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# New Non-deterministic Approaches for Register Allocation

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## Abstract

In this paper two algorithms for register allocation are presented. The first algorithm is a simulated annealing algorithm. The core of the algorithm is the Metropolis procedure. The algorithm presented in the paper has a linear time asymptotic complexity. The second algorithm is a genetic algorithm. The algorithm has a linear time complexity.
New Non-deterministic Approaches for Register Allocation
K. M. Elleithy E. G. Abd-El-Fattah
Computer Engineering Department Computer Department
King Fahd University of Petroleum and Minerals Mosahmit Beherah Company
Dhahran 31261, Saudi Arabia Alexandria, Egypt
elleithy@dpc.kfupm.edu.sa
Abstract: In this paper two algorithms for register
allocation are presented. The first algorithm is a simulated
annealing algorithm. The core of the algorithm is the
Metropolis procedure. The algorithm presented in the paper
has a linear time asymptotic complexity. The second
algorithm is a genetic algorithm. The algorithm has a linear
time complexity.
Keywords
RISC, register allocation, simulated annealing, genetic
algorithms.
I. INTRODUCTION
Most RISC processors make a great effort to optimize
register usage to reduce the number of variables that must
be kept in memory. The choice of a suitable register
allocation technique is vital to the performance of a RISC
processor. Register allocation may be viewed as a graph
coloring problem, where coloring a graph is an assignment
of a color to each of its nodes such that two connected
nodes have different colors. Each node in the graph stands
for a computed quantity that resides in a machine register.
Two nodes are connected by an edge if the quantities are
simultaneously live at some point in the object program.
The graph coloring problem is an NP-C problem.
Exhaustive search approaches for coloring a graph imply
an exponential time complexity. Heuristics of reasonable
time complexity are required for large size problems. A
number of factors can be considered in designing a graph
coloring heuristics, e.g., a vertex of high degree is harder to
color than a vertex of low degree, vertices with the same
neighborhood should be colored alike, and coloring many
vertices with the same color is a good idea.
In this paper two non-deterministic algorithms are
presented and compared in terms of asymptotic time
complexity and quality of solution. These algorithms are
simulated annealing and genetic algorithms.
In the last decade Simulated Annealing (SA) has been
used extensively in solving various combinatorial
problems, e.g., traveling salesman problem [1,2] and
heuristic and belongs to the class of non-deterministic
algorithms. The heuristic was first introduced by
Kirkpatrich, Gelatt and Vecchi in 1983 [2]. The name of
Simulated Annealing is inspirited from the process of
carefully cooling molten metals in order to obtain a good
crystal structure.
A recently proposed paradigm for solving hard
optimization problems is Genetic Algorithms (GA). GA
has been successfully employed for solving many
combinatorial problems [4-7] in areas such as pattern
classification, machine learning, scheduling, and VLSI
placement and floor planning. Genetic algorithms are
search algorithms which emulate the natural process of
evolution as a means of processing toward the optimum. A
genetic algorithm starts with an initial set of random
configurations called a population. Each individual in the
population, termed chromosome, is a string of symbols
called genes. A solution for the optimization problem is
represented by a chromosome. During each iteration, two
individuals at a time, called parents, are selected from the
population based on a fitness value. A number of genetic
operators, as crossover or inversion, are applied on the
selected parents to generate new individual solutions called
offsprings. These genetic operators combine the features of
both parents.
II. SIMULATED ANNEALING ALGORITHM
In this section a simulated annealing algorithm for
register allocation is presented. The algorithm is based on
the Metropolis procedure. The procedure accepts a new
solution with less profit based on a probabilistic function.
The objective of the heuristic is to color a graph
representing the register allocation problem to maximize a
profit function. The profit function is defined using the
profit gained from coloring a subset of the nodes. The
algorithm presented in the paper has a linear time
asymptotic complexity.
The algorithm is shown in Fig. 1. The core of the
algorithm is the Metropolis procedure given in Fig. 2. It
uses the procedure NEWSOLUTION to generate a local
neighbor new_solution for any given solution. If the profit
of the new_solution is greater than the profit of the current
solution, then certainly the new_solution is acceptable. If
the new_solution has less profit, Metropolis will accept the
new_solution on a probabilistic basis. A random number is
generated in the range 0 to 1. If this random number is
smaller than
e
delta temperature/
, where delta is the
difference in profits, then the inferior solution is accepted.
