# Chapter 2 Basic Computer Architecture

## 2.1 Typical Machine Layout

Figure based on M. L. Scott, Programming Language Pragmatics, Figure 5.1, p. 205

## 2.2 Structure of Lab Workstations

### 2.2.1 Processor and Cache

luke@l-lnx200 ~% lscpu
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              8
On-line CPU(s) list: 0-7
Core(s) per socket:  4
Socket(s):           1
NUMA node(s):        1
Vendor ID:           GenuineIntel
CPU family:          6
Model:               94
Model name:          Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
Stepping:            3
CPU MHz:             3895.093
CPU max MHz:         4000.0000
CPU min MHz:         800.0000
BogoMIPS:            6816.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            256K
L3 cache:            8192K
NUMA node0 CPU(s):   0-7
Flags: ...
• There is a single quad-core processor with hyperthreading that acts like eight separate processors
• Each has 8Mb of L3 cache

### 2.2.2 Memory and Swap Space

luke@l-lnx200 ~% free
total        used        free      shared  buff/cache   available
Mem:       32866464      396876    27076056       33620     5393532    31905476
Swap:      16449532           0    16449532
• The workstations have about 32G of memory.
• The swap space is about 16G.

### 2.2.3 Disk Space

Using the df command produces:

luke@l-lnx200 ~% df
Filesystem                    1K-blocks      Used Available Use% Mounted on
...
/dev/mapper/vg00-root          65924860  48668880  13884156  78% /
/dev/mapper/vg00-tmp            8125880     28976   7661092   1% /tmp
/dev/mapper/vg00-var           75439224  13591304  57992768  19% /var
/dev/mapper/vg00-scratch      622877536     33068 622844468   1% /var/scratch
...
...
netapp2:/vol/students         235929600  72504448 163425152  31% /mnt/nfs/netapp2/students
...
• Local disks are large but mostly unused
• Space in /var/scratch can be used for temporary storage.
• User space is on network disks.
• Network speed can be a bottle neck.

### 2.2.4 Performance Monitoring

Using the top command produces:

top - 11:06:34 up  4:06,  1 user,  load average: 0.00, 0.01, 0.05
Tasks: 127 total,   1 running, 126 sleeping,   0 stopped,   0 zombie
Cpu(s):  0.0%us,  0.0%sy,  0.0%ni, 99.8%id,  0.2%wa,  0.0%hi,  0.0%si,  0.0%st
Mem:  16393524k total,   898048k used, 15495476k free,   268200k buffers
Swap: 18481148k total,        0k used, 18481148k free,   217412k cached

PID USER      PR  NI  VIRT  RES  SHR S %CPU %MEM    TIME+  COMMAND
1445 root      20   0  445m  59m  23m S  2.0  0.4   0:11.48 kdm_greet
1 root      20   0 39544 4680 2036 S  0.0  0.0   0:01.01 systemd
2 root      20   0     0    0    0 S  0.0  0.0   0:00.00 kthreadd
3 root      20   0     0    0    0 S  0.0  0.0   0:00.00 ksoftirqd/0
5 root       0 -20     0    0    0 S  0.0  0.0   0:00.00 kworker/0:0H
6 root      20   0     0    0    0 S  0.0  0.0   0:00.00 kworker/u:0
7 root       0 -20     0    0    0 S  0.0  0.0   0:00.00 kworker/u:0H
8 root      RT   0     0    0    0 S  0.0  0.0   0:00.00 migration/0
9 root      RT   0     0    0    0 S  0.0  0.0   0:00.07 watchdog/0
10 root      RT   0     0    0    0 S  0.0  0.0   0:00.00 migration/1
12 root       0 -20     0    0    0 S  0.0  0.0   0:00.00 kworker/1:0H
13 root      20   0     0    0    0 S  0.0  0.0   0:00.00 ksoftirqd/1
14 root      RT   0     0    0    0 S  0.0  0.0   0:00.10 watchdog/1
15 root      RT   0     0    0    0 S  0.0  0.0   0:00.00 migration/2
17 root       0 -20     0    0    0 S  0.0  0.0   0:00.00 kworker/2:0H
18 root      20   0     0    0    0 S  0.0  0.0   0:00.00 ksoftirqd/2
...
• Interactive options allow you to kill or renice (change the priority of) processes you own.

