lecture 2 digital computer systems

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Digital Computer Systems

EE 344

Lecture 2: OverviewProf. Bruce Kim

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High Level Language

Assembly Language

Machine Language

Microarchitecture

Logic Level

Problem Algorithm Development Programmer

Compiler (translator)

Assembler (translator)

Control Unit (Interpreter)

Microsequencer (Interpreter)

Device Level Semiconductors Quantum

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Advantages of Higher Level Language

AMD Barcelona Multi-core

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Instruction Set Architecture (ISA)

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Technology Road Map

Changes in Technology

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Can We Solve Anything by a Computer?

• Undecidable – Cannot be solved by an algorithm – e.g. Halting problem (given a program and inputs

for it, decide whether it will run forever or will eventually halt. )

• Unsolvable– No finite algorithm– e.g. Goldbach’s conjecture (Every even number

greater than 2 can be written as the sum of two primes. )

• Intractable– Unreasonable amount of time and resources

Hierarchical View of a Computer System

• A computer system is complicated • In order to facilitate its study and

analysis, it is advisable to divide it into levels

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How do we Understand computers?

• Need to understand abstractions such:– Algorithms– Applications software– Systems software– Assembly Language– Machine Language (ISA)– Microarchitecture– Logic design– Device level– Semiconductors/Silicon used to build transistors– Properties of atoms, electrons, and quantum

dynamics• So much to learn!

A little bit of Vocabulary

Level N Level N-1

Level N Level N-1

Language A

Language A

Language B

Language B

Static Program Static Program

Individual Step of a Dynamic Program

Equivalent SequenceOf Steps

Almost always in software

In software SimulatorIn hardware Emulator

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Problem Definition Level

• Taking a complex real-life problem and formulating it so as to be solved by a computer (abstraction/modeling)

• Requires simplification (which details to remove?)

• Using mathematical model, graph theory, etc.

Algorithm Level

• Precise step-by-step procedure• Steps must be well defined, to be

executed by a machine (no ambiguity)• Algorithm development is a creative

process• Finite number of steps• Pseudocode or flowchart

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High-Level Language Level

• e.g. C/C++/C#, Java, Fortran, Lisp, etc.• Used by application programmers and

systems programmers• Can we build machines executing HLL

right away?• Compiler’s job is not only translating

Assembly Language Level

• More primitive instructions than HLL• English version of the machine language

+ some more• User mode and kernel mode• Can we go from this level to HLL?

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ISA (Instruction Set Architecture) level

• A very important abstraction– interface between hardware and low-level software– advantage: different implementations of the same

architecture– disadvantage: sometimes prevents using new innovations

True or False: Binary compatibility is extraordinarily important?

• Modern instruction set architectures:– IA-32, PowerPC, MIPS, SPARC, ARM, and others

Instructions

• Language of the Machine• Platform-specific• A limited set of machine language commands

"understood" by hardware (e.g. ADD, LOAD, STORE, RET)

• We’ll be working with the MIPS instruction set architecture– similar to other architectures developed since the 1980's– Almost 100 million MIPS processors manufactured in 2002– used by NEC, Nintendo, Cisco, Silicon Graphics, Sony, …

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CISC Vs RISC• Semantic Gap: the ratio of the number of

instruction in the high level language versus the number of instructions in the instruction set level.

• If the gap is small, then the instructions in the instruction set level are complicated (Complex Instruction Set Computing). This affects the performance.

• Hence (Reduced Instruction Set Computing) is used, where each instruction in the instruction set is simple.

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01998 2000 2001 20021999

Other

SPARC

Hitachi SH

PowerPC

Motorola 68K

MIPS

IA-32

ARM

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Microarchitecture Level

• Resources and techniques used to implement the ISA– Pentium IV implements the x86 ISA– Motorola G4 implements the Power PC ISA

• Register files, ALU, Fetch unit, etc.• Realize intended cost/performance

goals• Interpretation done by the control unit

Logic-Design Level

• Gates• Multiplexers, decoders, PLA, etc.• Synchronous (i.e. clocked) : the most

widely used• Asynchronous

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Device Level

• Transistors and wires• Implement the digital logic gates• Lower level:

– Solid state physics– Machine looks more analog than digital!

Intel Pentium 4 (3.06GHz)

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What have we learned till now?

• We have just scratched the surface of computing

• We have laid a solid foundation for computing and computers

• Can we now answer the question: what is a computer? [depends on the level ]

• Always remember: Computers are generally designed as a series of architectural abstractions, each one implementing the one immediately above it.

Performance Metrics

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Performance Metrics

Throughput versus Response Time • Response time (execution time) – the time between

the start and the completion of a task – Important to individual users

• Throughput (bandwidth) – the total amount of work done in a given time – Important to data center managers

• Will need different performance metrics as well as a different set of applications to benchmark embedded and desktop computers, which are more focused on response time, versus servers, which are more focused on throughput

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Defining (Speed) Performance

Relative Performance Example

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Measuring Execution Time • Elapsed time

– Total response time, including all aspects • Processing, I/O, OS overhead, idle time

– Determines system performance • CPU time

– Time spent processing a given job • Discounts I/O time, other jobs’ shares

– Comprises user CPU time and system CPU time – Different programs are affected differently by

CPU and system performance

Performance Factors

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Review: Machine Clock Rate

Improving Performance Example

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Clock Cycles per Instruction

Using the Performance Equation

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Effective (Average) CPI

THE Performance Equation

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Factors of CPU Performance

A Simple Example

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Workloads and Benchmarks

SPEC CINT2006 on Barcelona (CC = 0.4 x 109)

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Summary: Evaluating ISAs

Pitfall: Amdahl’s Law

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Pitfall: MIPS as a Performance Metric

Power Trends

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Uniprocessor Performance

Concluding Remarks • Cost/performance is improving

– Due to underlying technology development • Hierarchical layers of abstraction

– In both hardware and software • Instruction set architecture

– The hardware/software interface • Execution time: the best performance

measure • Power is a limiting factor

– Use parallelism to improve performance