The objective of the heuristic is to color a graph
representing the allocation problem to maximize a profit
function. The profit function is defined as follows:
Pr ( ) ( )ofit netsave node netsave node
i
i
all colored nodes
i
i
all uncolored nodes
1 1
{alpha is the cooling rate < 1};
{beta is a constant > 1};
begin
temperature = initial_temperature;
solution = initial_solution;
time = 0;
repeat
call Metropolis (solution, temperature, cooling_schedule);
time = time + cooling_schedule;
temperature = alpha * temperature;
cooling_schedule = beta * cooling_schedule;
until (time >= Maxtime);
end {of Simulated Annealing}.
Fig. 1 Procedure for Simulated Annealing Algorithm.
netsave node
i
( )
: A function representing the profit
gained from allocating a variable to a register.
The heuristic proceeds as follows:
(1) Find the set of largest profitable nodes. The number
of nodes in this set is equal to the number of available
colors.
(2) Color a new node from the uncolored set if it is
possible. If there is no more nodes to be colored or
time >= maxtime then stop.
(3) Calculate the change in profit after adding the
colored node from step 2.
4- (4) If the change is positive then add the node of step 2
to the colored set, otherwise the node is colored based
on a probabilistic comparison.
(5) Go to step 2.
Procedure Metropolis (solution, temperature,
cooling_schedule);
begin
repeat
get NEWSOLUTION;
delta = new_profit - old_profit
If ((delta > 0) or (random <
e
delta tempera ture/
)) then
solution = new_solution;
cooling_schedule = cooling_schedule - 1;
until (cooling_schedule = 0);
end {of Metropolis}.
Fig. 2 The Metropolis Procedure.
Experimental Results
In this section we apply the simulated annealing algorithm
for register allocation on a graph of size 128. The graph
used for this study is of random nature and random values
for the nodes' netsave. The effect of different parameters on
the proposed algorithm performance is evaluated. The
effect of the following parameters is considered : number
of colors, cooling-schedule, beta, temperature, and alpha.
Number of Colors
To evaluate the effect of number of colors on the profit
gained from coloring the graph and the number of trials to
changed from 10 to 20. The values for the parameters
cooling-schedule, beta, temperature, and alpha are 1.0, 1.0,
100, and 0.8 respectively. The following observations are
concluded :
(1) The best solution achieved is 2965. This value is
constant for colors greater than or equal to 14.
(2) While increasing the number of colors (i.e. greater
than 14), the profit remains constant but the number of
trials decreases. The effect of increasing the number of
colors is reflected on decreasing the number of trials.
(3) The graph can be colored using 14 colors only
while achieving a netsave of 2965.
Cooling Schedule (M)
To evaluate the effect of cooling schedule on the profit
gained from coloring the graph and the number of trials to
changed from 1 to 20. The values for the parameters beta,
temperature, and alpha are 1.0, 100, and 0.8 respectively.
The following is noticed :
1- The best solution is obtained at m = 1, and for m > 1 the
solution may be less or equal to the solution at m = 1.
2- 2- If the best solution remains constant while m is
increased, the number of trials remains constant as
well.
3- If the solution decreases as m increases, the number of
trials decreases as well.
4- The profit does not change with m if the number of
colors is greater than a certain threshold value (14 in
this example).
5- As m increases there is a better chance for a negative
solution to be accepted. This implies that the solution
may be worse or better than the previous solution. For
example if the number of colors is 10 and m is
increased from 4 to 5, the profit drops from 2565 to
2415 and the number of trials drops from 107 to 99,
and if the number of colors is 13 and m increases from
9 to 10, the profit increases from 2685 to 2865.
Beta
To evaluate the effect of beta on the profit gained from
solution, beta has been changed from 1 to 20.The values for
the parameters cooling-schedule, temperature, and alpha
are 1.0, 100, and 0.8 respectively. The following is noticed
:
1- The best solution is obtained at low values for beta.
This value is equal to 1 except for number of colors
equal to 12 and 13, the best solution is obtained at beta
equal to 3.
If the solution remains constant while beta is
increased, the number of trials may decrease slightly (
number of colors is 15 ), or remains constant ( number
of colors is 20 ).
3- If the solution decreases as beta increases, the number
of trials decreases as well.
