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+<div id="table-of-contents">
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+<h2>Table of Contents</h2>
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+<div id="text-table-of-contents">
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+<ul>
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+<li><a href="#sec-1">1. Introduction</a>
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+<ul>
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+<li>
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+<ul>
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+<li><a href="#sec-1-0-1">1.0.1. How to compile</a></li>
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+<li><a href="#sec-1-0-2">1.0.2. Software dependencies</a></li>
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+</ul>
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+</li>
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+</ul>
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+</li>
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+<li><a href="#sec-2">2. Gantt Charts of the whole Trace</a></li>
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+<li><a href="#sec-3">3. Table Summary</a></li>
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+<li><a href="#sec-4">4. State Duration during the Execution Time</a></li>
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+<li><a href="#sec-5">5. Distribution Histograms</a></li>
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+</ul>
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+</div>
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+</div>
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+```{r Setup, echo=FALSE}
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+opts_chunk$set(echo=FALSE)
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+```
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+
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+
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+
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+```{r Install_R_libraries}
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+InstalledPackage <- function(package)
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+{
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+ available <- suppressMessages(suppressWarnings(sapply(package, require, quietly = TRUE, character.only = TRUE, warn.conflicts = FALSE)))
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+ missing <- package[!available]
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+ if (length(missing) > 0) return(FALSE)
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+ return(TRUE)
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+}
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+
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+CRANChoosen <- function()
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+{
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+ return(getOption("repos")["CRAN"] != "@CRAN@")
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+}
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+
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+UsePackage <- function(package, defaultCRANmirror = "http://cran.at.r-project.org")
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+{
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+ if(!InstalledPackage(package))
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+ {
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+ if(!CRANChoosen())
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+ {
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+ chooseCRANmirror()
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+ if(!CRANChoosen())
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+ {
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+ options(repos = c(CRAN = defaultCRANmirror))
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+ }
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+ }
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+
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+ suppressMessages(suppressWarnings(install.packages(package)))
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+ if(!InstalledPackage(package)) return(FALSE)
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+ }
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+ return(TRUE)
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+}
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+
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+# Now install desired libraries
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+libraries <- c("ggplot2", "plyr", "data.table", "RColorBrewer")
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+for(libr in libraries)
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+{
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+ if(!UsePackage(libr))
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+ {
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+ stop("Error!", libr)
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+ }
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+}
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+```
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+
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+```{r Load_R_files}
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+# Load ggplot and plyr just for the following cases
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+ library(ggplot2)
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+ library(plyr)
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+ library(data.table)
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+ library(RColorBrewer)
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+
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+# Defining non-computation states:
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+def_states<-c("Initializing","Deinitializing","Overhead","Nothing","Sleeping","Freeing","Allocating","WritingBack","FetchingInput","PushingOutput","Callback","Progressing","Unpartitioning","AllocatingReuse","Reclaiming","DriverCopy","DriverCopyAsync","Scheduling","Executing")
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+
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+# Function for reading .csv file
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+read_df <- function(file,range1,range2) {
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+ df<-read.csv(file, header=FALSE, strip.white=TRUE)
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+ names(df) <- c("Nature","ResourceId","Type","Start","End","Duration", "Depth", "Value")
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+ df = df[!(names(df) %in% c("Nature","Type", "Depth"))]
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+ df$Origin<-as.factor(as.character(file))
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+
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+# Changing names if needed:
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+ df$Value <- as.character(df$Value)
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+ df$Value <- ifelse(df$Value == "F", "Freeing", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "A", "Allocating", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "W", "WritingBack", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "No", "Nothing", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "I", "Initializing", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "D", "Deinitializing", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "Fi", "FetchingInput", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "Po", "PushingOutput", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "C", "Callback", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "B", "Overhead", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "Sc", "Scheduling", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "E", "Executing", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "Sl", "Sleeping", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "P", "Progressing", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "U", "Unpartitioning", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "Ar", "AllocatingReuse", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "R", "Reclaiming", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "Co", "DriverCopy", as.character(df$Value))
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+ df$Value <- ifelse(df$Value == "CoA", "DriverCopyAsync", as.character(df$Value))
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+
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+# Small cleanup
