This document discusses data locality, which refers to the property that references to the same or adjacent memory locations are reused within a short time period. There are two types of data locality: temporal locality, where the same data is used multiple times close together; and spatial locality, where different nearby data elements are used close together. Good data locality is important for performance as it reduces cache misses. Loop fusion and parallelizing loops can improve data locality by processing data in larger sequential units.

1. COMPILER DESIGN
PRESENTED BY
SYAMLAL.S.S
NO:528
II MCA
NMCC

2. Topic:
DATA LOCALITY

3. Data Locality
 It is the property that, references to the same memory
location or adjacent locations are reused within a short
period of time
 Good data locality is essential for good application
performance.
 Applications with poor data locality reduce the
effectiveness of the cache, causing long stall times
waiting for memory accesses.

4. Temporal Locality
 It occurs when the same data is used several times within
a short period.
There are two kinds of locality:
1.Temporal Locality
2.Spatial Locality

5. Spatial Locality
 It occurs when different data elements that are located
near to each other are used within a short period of
time.
 An important form of spatial locality occurs when all the
elements that appear on one cache line are used
together.
 The effect of this spatial locality is that cache misses are
minimized, with a resulting important speedup of the
program.

6. Example1:
Fig: Program to find the squares of the differences
(a) without loop fusion (b) with loop fusion
[Image from: The Dragon book 2nd
edition]

7.  First loop finds the differences, the second finds the
squares.
 The fused loop(fig.b) has better performances because
it has better data locality.

8. Example2:
Sequential and parallel code for zeroing an array

9. The two examples above illustrate several important characteristics
associated with numeric applications operating on arrays:
• Array code often has many parallelizable loops.
• When loops have parallelism, their iterations can be executed in arbitrary
order ; they can be reordered to improve data locality drastically.
• As we create large units of parallel computation that are
independent of each other, executing these serially tends to
produce good data locality.

10. References:
 Compilers Principles, Techniques, and Tools by A. Aho,‐
M. Lam (2nd edition), R. Sethi, and J.Ullman, Addison‐
Wesley.
 www.google.com

11. Thank You!