4- The profit does not change with beta if the number of
colors is greater than a certain threshold value (14 in
this example).
5- As beta increases there is a better chance for a negative
solution to be accepted. This implies that the solution
may be worse or better than the previous solution. For
example if the number of colors is 11 and beta is
increased from 1 to 2, the profit drops from 2705 to
2555 and the number of trials drops from 109 to 101,
but if the number of colors is 12 and beta increases
from 2 to 3, the profit increases from 2695 to 2785.
Temperature
To evaluate the effect of temperature on the profit gained
from coloring the graph and the number of trials to get the
best solution, the temperature has been changed from 1 to
100. The values for the parameters cooling-schedule, beta,
and alpha are 1.0, 1.0, and 0.8 respectively The following
was noticed :
(1) The best solution is obtained at low values of
temperature.
2- (2) If the solution remains constant while temperature
is increased, the number of trials remains constant as
well.
3- (3) If the solution decreases as temperature increases,
the number of trials decreases as well.
(4) The profit does not change with temperature if the
number of colors is greater than or equal to a certain
threshold value (14 in this example).
(5) As temperature increases there is a better chance
for a negative solution to be accepted. This implies
that the solution may be worse or better than the
previous solution. If the number of colors is equal to
11 and temperature is increased from 10 to 20 the
profit increases from 2885 to 2895, but if the
temperature is increased from 20 to 30 the profit drops
from 2895 to 2835.
Alpha
To evaluate the effect of alpha on the profit gained from
solution, alpha has been changed from 0.1 to 0.99 The
values for the parameters cooling-schedule, beta, and
temperature are 1.0, 1.0, and 100 respectively.. The
following is noticed :
1- The best solution is obtained at low values for alpha.
This value is less than or equal to 0.6, except for
number of colors equal to 11 the best solution is
obtained at alpha in the range 0.2 to 0.6.
2- If the solution remains constant while alpha is
increased, the number of trials remains constant as
well.
3- If the solution decreases as alpha increases, the
number of trials decreases as well.
4- The profit does not change with alpha if the number of
colors is greater than or equal to 14, except for the
values of alpha in the range .95 to .99 where a
significant change is noticed.
5- As alpha decreases there is no chance that a negative
solution is accepted. That explains why the solution
remains constant for alpha less than or equal to 0.6.
6- 6- The profit achieved is very sensitive for values
of alpha greater than.
The simulated annealing algorithm presented in this
paper has a linear time asymptotic complexity. The
experimental results of the algorithm show optimal
solutions in many of the graphs used for testing. The results
show better performance compared with other
deterministic and non-deterministic approaches.
III. Genetic Algorithm
In this section we introduce a new genetic algorithm for
register allocation. A merge operator is used by the
selected parents to generate new individual solutions. The
number of steps required to examine all pairs in the
population matrix to generate candidate’s offspring is
n
2
(n is the population matrix size). Generating an offspring
from the parents needs m steps (m number of node. The
experimental results show optimal solutions in many of the
graphs used for testing. An outline of a genetic algorithm is
shown in Fig.. 3.The algorithm for register allocation is
shown in Fig. 4. The algorithm uses the following
parameters:
1- Initial population
An initial population consists of any random valid solutions
or it can be generated using a starting procedure. The
good solution that can be improved.
2- Hamming distance
Let A and B be any two individual strings of length N. The
hamming distance is defined as the total number of
positions where
A B
i i
.
Example
Let A = [0101100], B = [1011100]. A and B are different
in positions 1, 2, and 3, i.e., the hamming distance is 3.
Procedure Genetic
{
N
p
: population size}
{
N
g
: number of generations}
{
N
o
: number of offsprings}
{
P
i
: Inversion probability}
{Population : population matrix of size
N
p
}
begin
Generate an initial valid population;
for j=1 to
N
p
do evaluate fitness(population[j]);
for i=1 to
N
g
do
begin
for j=1 to
N
o
do
begin
choose parents with probability proportional to fitness value;
perform crossover to generate offsprings;
for k = 1 to
N
p
do
apply inversion(population[k]) with probability
P
i
;
Evaluate fitness(offspring[j]);
end;
population
select(population,offspring,
N
p
)
end;
Return highest scoring configuration in population;
end.
Fig. 3 Procedure for Genetic Algorithm.
