AKT211 – CAO 02 – Computer Evolution and Performance
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Transcript of AKT211 – CAO 02 – Computer Evolution and Performance
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AKT211 – CAO
02 – Computer Evolution and Performance
GhifarParahyangan Catholic University
Sept 5, 2011
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Outline ENIAC von Neumann machine Moore’s Law Amdahl’s Law
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ENIAC• Electronic Numerical Integrator And
Computer• Eckert and Mauchly• University of Pennsylvania• Trajectory tables for weapons • Started 1943• Finished 1946
– Too late for war effort• Used until 1955• The world’s 1st general-purpose electronic
digital computer
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ENIAC (2)• Decimal (not binary)• 20 accumulators of 10 digits• Programmed manually by
switches• 18,000 vacuum tubes• 30 tons• 15,000 square feet• 140 kW power consumption• 5,000 additions per second
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Von Neumann/Turing• stored-program concept• Main memory storing programs and data• ALU operating on binary data• Control unit interpreting instructions from
memory and executing• Input and output equipment operated by
control unit• Princeton Institute for Advanced Studies
– IAS• Completed 1952
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Structure of von Neumann/IAS machine
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Struture of IAS - detail• 1000 x 40
bit words– Binary
number– 2 x 20 bit
instructions
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IAS Components• Memory Buffer Register (MBR)
– Contains a word to be stored in memory, or is used to receive a word from memory
• Memory Address Register (MAR)– Specifies the address in memory of the word to be
written from or read into the MBR• Instruction Register (IR)
– Contains the 8-bit opcode instruction being executed
• Instruction Buffer Register (IBR)– Employed to hold temporarily the right-hand
instruction from a word in memory
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IAS Components (2)• Program Counter (PC)
– Contains the address of the next instruction-pair to be fetched from memory
• Accumulator (AC) dan Multiplier Quotient (MQ)– Employed to hold temporarily operands and
results of ALU operations. For example, the result of multiplying two 40-bit numbers is an 80-bit numbers: the most significant 40 bits are stored in the AC and the least significant in the MQ
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Commercial Computers• 1947 - Eckert-Mauchly Computer
Corporation• UNIVAC I (Universal Automatic
Computer)• US Bureau of Census 1950
calculations• Became part of Sperry-Rand
Corporation• Late 1950s - UNIVAC II
– Faster– More memory
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IBM• Punched-card processing
equipment• 1953 - the 701
– IBM’s first stored program computer– Scientific calculations
• 1955 - the 702– Business applications
• Lead to 700/7000 series
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Transistors• Replaced vacuum tubes• Smaller• Cheaper• Less heat dissipation• Solid State device• Made from Silicon (Sand)• Invented 1947 at Bell Labs• William Shockley et al.
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Transistor Based Computers• Second generation machines• NCR & RCA produced small
transistor machines• IBM 7000• DEC - 1957
– Produced PDP-1
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Generations of Computer• Vacuum tube - 1946-1957• Transistor - 1958-1964• Small scale integration - 1965 on
– Up to 100 devices on a chip• Medium scale integration - to 1971
– 100-3,000 devices on a chip• Large scale integration - 1971-1977
– 3,000 - 100,000 devices on a chip• Very large scale integration - 1978 -1991
– 100,000 - 100,000,000 devices on a chip• Ultra large scale integration – 1991 -
– Over 100,000,000 devices on a chip
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Microelectronics• Literally - “small electronics”• A computer is made up of gates,
memory cells and interconnections
• These can be manufactured on a semiconductor
• e.g. silicon wafer
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Intel• 1971 - 4004
– First microprocessor– All CPU components on a single chip– 4 bit
• Followed in 1972 by 8008– 8 bit– Both designed for specific applications
• 1974 - 8080– Intel’s first general purpose
microprocessor
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Moore’s Law• Increased density of components on chip• Gordon Moore – co-founder of Intel• “number of transistors on a chip will double
every year”• Since 1970’s development has slowed a little
– Number of transistors doubles every 18 months• Cost of a chip has remained almost unchanged• Higher packing density means shorter electrical
paths, giving higher performance• Smaller size gives increased flexibility• Reduced power and cooling requirements• Fewer interconnections increases reliability
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Growth in CPU Transistor Count
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Speeding it up• Pipelining• On board cache• On board L1 & L2 cache• Branch prediction• Data flow analysis• Speculative execution
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Logic and Memory Performance Gap
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Solution• Increase number of bits retrieved at
one time– Make DRAM “wider” rather than “deeper”
• Change DRAM interface– Cache
• Reduce frequency of memory access– More complex cache and cache on chip
• Increase interconnection bandwidth– High speed buses– Hierarchy of buses
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I/O Devices• Peripherals with intensive I/O demands• Large data throughput demands• Processors can handle this• Problem moving data • Solutions:
– Caching– Buffering– Higher-speed interconnection buses– More elaborate bus structures– Multiple-processor configurations
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Typical I/O Device Data Rates
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Key is Balance !• Processor components• Main memory• I/O devices• Interconnection structures
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New Approach – Multiple cores• Multiple processors on single chip
– Large shared cache• Within a processor, increase in performance
proportional to square root of increase in complexity
• If software can use multiple processors, doubling number of processors almost doubles performance
• So, use two simpler processors on the chip rather than one more complex processor
• With two processors, larger caches are justified– Power consumption of memory logic less than
processing logic
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Amdahl’s Law• Gene Amdahl [AMDA67]• Potential speed up of program using
multiple processors• Concluded that:
– Code needs to be parallelizable– Speed up is bound, giving diminishing returns for
more processors• Task dependent
– Servers gain by maintaining multiple connections on multiple processors
– Databases can be split into parallel tasks
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Amdahl’s Law Formula• For program running on single processor :
– Fraction f of code infinitely parallelizable with no scheduling overhead
– Fraction (1-f) of code inherently serial– T is total execution time for program on single
processor– N is number of processors that fully exploit
parallel portions of code
• Conclusions– f small, parallel processors has little effect– N ∞, speedup bound by 1/(1 – f)
• Diminishing returns for using more processors
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The Case of Amdahl’s Law• An algorithm has 40% of total codes which
can be executed in parallel. It has already implemented in a server with 1 CPU. Someday the server was upgraded so that it has 8 CPU. Using the old server, the algorithm can be executed in 0.3 seconds. How much is the execution time of the algorithm if executed using the new server ? What about the speedup?
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Any Question ?
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Assignment 1 - Find the meaningWrite your explanation about the processor performance speedup techniques below : • Pipelining• On board cache• On board L1 & L2 cache• Branch prediction• Data flow analysis• Speculative execution
Save your writing in a text file with this naming format: SpeedupXXYYY.txt
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Assignment 1 – Amdahl’s Law Program
• Write a program in Java that simulates the computation of Amdahl’s Law with the following specifications :– Input :
1. Fraction of code infinitely parallelizable (f)2. Number of processors (N)3. Single processor execution time (t1)
– Output :1. Multiple processor execution time (tn)2. Speed up
– Files/Classes Name: AmdahlCalcXXYYY.java & AmdahlCalcXXYYY.class
1t
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Assignment 1Collect all files in one .zip file with this naming format: A1AOKxxyyy.zip, and submit it to eLearning FTIS before Friday, Sept. 9th 2011, 17:00 P.M.
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THANK YOU