Month: January 2018

Parallel Processing

CPU1 CPU2 CPU3

#include <pthread.h>
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <assert.h>

void* f1(void* arg){
	int whos_better;
	whos_better = 1;
	while(1)
		printf("Thread 1: thread %d is better.\n", whos_better);

		return NULL;
}

void* f2(void* arg){
	int whos_better;
	whos_better = 2;
	while(1)
		printf("Thread 2: thread %d is better.\n", whos_better);

	return NULL;
}

int main(int argc, char **argv){
	pthread_t th1, th2;

	pthread_create(&th1, NULL, f1, NULL);	
	pthread_create(&th2, NULL, f2, NULL);

	pthread_join(th1, NULL);
	pthread_join(th2, NULL);

	pthread_exit(NULL);
}

File System

Key Abstractions
1. file
2. file name
3. Directory Tree

#include 
#include 
#include 
#include 

int main(int argc, char **argv){
	int fd;
	ssize_t len;
	char *filename;
	int key, srch_key, new_value;

	if(argc < 4){
		fprintf(stderr, "usage: sabotage filename key value\n");
		exit(EXIT_FAILURE);
	}

	filename = argv[1];
	srch_key = strtol(argv[2], NULL, 10);
	new_value = strtol(argv[3], NULL, 10);
}

Naive Memory Model

CPU
Memory: fast, random access, temporary
Disk: slow, sequential access, durable

CPU Register <- cache -> Main Memory
10 cycle latency, 256 bytes / cycle

Memory Hierarchy
CPU
Main Memory

Write Policy
Hit:
– write-through
– write-back

Miss:
– write-allocate
– no-write allocate

Virtual Address Abstraction
Kernel addresses, user stack, heap, uninitialized data, initialized data, code

ggplot2

> library(ggplot2)
> names(faithful)
[1] "eruptions" "waiting" 
> plot(faithful$eruptions, xlab = "sample number", ylab = "eruption times (min)", main = "Old Faithful Eruption Times")


qplot(x = waiting,
	data = faithful,
	binwindth = 3,
	main = "Waiting time to next eruption(min)")
ggplot(faithful, aes(x = waiting)) +
	geom_histogram(bindwidth = 1)

> names(mtcars)
[1] “mpg” “cyl” “disp” “hp” “drat” “wt” “qsec” “vs” “am” “gear”
[11] “carb”

Basic syntax

a = 3.2
a = "a string"
print("The variable 'a' stores:"); print(a)

a = 10; b = 5; c = 1
if (a < b){
  d = 1
}else if (a == b){
  d = 2
}else{
  d = 3
}
d
stopifnot(d==3)

sum = 0
i = 1
while(i <= 10){
  sum = sum + i
  i = i + 1
}
stopifnot(sum==55)

mySum = function(a,b){
  return(a + b)
}

x = vector(length=3, mode="numeric")

y = c(4,3,3)

stopifnot( x == c(0,0,0))

stopifnot(length(y) == 3)

x[1] = 2
x[3] = 1
stopifnot( x == c(2,0,1) )

a = 2*x + y
stopifnot( a == c(8,3,4) )

a = a - 1
stopifnot( a == c(7,2,3) )

stopifnot( (a>=7) == c(TRUE,FALSE,FALSE))
stopifnot( (a==2) == c(FALSE,TRUE,FALSE))

mask = c(TRUE,FALSE,TRUE)
stopifnot( a[mask] == c(7,3) )

indices = c(1,3)
stopifnot( a[indices] == c(7,3))

stopifnot( a[c(-1,-3)] == c(2) )

stopifnot( any(c(FALSE,TRUE,FALSE)) )
stopifnot( all(c(TRUE,TRUE,TRUE)) )
stopifnot( which(c(TRUE,FALSE,TRUE)) == c(1,3) )

b = rep(3.2, times=5)
stopifnot( b == c(3.2, 3.2, 3.2, 3.2, 3.2))

w = seq(0,3)
stopifnot(w == c(0,1,2,3))

x = seq(0,1,by=0.2)
stopifnot(x == c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0))

y = seq(0,1,length.out=3)
stopifnot( x == c(0.0, 0.5, 1.0) )

z = 1:10
stopifnot(z == seq(1,10,by=1))

sum = 0
for(i in z){
	sum = sum + i
}
stopifnot(sum == 55)

x = 1:10
f = function(a){
	a[1] = 10
}
f(x)
stopifnot(x == 1:10)

