Introduction to Physics Computing
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Introduction toPhysics Computing
MT 2013
Lecture 3
J Tseng
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Some reminders
While we encourage you to work on CO01/2 at home, don’t procrastinate 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
Computing: a physicist’s perspective Logic behind computing
Abstraction and control of complexity Physics impinging on computing
Limits to abstraction/control
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Electronic computers
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Electronic computers (2)
Time-ordered electrical signals in response to electrical inputs
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Electronic computers (3) How to get from basic electric signals to
3D gaming?
Freecodesource.com
Minecraftmuseum.net
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Digital electronics
Reduce range of voltages and currents to simple “on” and “off”, for example Off: < 0.3V On: > 2.0V
Less sensitive to physics 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
Outputs a pre-programmed series of signals Input: a clock or trigger Often used to control a series of actions
(triggered by outputs)
output01001011110010101110100100100100
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Finite state machine
input output
state
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State diagram
F
E
A
B
G
D
K
L
IHCJ
M
O
P
N
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Example: First Public Examinations
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Partial pass
First fail
Inputs:•CP1 ≥ 40•CP2 ≥ 40
Simple two-examPreliminary Exams
Pass both
Pass one
Pass none
Pass
Fail Pass
Fail
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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First Public Examination (2)
“Partial pass” actually encompassed two states Required passes
dictated by state B or C 6 states 4 possible input values
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A
B
D
E
F
11
01
00
1X
0X
11
00|01|10
C
10X1
X0
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Finite state machine
326Lookup
table
input output
state
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FSM to computer
2568Lookup
table
inputoutput
state
instructions(sequence)
memory
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Computer elements
processorinput output
program
memory
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Stored-program computer
processorinput output
memory
“Von Neumann” architecture: program stored in memory A program in memory is simply part of “state”
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Tinkertoys (1989) 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
Intel Pentium Submicron
geometry
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Computers for all seasons
Von Neumann computer is now so simple and cheap that we replicate it everywhere
processorinput output
memory
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Computers for all seasons (2)
Peripherals are themselves smaller von Neumann machines
processorinput output
memory
procinput output
mem
KeyboardMouseCD driveNetwork interface
Video controllerSound driverPrinterNetwork interface
Reflective memoryVirtual memory
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Computers for all seasons (3)
Peripheral design is now much simpler: stored-program computer + software 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 Not for generic computing – difficult to program, breaks von Neuman model
Modern machines often use computers to optimise their performance 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 Motorola
68332 CPU,
memory, I/O functions
Often used in peripherals, control apps
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Program execution
A program must ultimately turn into a predictable pattern of electrical (for now) signals Computers often use low-level “operation codes” Programmers typically write something they can read
Abstraction comes at a cost 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 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
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
Where we are
Where we used to be
Printers/graphics
web
Matlab
Matlab
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Interpreters
Alternative to compilation: program computer to interpret human-readable input 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
Modern Pentium: a legacy architecture emulated by a simpler CPU 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)
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
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) In addition, manages the simultaneous execution of
multiple programs: several users, peripherals, memory management, all sharing time on a single CPU Maintain fiction of a simple stored-program computer
Now done with modern operating systems: Unix/Linux/Mac OSX, VMS, Windows ≥ NT
hardwareOperating System
Web browseryour
programIn addition:• Hide complexities of
peripherals• Enable multiple users –
modern computers are mostly idle
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Operating systems (3)
Operating systems have enabled computers (and programmers) to take on increasingly sophisticated tasks: 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 Finally, it is possible to emulate one computer within
another computer Many web servers implemented this way to increase
utilisation, share resources, reduce power consumption
host hardwareHost Operating System
Emulatorother
softwareEmulated hardware
Guest OSUser Software
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
Matlab/SimuLink “powerlib” tool
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Power usage of computing
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. Average ~100% Google claims 6-13% with
custom hardware Slower data center power
growth than initially feared Physics behind
“the Cloud”
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Low-power computing
Mobile phones: increase battery life Computing with mobile phone
processors Claim greater computing per watt Typically less computing per second Do you notice?
x86 response: lower power consumption Remove functionality Reduce geometry Lower voltage thresholds
Not just CPU: How much power wasted signalling
to off-chip memory?
1993 PC (Octek Jaguar V)
Galaxy SII
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Low-power computing (2) 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)
Alternative “natural” fuel for web serving… 2.5V when fresh 76.8kHz
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A few more radical ideas
Quantum computing Small computers created, demonstrate parallel computations Somewhat like analog computation: possible in principle, but
physically difficult to make robust Reversible computing
Entropy increases when bits are erased (Shannon 1948 – information theory)
Landauer’s limit (1961): heat dissipation from erasure 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
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