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+df$Start<-round(df$Start,digit=1)
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+df$End<-round(df$End,digit=1)
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+df$ResourceId<-as.factor(df$ResourceId)
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+df$Value<-as.factor(df$Value)
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+
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+# Start from zero
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+ m <- min(df$Start)
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+ df$Start <- df$Start - m
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+ df$End <- df$Start+df$Duration
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+
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+# Return data frame
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+ df
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+}
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+```
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+
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+```{r Load_traces}
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+df<-data.frame()
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+if( !exists("input_traces") )
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+ input_traces<-c("example.native.trace.csv", "example.simgrid.trace.csv")
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+
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+for (i in 1:length(input_traces)){
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+ dfs<-read_df(input_traces[i])
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+ df<-rbindlist(list(df,dfs))
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+}
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+
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+# Color palettes
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+colourCount = length(unique(df$Value))
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+getPalette = colorRampPalette(brewer.pal(9, "Set1"))
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+
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+# Order of Value so we can have good colors
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+ker_states<-as.character(unique(df[!(df$Value %in% def_states),Value]))
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+ordered_states<-append(sort(ker_states), def_states)
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+df$Value <- factor(df$Value, levels=ordered_states)
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+
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+# Order of ResourceId so we can have y-axis
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+df$ResourceId <- factor(df$ResourceId, levels=sort(as.character(unique(df$ResourceId))))
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+```
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+
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+# Introduction
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+
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+This document presents a basic analysis of multiple StarPU
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+traces. First, paje *traces* will be transferred into *.csv* files and
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+then we analyze them with **R**. This summary is a first step that
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+should help researchers verify their hypothesis or find problematic
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+areas that require more exhaustive investigation.
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+
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+Be cautious, as the following results are only a brief analysis of
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+the traces and many important phenomena could still be hidden. Also,
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+be very careful when comparing different states or traces. Even
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+though some large discrepancies can be irrelevant, in other cases
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+even the smallest differences can be essential in understanding what
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+exactly happened during the StarPU execution.
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+
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+### How to compile
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+
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+ ./starpu_summary.sh example.native.trace example.simgrid.trace
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+
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+### Software dependencies
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+
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+In order to run this analysis you need to have R installed:
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+
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+ sudo apt-get install r-base
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+
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+Easiest way to transform *paje* traces generated by StarPU to *.csv* is to use *pjdump* program (<https://github.com/schnorr/pajeng>), so we encourage users to install it.
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+
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+When R is installed, one will need to start R (e.g., from terminal) and install *knitr* package:
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+
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+ R> install.packages("knitr")
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+
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+Additional R packages used in this analysis (*ggplot2, plyr, data.table, RColorBrewer*) will be installed automatically when the document is compiled for the first time. If there is any trouble, install them by hand directly from R (the same way as *knitr*)
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+
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+# Gantt Charts of the whole Trace
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+
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+First, we show a simple gantt chart of every trace. X-axis is a
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+simple timeline of the execution, *Resources* on y-axis correspond
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+to different CPUs/GPUs that were used and finally different colors
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+represent different *States* of the application.
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+
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+This kind of figures can often point to the idle time or
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+synchronization problems. Small disadvantage is that in most cases
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+there are too many states, thus it is impossible to display them all
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+on a single plot without aggregation. Therefore for any strange
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+behavior at a certain part of the trace, we strongly suggest to zoom
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+on the interval it occurred.
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+
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+```{r Gantt1}
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+ggplot(df,aes(x=Start,xend=End, y=factor(ResourceId), yend=factor(ResourceId),color=Value)) +
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+ theme_bw() + scale_color_manual(name="State",values=getPalette(colourCount)) +
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+ geom_segment(size=8) + ylab("Resource") + xlab("Time [ms]") +
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+ facet_wrap(~Origin,ncol=1,scale="free_y")
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+```
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+
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+Second, we will concentrate only on computation kernel states, to
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+get rid of visualization artifacts that can be introduced by other
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+(sometimes irrelevant) states. Normally, this plot should not be too
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+different from the previous one.
