1. Performance
Lecture notes from MKP, H. H. Lee and S. Yalamanchili

2. Reading
Section 1.6
Practice Problems: Module 3 – 20, 21, 28

3. Goals
Provide a simple model of performance for processor and memory (later) architectures
Simple model for reasoning about the impact of compiler, architecture and technology properties
A model for understanding performance limits

4. Understanding Performance
Morgan Kaufmann Publishers
May 9, 2019
Understanding Performance
Algorithm
Determines number of operations executed
Programming language, compiler, architecture
Determine number of machine instructions executed per operation
Processor and memory system
Determine how fast instructions are executed
I/O system (including OS)
Determines how fast I/O operations are executed
Instruction Set Architecture
Chapter 1 — Computer Abstractions and Technology

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May 9, 2019
Metrics
Response time (latency)
How long it takes to do a task
Throughput
Total work done per unit time
e.g., tasks/transactions/… per hour
Trading throughput vs. latency
Energy/Power
Measure of work being performed
Increases with clock frequency/voltage
Determines temperature
Is affected by temperature
At the moment our focus is here
We will then move here (pipelining)
And finish here
Chapter 1 — Computer Abstractions and Technology

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Relative Performance
“X is n time faster than Y”
Example: time taken to run a program
10s on A, 15s on B
Execution TimeB / Execution TimeA = 15s / 10s = 1.5
So A is 1.5 times faster than B
Speedup
Chapter 1 — Computer Abstractions and Technology

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CPU Clocking
Operation of digital hardware governed by a constant-rate clock
Our cycle time
Clock period
Clock (cycles)
Data transfer and computation
Update state
Clock period: duration of a clock cycle
e.g., 250ps = 0.25ns = 250×10–12s
Clock frequency (rate): cycles per second
e.g., 4.0GHz = 4000MHz = 4.0×109Hz
Chapter 1 — Computer Abstractions and Technology

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CPU Time
AKA Core
Performance improved by
Reducing number of clock cycles
Increasing clock rate
Hardware designer must often trade off clock rate against cycle count
Chapter 1 — Computer Abstractions and Technology

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CPU Time Example
Computer A: 2GHz clock, 10s CPU time
Designing Computer B
Aim for 6s CPU time
Can do faster clock, but causes 1.2 × clock cycles
How fast must Computer B clock be?
Chapter 1 — Computer Abstractions and Technology

10. Instruction Count and CPI
Morgan Kaufmann Publishers
May 9, 2019
Instruction Count and CPI
Instruction Count for a program
Determined by program, ISA and compiler
Average cycles per instruction
Determined by CPU hardware
If different instructions have different CPI
Average CPI affected by instruction mix
Chapter 1 — Computer Abstractions and Technology

11. Cycles and Instructions
time
Multiplication takes more time than addition
Floating point operations take longer than integer ones
Accessing memory takes (in general) more time than accessing registers
Important point: changing the cycle time often changes the number of cycles required for various instructions (more later)

12. Program Execution time
Number of instruction classes
~= Instruction_count * CPIavg * clock_cycle_time
technology
algorithms/compiler
architecture
Relative frequency

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CPI Example
Computer A: Cycle Time = 250ps, CPI = 2.0
Computer B: Cycle Time = 500ps, CPI = 1.2
Same ISA
Which is faster, and by how much?
A is faster…
…by this much
Chapter 1 — Computer Abstractions and Technology

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May 9, 2019
CPI Example
Alternative compiled code sequences using instructions in classes A, B, C
Class A B C
CPI for class 1 2 3
IC in sequence 1
IC in sequence 2 4
Sequence 1: IC = 5
Clock Cycles = 2×1 + 1×2 + 2×3 = 10
Avg. CPI = 10/5 = 2.0
Sequence 2: IC = 6
Clock Cycles = 4×1 + 1×2 + 1×3 = 9
Avg. CPI = 9/6 = 1.5
Example:
Chapter 1 — Computer Abstractions and Technology

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Performance Summary
The BIG Picture
Performance depends on
Algorithm: affects IC, possibly CPI
Programming language: affects IC, CPI
Compiler: affects IC, CPI
Instruction set architecture: affects IC, CPI, Tc
Chapter 1 — Computer Abstractions and Technology

16. Benchmark Suites
Report performance metrics for execution on target platforms
Designed to assess how well the platforms function in specific domains
Examples
Media Bench - Multimedia
EEMBC – Embedded systems
Rodinia, Parboil: For GPU Systems
SPECWeb, SPECJbb – Enterprise systems
Many more……

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Pitfall: Amdahl’s Law
Improving an aspect of a computer and expecting a proportional improvement in overall performance
Example: multiply accounts for 80s/100s
How much improvement in multiply performance to get 5× overall?
Can’t be done!
Corollary: make the common case fast
Chapter 1 — Computer Abstractions and Technology

18. Amdahl’s Law f (1 - f) (1 - f) f / P
Speed-up = Exec_timeold / Exec_timenew =
Performance improvement from using faster mode is limited by the fraction the faster mode can be applied.
affected f
(1 - f)
Told
(1 - f)
Tnew
f / P

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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
Chapter 1 — Computer Abstractions and Technology

20. Study Guide
Practice problems provided on the class website

21. Glossary Amdahl’s Law Benchmarks CPI (cycles per instruction) CPU Time
Execution Time
Latency
Throughput