Manipulating Array

[,1][.2]
y `= [,1] 1 5
[,2] 2 6

2 * y + 1
y `= [,1] 3 11
[,2] 5 13

y %*% Y
y `= [,1] 11 35
[,2] 14 46

outer(x[,1], x[,1])
[,1] [,2] [,3] [,4] [,5]
[1,] 1 5 9 13 17
[2,] 2 6 10 14 18
[3,] 3 7 11 15 19
[4,] 4 8 12 16 20

rbind(x[1,], x[1,])
rbindは縦に結合
[,1] [,2] [,3] [,4] [,5]
[1,] 1 5 9 13 17
[2,] 1 5 9 13 17

cbind(x[1,], x[1,])

L = list(name = ‘John’, age=55, no.children=2, children.ages = c(15, 18))
names(L) name age no.children children.ages
L[[2]] 55
L$name John
L[‘name’] John
L$children.ages[2]
L[[4]][2]

names(R) = c(“NAME”, “AGE”, “SALARY”)

if-Else

a = 10; b = 5; c = 1
if (a < b){
	d = 1
} else if (a == b){
	d = 2
} else {
	d = 3
}
print(d)

R for loop

total = function(n){
    sum = 0
    for(i in 1:100){
    sum = sum + i
    }
    
    print(sum)
    return(sum)
}
total(100)

total = function(n){
    sum = 5050
    num = n
    repeat {
      sum = sum - num
      num = num - 1
      if(sum == 0)break
    }
    return(sum)
}

total = function(){
    sum = 0
    a = 1
    b = 10
    
    while (a

R command

ls() – list variable names in workspace memory
save.image(file=”R_workspace”) – saving variables to a file
save(new.var, legal.var.name, file = “R_workspace”) – save specified variables
load(“R_workspace”) – load variables saved in a file
system(“ls -al”) – executes a command in the shell, for example ls -al

scalar
numeric, integer, logical

ordered factor

current.season = factor("summer", levels = c("summer", "fall", "winter", "spring"), ordered = TRUE)

x = c(4,3,3,4,3,1)
outcome: 433431
length(x)
outcome: length = 6
y = vector(mode=”logical”, length=4)
outocome: y= FALSE FALSE FALSE FALSE
z = vector(length=3, mode==”numeric”)
outcome: z = 0 0 0

q = rep(3.2, times = 10)
q = 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2
w = seq(0, 1 by=0.1)
w – 0.0 0.1 0.2 … 0.9 1.0
w = seq(0, 1, length.out 11)

w <= 0.5 0.0 0.1 0.2 0.3 0.4 0.5 any(w <= 0.5) TRUE all(w <= 0.5) FALSE which(w <= 0.5) 1 2 3 4 5 6

List of DataSet

> data(iris)
> head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
> levels(iris$Species)
[1] "setosa"     "versicolor" "virginica" 

> install.packages("ggplot2")

R: statistics, bio-statistics, social sciences
Matlab: engineering, applied math
python: web development, scripting

Running R – interactively

source("foo.R")
R CMD BATCH foo.R
Rscript foo.R

R Help documentation
>help {utils}
R Documentation
Documentation
Description
help is the primary interface to the help systems.
Usage
help(topic, package = NULL, lib.loc = NULL,
verbose = getOption(“verbose”),
try.all.packages = getOption(“help.try.all.packages”),
help_type = getOption(“help_type”))….

Security background

confidentiality, integrity, availability

The security of a system, application, or protocol is always relative to:
– a set of desired properties
– an adversary with specific capabilities

Control systems
sensor, system, actuator, controller

Open – loop
Desired output response -> controller -> actuator -> process -> output

closed – loop
Desired output response -> error -> controller -> actuator -> process -> output