Procedure Register-Allocation-by-Genetic Algorithm
{maxtime : is the total time allowed for the genetic process}
begin
get the population-matrix and the corresponding profit vector ;
for x = 1 to maxtime do
begin
Evaluate the hamming-distance between two individuals;
If hamming-distance > 1 then
Merge the two individuals to generate the new offspring;
If offspring-profit > max(parents' profit) then
Accept the offspring as a new individual in the
population-matrix by deleting any of the
parents and replacing the generated offspring;
end;
end.
Fig. 4 Procedure for Register Allocation by Genetic Algorithm.
3- Merge procedure
Let A and B be any two individual strings of length N. An
offspring C is generated by merging as follows:
c a
i i
if
a b
i i
c a
i i
if
a b a
i i i
, 0
and
netsave a
i
( ) 0
c b
i i
if
a b b
i i i
, 0
and
netsave b
i
( ) 0
c
i
0
otherwise
4- Fitness value
It is the profit function which is defined as follows :
)()(Pr
11
nodesuncoloredall
i
i
nodescoloredall
i
i
nodenetsavenodenetsaveofit
Analysis
The genetic algorithm presented in this paper may be
used to enhance previous results obtained using a starting
approach, e.g., simulated annealing. Let n be the population
matrix size and m be the vector length (no of nodes).
n
2
steps are required to examine all pairs in the population
matrix to generate candidate offspring. Generating an
offspring from the parents needs m steps. The total number
of steps required by the algorithm is
n m
2
, i.e., the genetic
algorithm has a linear time complexity in terms of number
of nodes.
IV. CONCLUSIONS
A reduced instruction set computer is a machine with a
small number of instructions optimized for a specific
application. A great effort is invested to optimize register
usage which influences the memory traffic. Two quantities
can share a register if their life times are mutually
exclusive. The problem of allocating values to registers can
be viewed as a graph coloring problem. Each node in the
graph represents a computed quantity that resides in a
machine register. Two nodes are connected if the residing
quantities do not have disjoint life times.
In this paper we introduce a combined two phase
approach for graph coloring; a Simulated Annealing
heuristic phase and a Genetic algorithm phase. The
performance of the Simulated Annealing is controlled by a
number of parameters, e.g., number of colors, cooling
scheduling, beta, temperature, and alpha. The effect of
different parameters on the performance has been
thoroughly investigated.
A Genetic algorithm is introduced for the second phase.
It is used to enhance results obtained from the SA phase. A
new efficient genetic operator is introduced to generate an
offspring from two parents. The GA proved to enhance the
SA results.
Other areas of research related to this combined
approach need more exploration. The approach can be
extended to include a global phase to measure the effect of
applying it across different subroutines. New genetic
operators, other than the merging operator introduced in
this paper, can be examined and compared with the
merging operator.
V. ACKNOWLEDGEMENTS
The first author would like to acknowledge the support of
King Fahd University of Petroleum and Minerals for
providing the necessary facilities to support this research.
VI. REFERENCES
[1] E. Bonomi and J. L. Lutton, "The N-City Traveling
Salesman Problem Statistical Mechanics and the
Metropolis Algorithm," SIAM Review, vol. 26, 1984,
pp. 551-568.
[2] S.Kirkpatrich, J.C.Gelatt and M.Vecchi, "Optimization
by Simulated Annealing," Science, vol. 220, May 1983,
pp. 498-516.
[3] R. E. Burkard and F. Rendl, "A Thermodynamically
motivated Simulation Procedure for Combinatorial
Optimization Problems," European Journal of
Operational Research, vol. 17, 1984, pp. 169-174.
[4] J. P. Cohoon, S. U. Hegde, W. N. Martin and D. S.
Richards, “Distributed Genetic Algorithms for
Floorplan Design Problem,” IEEE Trans. Comput.
Aided Des., vol. 4, April 1991, pp. 483-492.
[5] L. Davis, “Job Shop Scheduling with Genetic
Algorithms,” Proceedings of International Conference
on Genetic Algorithms and their Applications, 1987,
pp. 136-140.
[6] D. E. Goldberg, Genetic Algorithms in Search
Wesley, 1989.
[7] K. Shahookar and P. Majumder, “A Genetic Approach
to Standard Cell Placement using Meta-Genetic
Parameter Optimization,” IEEE Trans. Comput. Aided
Des., vol. 5, May 1990, pp. 500-512.
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