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+
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+```{r Gantt2}
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+# Select only computation kernels
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+ df1 <- df[!(df$Value %in% c("Initializing","Deinitializing","Overhead","Nothing","Sleeping","Freeing","Allocating","WritingBack","FetchingInput","PushingOutput","Callback","Progressing","Unpartitioning","AllocatingReuse","Reclaiming","DriverCopy","DriverCopyAsync","Scheduling","Executing")),]
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+
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+# Start from zero
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+ m <- min(df1$Start)
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+ df1$Start <- df1$Start - m
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+ df1$End <- df1$Start+df1$Duration
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+
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+# Plot
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+ ggplot(df1,aes(x=Start,xend=End, y=factor(ResourceId), yend=factor(ResourceId),color=Value)) +
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+ theme_bw() + scale_color_manual(name="State",values=getPalette(colourCount)) +
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+ geom_segment(size=8) + ylab("Resource") + xlab("Time [ms]") +
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+ facet_wrap(~Origin,ncol=1,scale="free_y")
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+```
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+
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+# Table Summary
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+
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+Here we present how much time application spent in each state
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+(OverallDuration), how many times it was in that state (Count),
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+mean and median values of duration (Mean and Median), and finally
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+what is a standard deviation (StandDev).
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+
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+General information provided by this table can sometimes give an
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+idea to application experts which parts of code are not working as
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+desired. Be aware that this kind of tables hide many important
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+things, such as outliers, multiple modes, etc.
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+
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+```{r Table}
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+options(width=120)
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+ddply(df,.(Value,Origin), summarize, OverallDuration=sum(Duration), Count=length(Duration), Mean=mean(Duration), Median=median(Duration), StandDev=sd(Duration))
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+```
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+
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+# State Duration during the Execution Time
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+
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+Now, we show how duration of each state was changing during the
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+execution. This can display a general behavior of a state; show if
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+there are outliers or multiple modes; are some events occurring in
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+groups, etc. . It can also suggest a strange behavior of a state
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+during a certain time interval, which should be later investigated
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+more carefully.
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+
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+ However, since each event is represented by a single point (and
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+there is no "alpha" factor), those events that happen almost
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+simultaneously are overplotted. Therefore density of events along
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+execution time may not be easy to read.
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+
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+```{r Dur}
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+ggplot(df,aes(x=Start,y=Duration)) + geom_point(aes(color=Value)) + theme_bw() + scale_color_manual(name="State",values=getPalette(colourCount)) + ggtitle("State Duration during the Execution Time") + theme(legend.position="none") + ylab("Duration [ms]") + xlab("Time [ms]") + facet_grid(Value~Origin, scale="free_y")
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+```
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+
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+# Distribution Histograms
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+
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+Finally, we show a distribution of *Duration* for each state in form
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+of histograms. X-axis is partitioned into bins with equidistant time
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+intervals in milliseconds, while y-axis represents the number of
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+occurrences inside such intervals for a certain state. Note that for
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+the first plot y-axis is not fixed, meaning that the scale changes
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+from one row to another. This plot allows to not only to see what
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+was the most frequent duration of a state, but also to compare
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+duration between different states.
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+
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+```{r Hist1}
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+ggplot(df, aes(x=Duration)) + geom_histogram(aes(y=..count..,fill=factor(Value)),binwidth = diff(range(df$Duration))/30) + theme_bw() + scale_fill_manual(name="State",values=getPalette(colourCount)) + ggtitle("Histograms for State Distribution") + ylab("Count") + xlab("Duration [ms]") + theme(legend.position="none") + facet_grid(Value~Origin,scales = "free_y")
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+```
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+
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+Similar to the previous figure, only now traces are showed vertically
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+instead of horizontally. Note that for this plot x-axis is not fixed,
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+meaning that the scale changes from one column to another. This plot
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+allows to compare frequency of different states and in case of
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+multiple traces to easily compare duration distribution for each
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+state.
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+
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+```{r Hist2}
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+ggplot(df, aes(x=Duration)) + geom_histogram(aes(y=..count..,fill=factor(Value)),binwidth = diff(range(df$Duration))/30) + theme_bw() + scale_fill_manual(name="State",values=getPalette(colourCount)) + ggtitle("Histograms for State Distribution") + ylab("Count") + xlab("Duration [ms]") + theme(legend.position="none") + facet_grid(Origin~Value,scales = "free_x")
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+```
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