Small R Script to generate graphs from periodic dump files for Marss and similar tools

graphs.r

#!/usr/bin/env Rscript

# First read in the file
args <- commandArgs(trailingOnly=TRUE)
# print(args)

# Arguments are like following:
# Operation output_file <operation specific #options...>

# Create a png image
create_png <- function(file_name, scale) {
	png(paste(file_name, ".png", sep=""), width=10000, height=scale*200, res=scale*20, pointsize=6)
}

create_png_low_res <- function(file_name, scale) {
	png(paste(file_name, ".png", sep=""), width=10000, height=scale*200, res=scale*10, pointsize=6)
}

plot_ipc <- function(data, type, coreid, col="red", ylab="IPC", newPlot=TRUE) {
	ipc_col <- c()

	if (type == "atom") {
		ipc_col <- paste("base_machine.atom_", coreid, ".thread_0.commit.ipc",
						 sep="")
	} else if (type == "ooo") {
		ipc_col <- paste("base_machine.ooo_", coreid, "_", coreid,
						 ".thread0.commit.ipc", sep="")
	}

	ipc_data <- data[[ipc_col]]

	if (newPlot) {
		plot(data$sim_cycle, ipc_data, type="n", xaxs="i", xlab="Cycles",
			 ylab=ylab,
			 xaxp=c(0, 10^9, 1000))
	}
	lines(data$sim_cycle, ipc_data, type="l", col=col, ylab=ylab)
}

# Helper function that returns cache data
get_cache_data <- function(data, cache_name) {
	cd <- list()
	cd[["read_hit"]] <- data[[paste("base_machine.", cache_name, ".cpurequest.count.hit.read.hit", sep="")]]
	cd[["write_hit"]] <- data[[paste("base_machine.", cache_name, ".cpurequest.count.hit.write.hit", sep="")]]
	cd[["read_miss"]] <- data[[paste("base_machine.", cache_name, ".cpurequest.count.miss.read", sep="")]]
	cd[["write_miss"]] <- data[[paste("base_machine.", cache_name, ".cpurequest.count.miss.write", sep="")]]
	cd[["total_hit"]] <- cd$read_hit + cd$write_hit
	cd[["total_miss"]] <- cd$read_miss + cd$write_miss
	cd[["total_access"]] <- cd$total_hit + cd$total_miss
	cd
}

plot_miss_ratio <- function(data, cache_id, coreid, col="red", ylab=NULL,
							newPlot=TRUE) {
	cd <- get_cache_data(data, paste(cache_id,"_",coreid,sep=""))
	miss_ratio <- cd$total_miss/cd$total_access

	if (newPlot) {
		yrange <- range(0.0, 1.0)
		xrange <- range(0, max(data$sim_cycle))
		plot(xrange, yrange, type="n", xaxs="i", xlab="Cycles",
			 ylab="Miss Ratio", xaxp=c(0, 10^9, 1000))
	}
	lines(data$sim_cycle, miss_ratio, type="l", col=col, ylab=ylab)
}

plot_miss_rate <- function(data, cache_id, coreid, col="red", ylab=NULL,
							newPlot=TRUE) {
	cd <- get_cache_data(data, paste(cache_id,"_",coreid,sep=""))

	if (newPlot) {
		plot(data$sim_cycle, cd$total_miss, type="n", xaxs="i", xlab="Cycles",
			 ylab="Miss Ratio", xaxp=c(0, 10^9, 1000))
	}
	lines(data$sim_cycle, cd$total_miss, type="l", col=col, ylab=ylab)
}

plot_branchpred <- function(data, type, coreid, col="red", ylab=NULL,
							newPlot=TRUE, separateYaxis=FALSE) {
	if (type == "ooo") {
		pred_hit <- data[[paste("base_machine.ooo_", coreid, "_", coreid,
								".thread0.branchpred.summary.correct",
								sep="")]]
		pred_miss <- data[[paste("base_machine.ooo_", coreid, "_", coreid,
								".thread0.branchpred.summary.mispred",
								sep="")]]
		pred_total <- pred_hit + pred_miss
	} else if (type == "atom") {
		pred_total <- data[[paste("base_machine.atom_", coreid,
								".thread_0.branch_predictions.predictions",
								sep="")]]
		pred_miss <- data[[paste("base_machine.atom_", coreid,
								".thread_0.branch_predictions.fail",
								sep="")]]
		pred_hit <- pred_total - pred_miss
	}

	pred_miss_ratio <- pred_miss / pred_total

	if (newPlot) {
		if (separateYaxis) {
			plot(data$sim_cycle, pred_miss_ratio, xaxs="i", type="n",
				 yaxt="n", xaxt="n", ylab=NULL)
		} else {
			plot(data$sim_cycle, pred_miss_ratio, xaxs="i", type="n",
				 xlab="Cycles", xaxp=c(0, 10^9, 1000),
				 ylab="BP Miss Ratio")
		}
	}

