Transcript Introduction to Physics Computing

Introduction to
Physics Computing
MT 2013
Lecture 3
J Tseng
Some reminders
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While we encourage you to work on CO01/2
at home, don’t procrastinate
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Learning by doing means putting in the time
To keep on top of things, you should be done with
CO01 after your 2nd or 3rd time in lab (week 4 for
some, week 7 for others)
Otherwise you’ll be working without help over the
Christmas vacation
Use the practical lab sessions wisely
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Outline
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Computing: a physicist’s perspective
Logic behind computing
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Abstraction and control of complexity
Physics impinging on computing
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Limits to abstraction/control
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Electronic computers
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Electronic computers (2)
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Time-ordered electrical signals in response to
electrical inputs
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Electronic computers (3)
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How to get from basic electric signals to
3D gaming?
Minecraftmuseum.net
Freecodesource.com
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Digital electronics
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Reduce range of voltages and currents to
simple “on” and “off”, for example
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Less sensitive to physics
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Off: < 0.3V
On: > 2.0V
Near instantaneous signal propagation over
relevant distances (this is an issue in high-speed
networks)
Resistance drops over wires
Digital “abstraction”: simplifies robust design
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Sequencer
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Outputs a pre-programmed series of signals
Input: a clock or trigger
Often used to control a series of actions
(triggered by outputs)
output
01001011
11001010
11101001
00100100
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Finite state machine
input
output
state
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State diagram
A
L
F
O
K
E
N
B
M
G
D
C
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H
I
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Example: First Public Examinations
Simple two-exam
Preliminary Exams
Inputs:
• CP1 ≥ 40
• CP2 ≥ 40
switch state
case before_exam
…
case partial_pass
if pass
state = passed
else
state = failed
end
case first_fail
…
otherwise
disp(‘Illegal state’)
end
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Pass both
Pass one
Pass
Partial
pass
Fail
Pass
Pass none
First
fail
Fail
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First Public Examination (2)
11
A
E
01
1X
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“Partial pass” actually
encompassed two
states
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0X
B
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Required passes
dictated by state B or C
6 states
4 possible input values
X1
11
C
X0
F
00
D
00|01|10
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Finite state machine
input
326
Lookup
table
output
state
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FSM to computer
input
instructions
(sequence)
output
2568
Lookup
table
state
memory
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Computer elements
program
input
processor
output
memory
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Stored-program computer
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“Von Neumann” architecture: program stored
in memory
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A program in memory is simply part of “state”
input
processor
output
memory
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Tinkertoys (1989)
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Actually more a sequencer
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Modern computers
Central
processing
unit (CPU)
memory
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CPU
Contains logic and instruction
decoding
The first μCPU: Intel 4004
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CPU
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Intel Pentium
Submicron
geometry
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Computers for all seasons
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Von Neumann computer is now so simple
and cheap that we replicate it everywhere
input
processor
output
memory
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Computers for all seasons (2)
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input
Peripherals are themselves smaller von
Neumann machines
proc
input
output
processor
output
mem
Keyboard
Mouse
CD drive
Network interface
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Video controller
Sound driver
Printer
Network interface
memory
Reflective memory
Virtual memory
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Computers for all seasons (3)
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Peripheral design is now much simpler: stored-program computer +
software
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Fewer specially designed components
More modular design
Graphical Processing Units (GPU’s) are now so common and capable that some
scientists are using them instead of the CPU
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Modern machines often use computers to optimise their performance
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Not for generic computing – difficult to program, breaks von Neuman model
Use more environmental inputs from sensors
Easier (and perhaps faster) than traditional mechanical or chemical means
Auto shops used to use wrenches a lot; now they use computer
terminals
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Single-chip
computer
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Motorola
68332
CPU,
memory, I/O
functions
Often used in
peripherals,
control apps
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Program execution
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A program must ultimately turn into a predictable pattern of
electrical (for now) signals
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Abstraction comes at a cost
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Greater memory consumption
Slower execution
However: write robust, correct code more quickly
The translation of high-level to low-level code is itself a
mechanical process
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Computers often use low-level “operation codes”
Programmers typically write something they can read
computers help program themselves (“compilation”)
Computer scientists have noticed they use certain
structures over and over again: these patterns are often
seen in new high-level programming languages
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Programming languages
Where we used to be
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Different traditions
and models, often
depending on
original application
“Every programmer
knows there is one
true programming
language. A new
one every week.”