• The command htop may be a little nicer to work with.

• A GUI tool, System Monitor, is available from one of the menus. From the command line this can be run as gnome-system-monitor.

Another useful command is ps (process status)

luke@l-lnx200 ~% ps -u luke
PID TTY          TIME CMD
4618 ?        00:00:00 sshd
4620 pts/0    00:00:00 tcsh
4651 pts/0    00:00:00 ps

There are many options; see man ps for details.

## 2.3 Processors

### 2.3.1 Basics

Processors execute a sequence of instructions

Each instruction requires some of

• decoding instruction
• fetching operands from memory
• performing an operation (add, multiply, …)
• etc.

Older processors would carry out one of these steps per clock cycle and then move to the next.

Most modern processors use pipelining to carry out some operations in parallel.

### 2.3.2 Pipelining

A simple example:

$$s \leftarrow 0$$ for $$i = 1$$ to $$n$$ do     $$s \leftarrow s + x_i y_i$$ end

Simplified view: Each step has two parts,

• Fetch $$x_i$$ and $$y_i$$ from memory
• Compute $$s = s + x_i y_i$$

Suppose the computer has two functional units that can operate in parallel,

• An Integer unit that can fetch from memory
• A Floating Point unit that can add and multiply

If each step takes roughly the same amount of time, a pipeline can speed the computation by a factor of two:

• Floating point operations are much slower than this.
• Modern chips contain many more separate functional units.
• Pipelines can have 10 or more stages.
• Some operations take more than one clock cycle.
• The compiler or the processor orders operations to keep the pipeline busy.
• If this fails, then the pipeline stalls.

### 2.3.3 Superscalar Processors, Hyper-Threading, and Multiple Cores

Some processors have enough functional units to have more than one pipeline running in parallel.

Such processors are called superscalar.

In some cases there are enough functional units per processor to allow one physical processor to pretend like it is two (somewhat simpler) logical processors. This approach is called hyper-threading.

• Hyper-threaded processors on a single physical chip share some resources, in particular cache.
• Benchmarks suggest that hyper-threading produces about a 20% speed-up in cases where dual physical processors would produce a factor of 2 speed-up

Recent advances allow full replication of processors within one chip; these are multi core processors.

• Multi-core machines are effectively full multi-processor machines (at least for most purposes).
• Dual core processors are now ubiquitous.
• The machines in the department research cluster have two dual core processors, or four effective processors.
• Our lab machines have a single quad core processor.
• Processors with 6 or 8 or even more cores are available.

Many processors support some form of vectorized operations, e.g. SSE2 (Single Instruction, Multiple Data, Extensions 2) on Intel and AMD processors.

### 2.3.4 Implications

Modern processors achieve high speed though a collection of clever tricks.

Most of the time these tricks work extremely well.

Every so often a small change in code may cause pipelining heuristics to fail, resulting in a pipeline stall.

These small changes can then cause large differences in performance.

The chances are that a “mall change” in code that causes a large change in performance was not in fact such a small change after all.

Processor speeds have not been increasing very much recently.

Many believe that speed improvements will need to come from increased use of explicit parallel programming.

More details are available in a talk at

http://www.infoq.com/presentations/click-crash-course-modern-hardware

## 2.4 Memory

### 2.4.1 Basics

Data and program code are stored in memory.

Memory consists of bits (binary integers)

On most computers

• Bits are collected into groups of eight, called byte.
• There is a natural word size of $$W$$ bits.
• The most common value of $$W$$ is still 32; 64 is becoming more common; 16 also occurs.
• Bytes are numbered consecutively, $$0, 1, 2, \dots, N = 2^W$$.
• An address for code or data is a number between $$0$$ and $$N$$ representing a location in memory, usually in bytes.
• $$2^{32} = 4,294,967,296 = 4\text{GB}$$.
• The maximum amount of memory a 32-bit process can address is 4 Gigabytes.
• Some 32-bit machines can use more than 4G of memory, but each process gets at most 4G.
• Most hard disks are much larger than 4G.

### 2.4.2 Memory Layout

A process can conceptually access up to $$2^W$$ bytes of address space.

