CS计算机代考程序代写 mips Java computer architecture Hive ada cache assembly python Fortran algorithm Computer Abstractions and Technology

Computer Abstractions and Technology
CS 154: Computer Architecture Lecture #2
Winter 2020
Ziad Matni, Ph.D.
Dept. of Computer Science, UCSB

A Word About Registration for CS154
• This class is FULL
• If you want to add this class AND you are on the
waitlist, see me after lecture
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Lecture Outline
• Tech Details • Trends
• Historical context
• The manufacturing process of Ics
• Important Performance Measures • CPU time
• Other factors (power, multiprocessors) • Pitfalls
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Parts of the CPU
• The Datapath, which includes the Arithmetic Logic Unit (ALU) and
other items that perform operations on data
• Cache Memory, which is small & fast RAM memory
for immediate access to data. Resides inside the CPU.
(other types of memory are outside the CPU, like DRAM, etc…)
• The Control Unit (CU)
which sequences how Datapath + Memory interact
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Image from wikimedia.org

Inside the Apple A5 Processor
Manufactured in 2011 – 2013 32 nm technology
37.8 mm2 die size
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The CPU’s Fetch-Execute Cycle
• Fetch the next instruction • Decode the instruction
• Get data if needed
• Execute the instruction
• Maybe access mem again and/or write back to reg.
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This is what happens inside a computer interacting with a program at the “lowest” level

Pipelining (Parallelism) in CPUs
• Pipelining is a fundamental design in CPUs
• Allows multiple instructions to go on at once • a.k.a instruction-level parallelism

Digital Design of a CPU (Showing Pipelining)
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Computer Languages and the F-E Cycle
• Instructions get executed in the CPU in machine language (i.e. all in “1”s and “0”s)
• Even small instructions, like
“add 2 to 3 then multiply by 4”,
need multiple cycles of the CPU to get fully executed • But THAT’S OK! Because, typically,
CPUs can run many millions of instructions per second 1/9/20 Matni, CS154, Wi20 9

Computer Languages and the F-E Cycle
• But THAT’S OK! Because, typically,
CPUs can run many millions of instructions per second
• In low-level languages (like assembly or machine lang.) you need to spell those parts of the cycles one at a time
• In high-level languages (like C, Python, Java, etc…) you don’t • 1 HLL statement, like “x = c*(a + b)” is enough to get the job done
• This would translate into multiple statements in LLLs
• What translates HLL to LLL?
• What translates LLL to ML?
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Machine vs. Assembly Language
• Machine language (ML) is the actual 1s and 0s Example:
• Assembly language is one step above ML
• Instructions are given mnemonic codes but still displayed
one step at a time
• Advantage? Better human readability
lw $t0, 4($sp) # fetch N from someplace in memory
add $t0, $t0, $t0 # add N to itself
# and store the result in N
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Computer Memory
Usually organized in two parts:
• Address: Where can I find my data? • Data (payload): What is my data?
• A bit (b) is ____________________
• A byte (B) is ___________________
• MIPS CPUs operate instructions that are ____ bits long
• MIPS CPUs organize memory in units called ______ that are ___ bits long • MIPS memory is addressable in ________ endian
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Reminder of some MIPS instructions
• add vs addi vs addu vs addui • mult and mflo
• sll
• srl vs sra
• lavslivslwvssw
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Eight Great Ideas in Computer Architecture
• Design for Moore’s Law
• Use abstraction to simplify design • Make the common case fast
• Performance via parallelism
• Performance via pipelining
• Performance via prediction
• Hierarchy of memories
• Dependability via redundancy
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Electronic Circuitry Tech Trends
• Electronics technology continues to evolve
• Increased memory capacity (at same price/size) • Increased CPU performance
• Reduced costs overall
Relative Performance
Vacuum tube
Integrated circuit (IC)
Very large scale IC (VLSI)
2.4 million
Application Specific IC or ASIC (ultra-large scale)
250 million
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DRAM capacity goes up and the prices come down…
• DRAM = Dynamic RAM
• Very common tech used for
computer memory
DRAM capacity
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Source: K.Asanovic, UCB
Major Technology Generations

Single-Thread Processor Performance
[ Hennessy & Patterson, 2017 ]

