Memory Monitor

mem_monitor is self contained header file containing a C++11 memory monitor. This is a modified version obtained from https://github.com/mpetri/mem_monitor, that uses a multi-platform procedure for obtaining the current memory usage.

This version, however, has a limited amount of information that can be fetched:

Metric	Description

     pid | The process ID
     VmPeak | Peak virtual memory size
     VmRSS | Resident set size

Usage

Here an example of how the class can be used:

#include "mem_monitor.hpp"

int main(int argc, char const *argv[])
{
	mem_monitor mm("mem-mon-out.csv"); // poll every 50 milliseconds

	mm.event("vector init"); // optional event
	std::vector<uint64_t> vec(50000);
	/* do more stuff */
	mm.event("sort vector"); // optional event
	std::sort(vec.begin(),vec.end());
}

the information are periodically written to the output file and flushed once the object is destroyed.

Additionally, the granularity with which the monitoring thread polls

mem_monitor mm("mem-mon-out.csv",std::chrono::milliseconds(5));

to use the memory monitor, your program has to be linked to the pthread and rt library and the c++11 flags:

g++ -std=c++11 -o a.out example.cpp -l pthread -l rt

Note: The object does not need to be created at the beginning of main(). The monitor can also be used to track only certain aspects of the executing program.

Output

The output produced by the class consist of a CSV file:

time_ms	pid	VmPeakVmRSS	event
1	12382	15995289664116	"vector init"
51	12382	159952896682142	"vector init"
101	12382	15995289848445	"vector init"
151	12382	16044032744676	"sort vector"
201	12382	19127910976845	"sort vector"
251	12382	20180992027217	"sort vector"
301	12382	22019276446411	"sort vector"

where time_ms corresponds to the number of milliseconds since the creation of the object.

Visualization

The output can be visualized, for example, with the following R script:

library(ggplot2)
library(reshape2)
library(sitools)
library(scales)

data <- read.csv(file="res-mon.csv",sep=";",header=TRUE);
tdata <- melt(data,id=c("time_ms","event"))
tdata <- subset(tdata,variable!="pid")
dup <-tdata[!duplicated(tdata$event),]

plot <- ggplot(tdata,aes(time_ms,value,color=variable))
plot <- plot + geom_line(size=1)
#plot <- plot + scale_y_log10(expand=c(0,0),labels=f2si,breaks = trans_breaks("log10", function(x) 10^x),"Memory Usage [Byte]")
plot <- plot + scale_y_continuous(expand=c(0,0),labels=f2si,"Memory Usage [Byte]")
#plot <- plot + annotation_logticks(sides = "lr")
plot <- plot + scale_x_continuous(expand=c(0,0),labels=f2si,"Time [Millseconds]")
plot <- plot + scale_linetype(name="Events") + scale_color_discrete(name="Metric")
plot <- plot + geom_vline(data=dup,aes(xintercept = time_ms,linetype=event),show_guide=TRUE)
plot <- plot + theme_grey() + ggtitle("Memory Usage over Time")
ggsave(plot,file="memory_usage.png")

License

LGPLv3