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scripts/plot_timings.R  view on Meta::CPAN

    label = "node 0",
    hjust = 0.5,
    size = txtsize
  ) +
  annotate(
    "text",
    x = 24,
    y = 70,
    label = "node 1",
    hjust = 0.2,
    size = txtsize
  ) +
  annotate(
    "text",
    x = 41,
    y = 70,
    label = "node 0",
    hjust = 0.0,
    size = txtsize
  ) +
  annotate(
    "text",
    x = 56,
    y = 70,
    label = "node 1",
    hjust = 0,
    size = txtsize
  )+
  xlab("Number of Workers") + ylab("Resource Use ( cp x s)")
ggsave(
  "spacetime_MCE_OMP.png",
  width = 6,
  height = 4,
  units = "in",
  dpi = 1200
)
################################################################################
datOpenMP$SpaceTime <- datOpenMP$Time * datOpenMP$Workers

## fit for time
f4<-gam(log2(Time)~ti(log2(Workers),bs="ts")+
          ti(log2(Num_threads),bs="ts")+
          ti(log2(Workers),log2(Num_threads),bs="ts"),data=datOpenMP)
datOpenMP$fitTime <- predict(f4,type="response")

## fit for space time
f5<-gam(log2(SpaceTime)~ti(log2(Workers),bs="ts")+
          ti(log2(Num_threads),bs="ts")+
          ti(log2(Workers),log2(Num_threads),bs="ts"),data=datOpenMP)
datOpenMP$fitSpaceTime <-predict(f5,type="response")

## find the 5% rows with the smallest Time & SpaceTime in the datOpenMP data
minTime<-datOpenMP[order(Time)][1:round(nrow(datOpenMP)*0.05)]
minSpaceTime<-datOpenMP[order(Time*Workers)][1:round(nrow(datOpenMP)*0.05)]



## three dimensional plots as contourplots

ggplot(datOpenMP, aes(x = Workers, y = Num_threads, z = fitTime)) +
  geom_contour(aes(colour = after_stat(level))) +theme_bw() + theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.text.x = element_text(size = 8),
    legend.position="bottom"
  ) + 
  scale_color_viridis(discrete=FALSE,name="Time (log2 sec)") +
  geom_point(data=minTime,aes(x=Workers,y=Num_threads),color="black",shape="+")+
  scale_y_continuous(trans = 'log2', breaks = c(1, 2, 4, 8, 18, 36,72)) +
  scale_x_continuous(trans = 'log2', breaks = c(1, 2, 4, 8, 18, 36,72)) +
  xlab("Number of Workers") + ylab("Number of Threads")

ggsave(
  "timings_MCE_with_OMP.png",
  width = 6,
  height = 6.5,
  units = "in",
  dpi = 1200
)

ggplot(datOpenMP, aes(x = Workers, y = Num_threads, z = fitSpaceTime)) +
  geom_contour(aes(colour = after_stat(level))) +theme_bw() + theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.text.x = element_text(size = 8),
    legend.position="bottom"
  ) + 
  scale_color_viridis(discrete=FALSE,name="Resource Use ( cp x s)") +
  geom_point(data=minSpaceTime,aes(x=Workers,y=Num_threads),color="black",shape="+")+
  scale_y_continuous(trans = 'log2', breaks = c(1, 2, 4, 8, 18, 36,72)) +
  scale_x_continuous(trans = 'log2', breaks = c(1, 2, 4, 8, 18, 36,72)) +
  xlab("Number of Workers") + ylab("Number of Threads")
ggsave(
  "spacetime_MCE_with_OMP.png",
  width = 6,
  height = 6.5,
  units = "in",
  dpi = 1200
)
################################################################################
## make a copy to replot the statistical model for the data
datOpenMP_log2<-datOpenMP
datOpenMP_log2$Time<-log2(datOpenMP_log2$Time)
datOpenMP_log2$Workers<-log2(datOpenMP_log2$Workers)
datOpenMP_log2$Num_threads<-log2(datOpenMP_log2$Num_threads)

f4_log2<-gam(Time~ti(Workers,bs="ts")+ti(Num_threads)+ti(Workers,Num_threads,bs="ts"),data=datOpenMP_log2)

predTerms<-predict(f4_log2,type="terms",se.fit=TRUE)

datOpenMP_log2$Weff<-predTerms$fit[,1]
datOpenMP_log2$Weffmin<-predTerms$fit[,1]-1.96*predTerms$se.fit[,1]
datOpenMP_log2$Weffmax<-predTerms$fit[,1]+1.96*predTerms$se.fit[,1]

datOpenMP_log2$Teff<-predTerms$fit[,2]
datOpenMP_log2$Teffmin<-predTerms$fit[,2]-1.96*predTerms$se.fit[,2]
datOpenMP_log2$Teffmax<-predTerms$fit[,2]+1.96*predTerms$se.fit[,2]

datOpenMP_log2$WeffTeff<-predTerms$fit[,3]
datOpenMP_log2$WeffTeffmin<-predTerms$fit[,3]-1.96*predTerms$se.fit[,3]
datOpenMP_log2$WeffTeffmax<-predTerms$fit[,3]+1.96*predTerms$se.fit[,3]

png("Worker_Thread_interaction.png",width=6,height=6,units="in",res=1200,pointsize=10)
par(xaxt="n",yaxt="n")
plot(f4_log2,select=3,se=F,rug=FALSE,main = "Worker x Thread Interaction",
     xlab = "Workers (log2)",ylab="Threads (log2)")
par(xaxt="s")
axis(1,at=log2(c(1, 2, 4, 8, 18, 36,72)),labels=c(1, 2, 4, 8, 18, 36,72))
par(yaxt="s")
axis(2,at=log2(c(1, 2, 4, 8, 18, 36,72)),labels=c(1, 2, 4, 8, 18, 36,72))
dev.off()

tick<-c(1, 2, 4, 8, 18, 36,72)
ytick<-seq(0,1.5,length.out=5)
pW<-draw(f4,rug=F,se=T,select=1,xlab="Workers (log2)")+
  scale_y_continuous(breaks=ytick)+
  scale_x_continuous(breaks=log2(tick),labels=tick)+
  theme_bw() + theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.text.x = element_text(size = 8),
    plot.title = element_text(hjust=0.5)