This presentation discusses peephole optimization. Peephole optimization is performed on small segments of generated code to replace sets of instructions with shorter or faster equivalents. It aims to improve performance, reduce code size, and reduce memory footprint. The working flow of peephole optimization involves scanning code for patterns that match predefined replacement rules. These rules include constant folding, strength reduction, removing null sequences, and combining operations. Peephole optimization functions by replacing slow instructions with faster ones, removing redundant code and stack instructions, and optimizing jumps.

1. Welcome to my
Presentation
Topic : Peephole Optimization
Samrin Ahmed
ID: 011142021

2. CONTENTS
Functioning of Peephole Optimization & Example
Replacement Rules
Working Flow of Peephole Optimization
Introduction to Peephole Optimization
What is an Optimization
2
Conclusion
References

3. What is Optimization ??
 Process of transforming a piece of code to make it more efficient (either in
terms of time or space) without changing its output or side-effects.
 Tries to minimize or maximize some attributes of an executable computer
program.

4. Introduction to Peephole
Optimization ??
 Optimization performed over a very small set of
instructions in a segment of generated code.
 Works by recognizing sets of instructions that can be
replaced by shorter or faster sets of instructions.
 Goals:
 improve performance
 reduce code size
 reduce memory footprint

5. Why it’s needed ??
After compilation, the code still has some flaws
 Scheduling & allocation really are NP-Complete
 Optimizer may not implement every needed transformation
Curing the problem
 More work on scheduling and allocation
 Implement more optimizations
— or —
 Optimize after compilation
 Peephole optimization
 Link-time optimization

6. Working Flow of Peephole
Optimization

7. Replacement Rules
Common techniques applied in peephole optimization:-
 Constant folding
– Evaluate constant sub-expressions in advance.
 Strength reduction
– Replace slow operations with faster equivalents.
 Null sequences
– Delete useless operations.
 Combine operations
– Replace several operations with one equivalent.
 Algebraic laws
– Use algebraic laws to simplify or reorder instructions.
 Special case instructions
– Use instructions designed for special operand cases.
 Address mode operations
– Use address modes to simplify code.

8. Functioning of Peephole
Optimization
 Replacing slow instructions with faster ones
 Removing redundant code
 Removing redundant stack instructions

9. Functioning (Contd…)
 Replacing slow instructions with faster ones
The following Java byteCode
...
load 1
load 1
mul
...
Can be replaced by
...
load 1
dup
mul
...

10. Functioning (Contd…)
 Removing redundant code
 Another example is to eliminate redundant load stores.
a = b + c;
d = a + e;
is straightforwardly implemented as
MOV b, R0 # Copy b to the register
ADD c, R0 # Add c to the register, the register is now b+c
MOV R0, a # Copy the register to a
MOV a, R0 # Copy a to the register
ADD e, R0 # Add e to the register, the register is now a+e [(b+c)+e]
MOV R0, d # Copy the register to d

11. Functioning (Contd…)
 Removing redundant code
but can be optimized to
MOV b, R0 # Copy b to the register
ADD c, R0 # Add c to the register, which is now b+c (a)
MOV R0, a # Copy the register to a
ADD e, R0 # Add e to the register, which is now b+c+e [(a)+e]
MOV R0, d # Copy the register to d

12. Functioning (Contd…)
 Removing redundant stack instructions
PUSH AF
PUSH BC
PUSH DE
PUSH HL
CALL _ADDR1
POP HL
POP DE
POP BC
POP AF
PUSH AF
PUSH BC
PUSH DE
PUSH HL
CALL _ADDR2
POP HL
POP DE
POP BC
POP AF

PUSH AF
PUSH BC
PUSH DE
PUSH HL
CALL _ADDR1
CALL _ADDR2
POP HL
POP DE
POP BC
POP AF

13. Functioning (Contd…)
Source Code:
If ( a<b ) {
a = a + b;
}
 Peephole optimization of jumps
 Eliminate jumps to jumps
 Eliminate jumps after conditional branches
“Adjacent” instructions = “Adjacent in control flow”
IL Code:
PUSH a
PUSH b
CMP a,b
JUMP L1
EXIT
L1:
ADD a,b


14. Other Considerations
Control-flow operations
 Can clear simplifier’s window at branch or label
 More aggressive approach: combine across branches
 Same considerations arise with predication
Physical versus logical windows
 Can run optimizer over a logical window
 Logical windows (within block) improve effectiveness

15. Conclusion
So…
 Peephole optimization remains viable
 Post allocation improvements
 Cleans up rough edges
 Peephole technology works for selection
 Description driven matchers
 Used in several important systems
All of this will work equally well in binary-to-binary translation

16. References
 https://en.wikipedia.org/wiki/Peephole_optimization
 http://www.iosrjournals.org/iosr-jce/papers/Vol9-Issue4/N0948086.pdf?id=255
 https://class.coursera.org/compilers/lecture/76
 https://www.slideshare.net/AnulChaudhary/peephole-optimization-techniques-in-
compiler-design

17. That’s all
Any Question