Laurae Laurae2

Code:

library(anytime)
library(flipTime)
library(data.table)

set.seed(1)
system.time({
  some_dates <- as.Date("2020-12-31") - sample(40000, 1e7, TRUE)

Step 1: download cplexAPI : Step 2: edit src/Makevars.win like the following, make sure cplex1280 is equal to your version you must change to the corresponding number you can find in C:/Program Files/IBM/ILOG/CPLEX_Studio128/cplex/bin/x64_win64:

PKG_CPPFLAGS=-g -D_R_=1 -DUSE_R=1 -I"C:/Program Files/IBM/ILOG/CPLEX_Studio128/cplex/include"
PKG_LIBS=-L"C:/Program Files/IBM/ILOG/CPLEX_Studio128/cplex/bin/x64_win64" -lm -lcplex1280

Step 3: in a R console, on the right folder:

	Walk on thin ice in R for gcc-7/g++-7 + CUDA 10.0.130

	Note: OpenCL can run on Single Precision (gpu_use_dp = FALSE) or Double Precision (gpu_use_dp = TRUE) whereas CUDA is strictly Double Precision in LightGBM.

	--

	STEP 1: HACK IN SOME FILES

	Hack in the following:
	```

	library(xgboost)
	library(lightgbm)
	library(data.table)

	setwd("/home/laurae/Documents/R/GBM-perf")

	n_threads <- 16

	data <- fread("HIGGS.csv")
	labels <- data$V1

	library(xgboost)

	set.seed(1)
	N <- 1000000
	p <- 100
	pp <- 25
	X <- matrix(runif(N * p), ncol = p)
	betas <- 2 * runif(pp) - 1
	sel <- sort(sample(p, pp))
	m <- X[, sel] %*% betas - 1 + rnorm(N)

	set.seed(1)
	N <- 500000
	p <- 100
	pp <- 25
	X <- matrix(runif(N * p), ncol = p)
	betas <- 2 * runif(pp) - 1
	sel <- sort(sample(p, pp))
	m <- X[, sel] %*% betas - 1 + rnorm(N)
	y <- rbinom(N, 1, plogis(m))

	library(xgboost)

	set.seed(1)
	N <- 10000000
	p <- 100
	pp <- 25
	X <- matrix(runif(N * p), ncol = p)
	betas <- 2 * runif(pp) - 1
	sel <- sort(sample(p, pp))
	m <- X[, sel] %*% betas - 1 + rnorm(N)

	library(Rcpp)

	Sys.setenv(PKG_CXXFLAGS = "-fopenmp -std=c++11")

	cppFunction(code = '#include <Rcpp.h>
	#include <omp.h>

	Rcpp::NumericVector streamer(Rcpp::NumericVector threads, int triad_size = 2147483647, int repeats = 10, int repeats_alloc = 3, bool simd = false) {

	int i;

	# library(data.table)
	# N=2e9; K=100
	# set.seed(1)
	# DT <- data.table(
	# id1 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char)
	# id2 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char)
	# id3 = sample(sprintf("id%010d",1:(N/K)), N, TRUE), # small groups (char)
	# id4 = sample(K, N, TRUE), # large groups (int)
	# id5 = sample(K, N, TRUE), # large groups (int)
	# id6 = sample(N/K, N, TRUE), # small groups (int)

	# List all possible power config GUIDs in Windows
	# Run: this-script.ps1 \| Out-File powercfg.ps1
	# Then edit and run powercfg.ps1
	# (c) Pekka "raspi" Järvinen 2017

	$powerSettingTable = Get-WmiObject -Namespace root\cimv2\power -Class Win32_PowerSetting
	$powerSettingInSubgroubTable = Get-WmiObject -Namespace root\cimv2\power -Class Win32_PowerSettingInSubgroup

	Get-WmiObject -Namespace root\cimv2\power -Class Win32_PowerSettingCapabilities \| ForEach-Object {
	$tmp = $_.ManagedElement