Read Chapter 1

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Introduction

What do the following mean:

• X is faster then Y
• X is n times faster than Y
• X is n% faster than Y

You need to answer these in terms of execution time for a given task.
We will never use the word slower in this context, only faster.

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Today's News: August 31, 2012

Several Scholarships are available for CS undergraduates.
Fill out a separate form for each scholarship.
Forms are available at the front of the class.
Write the name of the scholarship on the form.
Return the forms to the CS Department office.

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Section 1.1: Traditional Performance Growth

• Moore's Law (1965): Transistor count doubles every 2 years
  Actually closer to 18 months
• Traditionally, more transistors meant smaller and faster transistors, so performance also grew rapidly.
• Today's $500 laptop is faster, has more memory, and more disk space than the fastest multi-million dollar computer of 1975 (Cray 1).
• Single processor performance grew at a rate close to 50% per year from the 1980's until about 2002.
• Clock rates have stagnated at around 3 Ghz.
• Recent performance growth has come from increased number of processors, and has been at a slower rate.
• Here is Figure 1.1.
• Performance increases no longer just come from clock rate and instruction-level parallelism (ILP).
• Now data-level parallelism (DLP), thread-level parallelism (TLP) and request-level parallelism (RLP) are more important.

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Section 1.2: Classes of Computers

Types of computers:

• Personal Mobile Device (PMD): cell phones and tablets
  price
  energy efficiency
  responsiveness
  size
• Desktop
  price
  performance
• Server
  price
  throughput
  availability
  scalability
• Clusters
  price-performance
  throughput
  energy-proportionality
  
• Embedded: cameras, automobiles, microwave ovens, etc.
  price
  energy efficiency
  application-specific performance

Application Parallelism

• Data-Level Parallelism (DLP): many data items operated on at the same time
• Task-Level Parallelism (TLP): independent tasks of an application

Hardware Parallelism

• Instruction-Level Parallelism (ILP):
  exploits DLP with compiler help, e.g. pipelining, speculative execution
• Vector Architectures and Graphic Processor Units (GPU):
  exploit DLP by having a single instruction operate on a collection of data
• Thread-Level Parallelism (TLP):
  can exploit either DLP or TLP
• Request-Level Parallelism (RLP):
  exploits TLP as specified by the programmer or OS

Hardware Classifications

• Single instruction stream, single data stream (SISD)
  Traditional uniprocessor, can use ILP
• Single instruction stream, multiple data stream (SIMD)
  The same instruction executes on multiple processors using different data.
  e.g. vector processors and GPUs
• Multiple instruction stream, single data stream (MISD)
  not useful, included only for completeness
• Multiple instruction stream, multiple data stream (MIMD)
  independent processors that can act on independent data.
  e.g. multicore

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Section 1.3: Defining Computer Architecture

In CS 3843 we concentrated on the ISA.
Computer Architecture has 3 main components: Design the organization and hardware to meet goals
Example:

Instruction Set Architecture

This is what we used to define the architecture of the X86 and Y86 in CS 3843.

Class of ISA

Memory Addressing

Addressing Modes

Types and sizes of operands

Operations

Control flow instructions

ISA encoding

Authors' View of Computer Architecture

ISA, organization, hardware

Organization

Hardware

Some aspects of computer architectural design

• Application area
• Software compatibility: programming language or object code
• OS requirements: address space, memory management, protection
• Standards: e.g. IEEE floating point, SATA

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Section 1.4: Trends in Technology

Integrated Circuit Technology

• transistor density: 35% per year
• die size: 10 to 20% per year
• combines to 40% to 50% per year: Moore's law
• 6-core i7 Sandy Bridge: 2.27 billion transistors
• GPU about double this

Semiconductor Ram

• density increased 60% per year in the 1990's
• slowed to 25%-40% per year
• August 2012 1GB: $17

Semiconductor Flash

• Used in PMDs
• capacity increasing 50% to 60% per year
• 15 to 20 times cheaper than DRAM
• August 2012 512GB SSD: $400

Magnetic Disk

• density increase was 60% to 100% per year
• slowed to 40% per year
• 15 to 25 times cheaper than Flash
• 300 to 500 times cheaper than DRAM
• August 2012 2TB: $100

Network Technology

• ?

Trends in Bandwidth and Latency

• bandwidth or throughput is total work done in a unit of time
• latency or response time is the time between start and completion of an event
• bandwidth has increased faster than latency has decreased
• Here is Figure 1.9.

