Gradient boosting machine still running after 13 hours - should I terminate?

I'm running a gradient boosting machine with the caret package in RStudio on a fairly large healthcare dataset, ~700k records, 600+ variables (most are sparse binary) predicting a binary outcome. It's running very slow on my work laptop, over 13 hours.

Given the dimensions of my data, was I too ambitious choosing hyperparameters of 5,000 iterations and a shrinkage parameter of .001?

My code:
### Partition into Training and Testing data sets ###

set.seed(123)

inTrain <- createDataPartition(asd_data2$K_ASD_char, p = .80, list = FALSE)

train <- asd_data2[ inTrain,]

test <- asd_data2[-inTrain,]

### Fitting Gradient Boosting Machine ###

set.seed(345)

gbmGrid <- expand.grid(interaction.depth=c(1,2,4), n.trees=5000, shrinkage=0.001, n.minobsinnode=c(5,10,15))

gbm_fit_brier_2 <- train(as.factor(K_ASD_char) ~ .,

tuneGrid = gbmGrid,

data=train,

trControl=trainControl(method="cv", number=5, summaryFunction=BigSummary, classProbs=TRUE, savePredictions=TRUE),

train.fraction = 0.5,

method="gbm",

metric="Brier", maximize = FALSE,

preProcess=c("center","scale"))