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xgboost CPU vs GPU
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| 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) | |
| y <- rbinom(N, 1, plogis(m)) | |
| tr <- sample.int(N, N * 0.90) | |
| trainer <- function(n_cpus, n_gpus, n_iterations, n_depth) { | |
| if (n_gpus == 0) { | |
| pt <- proc.time() | |
| set.seed(11111) | |
| model <- xgb.train(list(objective = "binary:logistic", eval_metric = "logloss", nthread = n_cpus, eta = 0.10, max_depth = n_depth, max_bin = 64, tree_method = "hist"), | |
| dtrain, watchlist = wl, nrounds = n_iterations, verbose = 0) | |
| my_time <- proc.time() - pt | |
| } else { | |
| pt <- proc.time() | |
| set.seed(11111) | |
| model <- xgb.train(list(objective = "gpu:binary:logistic", eval_metric = "logloss", nthread = n_cpus, eta = 0.10, max_depth = n_depth, max_bin = 64, tree_method = "gpu_hist", n_gpus = n_gpus), | |
| dtrain, watchlist = wl, nrounds = n_iterations, verbose = 0) | |
| my_time <- proc.time() - pt | |
| } | |
| rm(model) | |
| gc(verbose = FALSE) | |
| return(my_time) | |
| } | |
| dtrain <- xgb.DMatrix(X[tr,], label = y[tr]) | |
| dtest <- xgb.DMatrix(X[-tr,], label = y[-tr]) | |
| wl <- list(test = dtest) | |
| library(data.table) | |
| log_file <- "/home/laurae/Documents/R/xgboost_GPU_test/log.csv" | |
| data_file <- "/home/laurae/Documents/R/xgboost_GPU_test/data.csv" | |
| n_cpus <- c(1, 2, 4, 6, 9, 17, 18, 19, 35, 36, 37, 70, 71, 72) | |
| n_cpus_rep <- 2 | |
| n_gpus <- c(1, 2, 3, 4) | |
| n_gpus_rep <- 4 | |
| n_iters <- 500 | |
| n_depths <- 2:12 | |
| data <- rbindlist(list(data.table(Workload = "GPU", CPU = rep(rev(n_gpus), each = length(n_depths)), GPU = rep(rev(n_gpus), each = length(n_depths)), Depth = rep(rev(n_depths), length(n_gpus)), Repeats = n_gpus_rep, Speed = 0), | |
| data.table(Workload = "CPU", CPU = rep(rev(n_cpus), each = length(n_depths)), GPU = 0, Depth = rep(rev(n_depths), length(n_cpus)), Repeats = n_cpus_rep, Speed = 0))) | |
| colnames(data) <- c("Workload", "CPU Threads", "GPU Count", "Depth", "Repeats", "Speed") | |
| sink(log_file, append = FALSE) | |
| for (k in seq_len(nrow(data))) { | |
| cat(k, "\n", sep = "", file = log_file, append = TRUE) | |
| if (k > 1) { | |
| if ((data$`GPU Count`[k - 1] - data$`GPU Count`[k]) != 0) { | |
| rm(dtrain, dtest, wl) | |
| gc(verbose = FALSE) | |
| dtrain <- xgb.DMatrix(X[tr,], label = y[tr]) | |
| dtest <- xgb.DMatrix(X[-tr,], label = y[-tr]) | |
| wl <- list(test = dtest) | |
| gc(verbose = FALSE) | |
| } | |
| } | |
| zz <- 0 | |
| z <- 0 | |
| for (j in seq_len(data$Repeats[k])) { | |
| zz <- unname(trainer(data$`CPU Threads`[k], data$`GPU Count`[k], n_iters, data$Depth[k]))[3] | |
| z <- z + zz | |
| cat("[", sprintf(paste0("%0", floor(log10(max(data$Repeats)) + 1), "d"), j), " / ", data$Repeats[k], "] ", sprintf("%02d", data$`CPU Threads`[k]), " CPUs / ", data$`GPU Count`[k], " GPUs / ", sprintf("%02d", data$Depth[k]), " Depth: ", sprintf("%08.03f", zz), "s\n", sep = "", file = log_file, append = TRUE) | |
| gc(verbose = FALSE) | |
| } | |
| data[k, Speed := z / j] | |
| cat("[Done] ", sprintf("%02d", data$`CPU Threads`[k]), " CPUs / ", data$`GPU Count`[k], " GPUs / ", sprintf("%02d", data$Depth[k]), " Depth: ", sprintf("%08.03f", data$Speed[k]), "s\n\n", sep = "", file = log_file, append = TRUE) | |
| } | |
| sink() | |
| fwrite(data, data_file) |
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