Wires

• transistor density improvements mainly from decrease in feature size usually given in nm (nanometers)
  1980: 4000, 1990: 1000, 2000: 180, 2010: 65, 2012: 32
• transistor count related to square of feature size
• signal delay of a wire increases with resistance and capacitance
• these get worse as wires shrink in size

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Section 1.5: Trends in Power and Energy

Issues

• maximum power
• sustained power: metric is thermal design power (TDP)
• used both for power supplies and cooling requirements

Energy vs. Power

• power is energy per unit time
• power is measured in watts
• energy is measured in joules
• 1 watt = 1 joule per second

Microprocessor Energy and Power

• Energy for a transition 0 → 1 or 1 → 0:
  Energy_dynamic ∝ capacitive load × Voltage²
• Power_dynamic ∝ capacitive load × Voltage² × frequency
• For a fixed task, slowing the clock rate reduces the power, but not the energy
• Lowering the voltage lowers both
• Voltages have been reduced from 5 volts to 1 volt over 20 years, but can't go much lower

Techniques for improving energy efficiency

• Do nothing well
• Dynamic Voltage-Frequency Scaling (DVFS)
• Design for the typical use
• Overclocking: run one core faster when other cores not needed (turbo mode)

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Section 1.6: Trends in Cost

Cost of an Integrated Circuit

• die and wafer: See Figure 1.15.
  How big is 300mm?
• See Figure 1.13 and Figure 1.14
• cost of IC =
  cost of die + cost of testing die + cost of packaging

  ---

  final test yield
• cost of die =
  cost of wafer

  ---

  dies per wafer × die yield
• dies per wafer =
  π × (wafer diameter/2)²

  ---

  die area
   - 
  π × wafer diameter

  ---

  √2 × die area
• die yield depends on complexity and die area
• large fixed costs are important when quantities are not large

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Section 1.7: Dependability

• MTTF: mean time to failure
• MTTR: mean time to repair
• MTBF: mean time between failures = MTTF + MTTR
• availability =
  MTTF

  ---

  MTTF + MTTR
• failure rate = 1/MTTF
• failure rates add (if small), MTTF's do not

Example 1:
Example 2:

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Section 1.8: Measuring, Reporting, and Summarizing Performance

Comparing two computers

• X is n times faster than Y
• Execution time_Y

  ---

  Execution time_X
   =  n
• Performance_X

  ---

  Performance_Y
   =  n
• Example: the throughput of X is 1.3 times higher than that of Y:
  
  Throughput_X

  ---

  Throughput_Y

  = 1.3 so

  Execution time_Y

  ---

  Execution time_X

  = 1.3
  This is the same as X is 30% faster than Y.
• X is n % faster than Y means
  Execution time_Y

  ---

  Execution time_X
  = 1 +
  n

  ---

  100
  
  We can also say that the performance of X is n % greater than the performance of Y.
• These are not usually used to compare machines because they depend on the job being run.

Examples (Also see ClassQue Questions)

Time

• wall-clock time, response time, elapsed time: includes everything, e.g. disk and memory accesses, OS overhead
• CPU time: does not include time waiting for I/O or time running other programs

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Today's News: September 7, 2012

Recitations start next week.
Be sure to bring a calculator.

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Benchmarks

Types of benchmarks:

• kernels: small pieces of an application
• toy programs
• synthetic benchmarks: like real programs

Tricks people play

• benchmark-specific hardware
• benchmark-specific compilers
• benchmark-specific compiler flags
• source code modification

SPEC: Standard Performance Evaluation Corporation

• set of standardized benchmarks
• have evolved over time
• separate sets for integer, floating point, and server
• performance results should be reproducible

Summarizing Performance Results

• arithmetic means give more weight to programs with longer running times
• use geometric means to combine results
• SPECRatio: ratio of geometric mean to that of a standard reference computer
• SPECRatio_A

  ---

  SPECRatio_B
   = 
  Execution time_B

  ---

  Execution time_A
   = 
  Performance_A

  ---

  Performance_B
     independent of reference computer
• Example: Geometric mean of 4 numbers: 10, 20, 15, 17:
  (10 × 20 × 15 × 17)^1/4 = 15.03

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Section 1.9: Quantitative Principles of Computer Design

Take advantage of parallelism

• Multiple processors
• Pipelining
• Caches

Principle of locality

• programs tend to reuse data and instructions that have been used recently
• a typical program will spend 90% of its execution time on 10% of the code
• temporal locality: predict which instructions and data will be used in the near future
• spacial locality: items with addresses near each other tend to be accessed close together in time

Focus on the common case

• adds are more common than multiplies
• for database, disk access may be most important

Amdahl's law

A given enhancement will only improve a part of a program.
Example: on multicore machine, some of the code will only user one core.

Speedup
Speedup =

Performance for entire task using the enhancement when possible

---

Performance for the entire task without using the enhancement

or

Speedup =

Execution time for entire task without using the enhancement

---

Execution time for the entire task using the enhancement when possible

Amdahl's law depends on: Fraction_enhanced and Speedup_enhanced
Note: Fraction_enhanced is relative to the original design.
Fraction_unenhanced = 1 - Fraction_enhanced
Execution time_old = Execution time_{old-unenhanced} + Execution time_old-enhanced
Execution time_old = Execution time_old × Fraction_unenhanced + Execution time_old × Fraction_enhanced
Execution time_new = Execution time_old × Fraction_unenhanced +

Execution time_old

---

Speedup_enhanced

× Fraction_enhanced
Execution time_new = Execution time_old × (Fraction_unenhanced +

Fraction_enhanced

---

Speedup_enhanced

)
Speedup_overall =

Execution time_old

---

Execution time_new

Amdahl's Law:

Speedup_overall =

 
1

---

Fraction_unenhanced +

Fraction_enhanced

---

Speedup_enhanced

or

Speedup_overall =

 
1

---

1 - Fraction_enhanced +

Fraction_enhanced

---

Speedup_enhanced

You must be able to use this formula, but you also need to understand when the formula cannot be used directly.
You can only use the formula directly when you know both the enhanced fraction and the enhanced speedup.