B Hayes, “The
Semicolon Wars”,
American Scientist,
July/Aug 2006
Matlab
Printers/graphics
Where we are
web
Matlab
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Interpreters
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Alternative to compilation: program computer
to interpret human-readable input
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Usual Matlab operation
More common: compile to an intermediate
form (“bytecodes”) which are then fed to a
simple interpreter (p-System, Smalltalk and
Java virtual machines)
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Interpretation and CPU’s
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Modern Pentium: a legacy
architecture emulated by a
simpler CPU
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Internal microcode
Much like microcontroller
Implementation more
complex, optimised for
performance
Preserves von Neumann
model on the outside
Laid out by compiler even so
(also a mechanical process)
Internal processing so fast
that it mostly waits for
memory
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Interpretation and CPU’s (2)
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Transmeta CPU’s
 Simple, elegant, low-power processor
 Interprets Pentium operation codes
 Compiles frequently-used code sections into native code for
faster execution
 Intel responded by cutting power consumption, optimising
execution and I/O pipelines
Another example: HotSpot JVM progressively optimises code
 Now a common strategy for modern languages: Java, Python,
javascript…
 Runtime compilation routinely matches and outperforms static
compilation
 “traffic analysis” also mechanical
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Operating systems
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Recall that a program in memory is simply
part of “state” until it is executed (i.e., treated
like a program)
Loading and executing a program is itself a
mechanical process suitable for a program –
the operating system
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Operating systems (2)
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In addition, manages the simultaneous execution of
multiple programs: several users, peripherals, memory
management, all sharing time on a single CPU
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Maintain fiction of a simple stored-program computer
Now done with modern operating systems:
Unix/Linux/Mac OSX, VMS, Windows ≥ NT
your
Web browser program
Operating System
hardware
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In addition:
• Hide complexities of
peripherals
• Enable multiple users –
modern computers are mostly
idle
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Operating systems (3)
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Operating systems have enabled computers
(and programmers) to take on increasingly
sophisticated tasks:
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Worry only about the simple computer abstraction
OS handles the simultaneous processes, devices
Computers handle time-ordered logic
Electronics translate logic into signals
These signals control keyboards, display screens,
airplanes, space stations, toaster ovens
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Virtual computers
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Finally, it is possible to emulate one computer within
another computer
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Many web servers implemented this way to increase
utilisation, share resources, reduce power consumption
User Software
Guest OS
Emulated hardware
other
software
Emulator
Host Operating System
host hardware
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Virtualization: VMWare, Xen
• manages use of hardware by virtual
computers
Emulation: QEMU, JPC
• implements hardware entire in software
• user software can’t tell the difference
• open question: can emulating x86 on
ARM save power?
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Simulating circuits
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Matlab/SimuLink “powerlib” tool
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Power usage of computing
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US EPA, 2006: data center
power consumption in US is
1.5% total power
“power usage
effectiveness”: power in
data center not associated
with computing – power
distribution, cooling, etc.
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Average ~100%
Google claims 6-13% with
custom hardware
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Slower data center power
growth than initially feared
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Physics behind
“the Cloud”
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Low-power computing
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Mobile phones: increase battery life
Computing with mobile phone
processors
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Claim greater computing per watt
Typically less computing per second
Do you notice?
x86 response: lower power
consumption
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1993 PC (Octek Jaguar V)
Remove functionality
Reduce geometry
Lower voltage thresholds
Not just CPU:
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How much power wasted signalling
to off-chip memory?
Galaxy SII
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Low-power computing (2)
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Simplify to lower complexity, power consumption, size
Enables mobile computing, embedded devices (e.g., your
refrigerator’s web page)
webACE: 2
tiny web pages
in on-chip
memory
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Smallest server farm
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Low-power computing (3)
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Alternative “natural” fuel for web serving…
2.5V when fresh
76.8kHz
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A few more radical ideas
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Quantum computing
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Small computers created, demonstrate parallel computations
Somewhat like analog computation: possible in principle, but
physically difficult to make robust
Reversible computing
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Entropy increases when bits are erased (Shannon 1948 –
information theory)
Landauer’s limit (1961): heat dissipation from erasure
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Current computers operate at 1000× this limit
Recently demonstrated in Berut, et al., Nature 483, 187 (2012)
Theoretically well developed – technology less so
Physics impinging on computers again, especially at limits
of performance
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Conclusion
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Many of you should have run your first programs by now
 If having trouble, ask for help at your next opportunity
Electronic computers have progressed immensely in ~60 years
 On one hand, exponential increase in complexity
 Complexity managed by abstraction ad nauseum
 In many cases, computers simplify design
 Proliferation raises other issues, e.g., power consumption
 Future balance between physics and abstraction
Computers are working their way into every walk of life; your
career will likely depend on them
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