	if (separateYaxis) {
		axis(4)
	}

	lines(data$sim_cycle, pred_miss_ratio, type="l", col=col, ylab=ylab)
}


# IPC of OOO
# Specific Arugments [3..]: data number_of_cores
ipc_ooo <- function(args) {
	out_file_name <- args[2]
	data <- read.csv(args[3])
	num_cores <- strtoi(args[4])
	create_png(out_file_name, num_cores)
	par(mfrow=c(num_cores,1))

	for(i in seq(0, num_cores-1)) {
		plot_ipc(data, "ooo", i)
	}
}

# IPC of atom
# Specifc Argumments [3..] : data number_of_cores
ipc_atom <- function(args) {
	out_file_name <- args[2]
	data <- read.csv(args[3])
	num_cores <- strtoi(args[4])
	create_png(out_file_name, num_cores)
	par(mfrow=c(num_cores,1))

	for(i in seq(0, num_cores-1)) {
		plot_ipc(data, "atom", i)
	}
}

# IPC Mix - Merge atom and ooo ipc in one graph
# Specific Arguments [3..] : ooo_data atom_data number_of_cores
ipc_mix <- function(args) {
	out_file_name <- args[2]
	ooo_data <- read.csv(args[3])
	atom_data <- read.csv(args[4])
	num_cores <- strtoi(args[5])
	create_png(out_file_name, num_cores)
	par(mfrow=c(num_cores,1))
	colors <- rainbow(2)

	for(i in seq(0, num_cores-1)) {
		plot_ipc(ooo_data, "ooo", i, col=colors[1])
		plot_ipc(atom_data, "atom", i, col=colors[2], newPlot=FALSE)
		legend('topleft',lwd=c(1),col=colors,legend=c("atom","ooo"))
	}
}

# L1-D Miss Ratio
# Sepcific Arguments [3..] : data number_of_l1d_caches
l1d_l2_miss_ratio <- function(args) {
	out_file_name <- args[2]
	data <- read.csv(args[3])
	num_caches <- strtoi(args[4])
	create_png(out_file_name, num_caches)
	par(mfrow=c(num_caches,1))
	colors = rainbow(2)

	for(i in seq(0, num_caches-1)) {
		labels <- c(paste("L1-D-",i,sep=""), paste("L2-",i,sep=""))

		plot_miss_ratio(data, "L1_D", i, col=colors[1], ylab=labels[1])
		plot_miss_ratio(data, "L2", i, col=colors[2], ylab=labels[2],
						newPlot=FALSE)
		legend('topleft',lwd=c(1),col=colors,legend=labels)
	}
}

# L1-D L1-I and L2 Miss Rate
# Sepcific Arguments [3..] : data number_of_l1d_caches
l1_l2_miss_rate <- function(args) {
	out_file_name <- args[2]
	data <- read.csv(args[3])
	num_caches <- strtoi(args[4])
	create_png(out_file_name, num_caches)
	par(mfrow=c(num_caches,1))
	colors = rainbow(3)

	for(i in seq(0, num_caches-1)) {
		labels <- c(paste("L1-D-",i,sep=""), paste("L1-I-",i,sep=""), paste("L2-",i,sep=""))

		plot_miss_rate(data, "L1_D", i, col=colors[1], ylab=labels[1])
		plot_miss_rate(data, "L1_I", i, col=colors[2], ylab=labels[2],
					   newPlot=FALSE)
		plot_miss_rate(data, "L2", i, col=colors[3], ylab=labels[3],
					   newPlot=FALSE)

		legend('topleft',lwd=c(1),col=colors,legend=labels)
	}
}

# IPC L1-D L1-I and L2 Miss Rate
# Sepcific Arguments [3..] : data number_of_l1d_caches
ipc_l1_l2_miss_rate <- function(args) {
	out_file_name <- args[2]
	data <- read.csv(args[3])
	num_caches <- strtoi(args[4])
	create_png_low_res(out_file_name, num_caches*2)
	par(mfrow=c(num_caches*2,1))
	colors = rainbow(5)

	for(i in seq(0, num_caches-1)) {

		plot_ipc(data, "ooo", i)

		labels <- c(paste("L1-D-",i,sep=""), paste("L1-I-",i,sep=""), paste("L2-",i,sep=""))

		plot_miss_rate(data, "L1_D", i, col=colors[1], ylab=labels[1])
		plot_miss_rate(data, "L1_I", i, col=colors[2], ylab=labels[2],
					   newPlot=FALSE)
		plot_miss_rate(data, "L2", i, col=colors[3], ylab=labels[3],
					   newPlot=FALSE)

		legend('topleft',lwd=c(1),col=colors,legend=labels)
	}
}


do.call(args[1], list(args))
warnings()