Examples

1. A new design makes the floating point processor of the CPU 80% faster than before. What is the overall speedup for a task in which floating point operations took up 30% of the CPU time with the old design?
   Solution: The enhanced fraction is 30% = .3. The unenhanced fraction is .7. The enhanced speedup is 1.8. The overall speeup is
   

   1

   ---

   .7 + .3/1.8

     =   1.1538.
2. A new design makes the floating point processor of the CPU by 80% faster than before. What is the overall speedup for a task in which floating point operations took up 10% of the CPU time with the new design?
   Solution: We cannot use the formula directly since the enhanced fraction in the formula is based on the old design.
   Method 1: first calculate Fraction_enhanced (relative to the old design).

   Suppose the total execution time on the new systems is E.
   The unenhanced time on the new system is .9E and the enhanced time is .1E.
   The unenhanced time on the old system is still .9E and the enhanced time is .1E × 1.8 = .18E.
   The fraction enhanced (relative to the old system) is

   .18E

   ---

   (.9E + .18E)

   = .1667.
   The speedup is

   1

   ---

   1-.1667 + .1667/1.8

   = 1.08.

   Method 2: Use the definition of Speedup directly

   Relative to the new design, the unenhanced fraction is .1 and the unenhanced fraction is .9.
   Execution time_new = .9 × Execution time_new + .1 × Execution time_new
   Execution time_old = .9 × Execution time_new + .1 × Execution time_new × 1.8
   Speedup_overall =

   Execution time_old

   ---

   Execution time_new

   =

   .9 × Execution time_new + .1 × Execution time_new × 1.8

   ---

   Execution time_new

   = .9 + .18 = 1.08.
   

Processor Performance Equations

• CPU time = CPU clock cycles for a program × Clock cycle time
• CPU time =
  CPU clock cycles for a program

  ---

  Clock rate
• CPI: average clock cycles per instruction
• CPI =
  CPU clock cycles for a program

  ---

  Instruction count
• CPU time = Instruction count × Cycles per instruction × Clock cycle time
• CPU time =
  Instructions

  ---

  Program
  ×
  Clock cycles

  ---

  Instruction
  ×
  Seconds

  ---

  Clock cycle

Dependencies

• Clock cycle time: hardware technology and organization
• CPI: organization and ISA
• Instruction count: ISA and compiler technology

More on CPI

• CPU clock cycles =
  _n
  Σ
  ⁱ⁼¹
   IC_i × CPU_i
• CPI =
  _n
  Σ
  ⁱ⁼¹
   
  IC_i

  ---

  Instruction count
  × CPU_i

Examples

1. In a particular task, 23% of the instructions are floating point instruction which each take 5 cycles to execute. All other instructions take 1 cycle to execute. What is the average CPI for this task?
   Solution: CPI = .23 × 5 + .77 × 1 = 1.92
2. A new design can reduce the number of cycles for a floating point operation to 4, without changing the clock speed. What is the new CPI and what is the expected speedup?
   Solution: CPI = .23 × 4 + .77 × 1 = 1.69
   Speedup = 1.92/1.69 = 1.136
3. What is wrong with the following method of solving the above problem using the Amdahl's Law equation?
   Fraction_enhanced = .23, Fraction_unenhanced = .77, and Speedup_enhanced = 5/4 = 1.25. Therefore
   Speedup_overall =
   1

   ---

   .77 + .23/1.25
   = 1.0482
   Answer: .23 is the fraction of instructions that are enhanced, not the time spent executing the enhanceable part.
   The correct enhanced fraction is (.23 × 5)/(.23 × 5 + .77) = 1.15/1.92 = .5990. This gives
   Speedup_overall =
   1

   ---

   .4010 + .5990/1.25
   = 1.136
   

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Today's News: September 10, 2012

Recitations start this week.
Be sure to bring a calculator.

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Section 1.10: Putting It All Together

• This section compares three server systems using the SPECPower benchmark.
• These are configured with a small amount of memory and small disks.
• Comparing just the cost of the hardware and software, the systems are ordered by performance (ops/$): System 1, System 2, System 3.
• Comparing power requirements, the systems are ordered by performance (ops/joule): System 3, System 2, System 1.

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Section 1.11: Fallacies and Pitfalls

Fallacy: a commonly held misbelief
Pitfall: an easily made mistake

Fallacies

• Multiprocessors are a silver bullet.
• Hardware enhancements that increase performance improve energy efficiency or are at least neutral.
• Benchmarks remain valid indefinitely.
• The rated MTTF of disks is almost 140 years, so disks rarely fail.
• Peak performance tracks observed performance.

Pitfalls

• Amdahl's law
• Single point of failure
• Fault detection can lower availability

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