Computer Architecture: A Little History
Throughout the course we’ll use a historical narrative to help understand why certain ideas arose
Why worry about old ideas?
• Helps to illustrate the design process, and explains why certain decisions were taken
• Because future technologies might be as constrained as older ones • Those who ignore history are doomed to repeat it
• Every mistake made in mainframe design was also made in minicomputers, then microcomputers, where next?
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Digital Computers
• An improvement over Analog Computers…
• Represent problem variables as numbers encoded using discrete steps
• Discrete steps provide noise immunity
• Enables accurate and deterministic calculations
• Same inputs give same outputs exactly
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Computing Devices for General Purposes
• Charles Babbage (UK)
• Analytical Engine could calculate polynomial
functions and differentials
• Inspired by older generation of calculating machines made by Blaise Pascal (1623-1662, France)
• Calculated results, but also
stored intermediate findings
(i.e. precursor to computer memory)
• “Father of Computer Engineering” • Ada Byron Lovelace (UK)
• Worked with Babbage and foresaw computers doing much more than calculating numbers
• Loops and Conditional Branching
• “Mother of Computer Programming” 1/9/20
C. Babbage (1791

A. Byron Lovelace (1815

Part of Babbage’s Analytical Engine
Images from Wikimedia.org

The Modern Digital Computer
• Calculating machines kept being produced in the early 20th century (IBM was established in the US in 1911)
• Instructions were very simple, which made hardware implementation easier, but this hindered the creation of complex programs.
Alan Turing (UK)
• Theorized the possibility of computing machines capable of performing any conceivable mathematical computation as long as this was representable as an algorithm
• Called “Turing Machines” (1936) – ideas live on today…
• Lead the effort to create a machine to successfully decipher the
German “Enigma Code” during World War II
A. Turing (1912

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Zuse Z3 (1941)
• Built by Konrad Zuse in wartime Germany using 2000 relays
• Could do floating-point arithmetic with hardware
• 22-bit word length ; clock frequency of about 4–5 Hz!!
• 64 words of memory!!!
• Two-stage pipeline
1) fetch & execute, 2) writeback
• No conditional branch
• Programmed via paper tape
Replica of the Zuse Z3 in the Deutsches Museum, Munich
[Venusianer, Creative Commons BY-SA 3.0 ]

ENIAC (1946)
• First electronic general-purpose computer
• Constructed during WWII to calculate firing tables for US Army • Trajectories (for bombs) computed in 30 seconds instead of 40 hours • Was very fast for its time – started to replace human “computers”
• Used vacuum tubes (transistors hadn’t been invented yet)
• Weighed 30 tons, occupied 1800 sq ft
• It used 160 kW of power (about 3000 light bulbs worth)
• It cost $6.3 million in today’s money to build.
• Programmed by plugboard and switches, time consuming!
• As a result of large number of tubes, it was often broken
(5 days was longest time between failures!)
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Changing the program could take days!
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[Public Domain, US Army Photo]

Comparing today’s cell phones (with dual CPUs), with ENIAC, we see they
cost 17,000X less
are 40,000,000X smaller use 400,000X less power are 120,000X lighter AND…
are 1,300X more powerful.
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EDVAC (1951)
• ENIAC team started discussing stored-program concept to speed up programming and simplify machine design
• Based on ideas by John von Nuemann & Herman Goldstine • Still the basis for our general CPU architecture today
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Commercial computers:
BINAC (1949) and UNIVAC (1951) at EMC
• Eckert and Mauchly left academia and formed the Eckert- Mauchly Computer Corporation (EMC)
• World’s first commercial computer was BINAC which didn’t work…
• Second commercial computer was UNIVAC
• Famously used to predict presidential election in 1952 • Eventually 46 units sold at >$1M each
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IBM 650 (1953)
• The first mass-produced computer
• Low-end system aimed at businesses rather than scientific enterprises
• Almost 2,000 produced
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[Cushing Memorial Library and Archives, Texas A&M, Creative Commons Attribution 2.0 Generic ]

Improvements in C.A.
• IBM 650’s instruction set architecture (ISA)
• 44 instructions in base instruction set, expandable to 97 instructions
• Hiding instruction set completely from programmer using the concept of high-level languages like Fortran (1956), ALGOL (1958) and COBOL (1959)
• Allowed the use of stack architecture, nested loops, recursive calls, interrupt handling, etc…
Adm. Grace Hopper (1906 – 1992), inventor of several High-level language concepts
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Manufacturing ICs
Yield: the proportion of working dies per wafer; often expressed as a number between 0 and 1
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Example: Intel Core i7 Wafer
• 300mm (diameter) wafer
• 280 chips
• Each chip is 20.7 mm x 10.5 mm
• 32nm CMOS technology
(the size of the smallest piece of logic
and the type of Silicon semiconductor used)
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Costs of Manufacturing ICs
• Wafer cost and area are fixed
• Defect rate determined by manufacturing process
• Die area determined by architecture and circuit design
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YOUR TO-DOs for the Week
• Do your reading for next class (see syllabus)
• Work on Assignment #1 for lab (lab01)
• Meet up in the lab this Friday
• Do the lab assignment
• You have to submit it as a PDF using Gradescope • Due on Wednesday, 1/15, by 11:59:59 PM
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