The operating system usually reserves some of the address space for things it does on behalf of the process.

On 32-bit Linux the upper 1GB is reserved for the operating system kernel.

Only a portion of the usable address space has memory allocated to it.

Standard 32-bit Linux memory layout:

Standard heap can only grow to 1G.

malloc implementations can allocate more using memory mapping.

Obtaining large amounts of contiguous address space can be hard.

Memory allocation can slow down when memory mapping is needed.

Other operating systems differ in detail only.

64-bit machines are much less limited.

The design matrix for $$n$$ cases and $$p$$ variables stored in double precision needs $$8np$$ bytes of memory.

 $$p = 10$$ $$p = 100$$ $$p = 1000$$ n = 100 8,000 80,000 800,000 n = 1,000 80,000 800,000 8,000,000 n = 10,000 800,000 8,000,000 80,000,000 n = 100,000 8,000,000 80,000,000 800,000,000

### 2.4.3 Virtual and Physical Memory

To use address space, a process must ask the kernel to map physical space to the address space.

There is a hierarchy of physical memory:

Hardware/OS hides the distinction.

Caches are usually on or very near the processor chip and very fast.

RAM usually needs to be accessed via the bus

The hardware/OS try to keep recently accessed memory and locations nearby in cache.

A simple example:

msum <- function(x) {
nr <- nrow(x)
nc <- ncol(x)
s <- 0
for (i in 1 : nr)
for (j in 1 : nc)
s <- s + x[i, j]
s
}
m <- matrix(0, nrow = 5000000, 2)
system.time(msum(m))
##   user  system elapsed
##  1.712   0.000   1.712
fix(msum) ## reverse the order of the sums
system.time(msum(m))
##   user  system elapsed
##  0.836   0.000   0.835 
• Matrices are stored in column major order.
• This effect is more pronounced in low level code.
• Careful code tries to preserve locality of reference.

### 2.4.4 Registers

Registers are storage locations on the processor that can be accessed very fast.

Most basic processor operations operate on registers.

Most processors have separate sets of registers for integer and floating point data.

On some processors, including i386, the floating point registers have extended precision.

The i386 architecture has few registers, 8 floating point, 8 integer data, 8 address; some of these have dedicated purposes. Not sure about x86_64 (our lab computers).

RISC processors usually have 32 or more of each kind.

Optimizing compilers work hard to keep data in registers.

Small code changes can cause dramatic speed changes in optimized code because they make it easier or harder for the compiler to keep data in registers.

If enough registers are available, then some function arguments can be passed in registers.

Vector support facilities, like SSE2, provide additional registers that compilers may use to improve performance.

## 2.5 Processes and Shells

A shell is a command line interface to the computer’s operating system.

Common shells on Linux and MacOS are bash andtcsh.

You can now set your default Linix shell at https://hawkid.uiowa.edu/.

Shells are used to interact with the file system and to start processes that run programs.

You can set process limits and environment variables the shell.

Programs run from shells take command line arguments.

### 2.5.1 Some Basic bash/tcsh Commands

hostname prints the name of the computer the shell is running on.

pwd prints the current working directory.

ls lists files a directory

• ls lists files in the current directory.
• ls foo lists files in a sub-directory foo.

cd changes the working directory:

• cd or cd ~ moves to your home directory;
• cd foo moves to the sub-directory foo;
• cd .. moves up to the parent directory;

mkdir foo creates a new sub-directory foo in your current working directory;

rm, rmdir can be used to remove files and directories; BE VERY CAREFUL WITH THESE!!!

### 2.5.2 Standard Input, Standard Output, and Pipes

Programs can also be designed to read from standard input and write to standard output.

Shells can redirect standard input and standard output.

Shells can also connect processes into pipelines.

On multi-core systems pipelines can run in parallel.

A using the bash shell script P1.sh:

#!/bin/bash

while true; do echo \$1; done

and the rev program can be run as

bash P1.sh fox
bash P1.sh fox > /dev/null
bash P1.sh fox | rev
bash P1.sh fox | rev > /dev/null
bash P1.sh fox | rev | rev > /dev/null

### 2.5.3 The proc File System

The proc` file system allows you to view many aspects of a process.