clb = c(0.001, 0.001, 0.001), cub = c(5, 5,5 ), tol = 1e-4, max_iters = 20))
#print(result)
#Finalize exageostat instance
exageostat_finalize()

Batch R script to distributed environment example

#!/bin/bash
#SBATCH --job-name=job_name
#SBATCH --output=output_file.txt
#SBATCH --partition=XXXX
#SBATCH --nodes=4
#SBATCH --ntasks=4
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=31
#SBATCH --time 00:30:00

srun Rscript Rtest.r

ecrc/exageostatR-dev documentation built on May 14, 2019, 3:06 a.m.