This document discusses using static analysis to predict the worst-case energy consumption of software. It proposes applying techniques for worst-case execution time (WCET) analysis to energy by building an energy model and generating constraints to solve using an integer linear programming (ILP) solver. Preliminary results applying this approach to a software product line with 7 features are promising but constraint solving could be optimized by avoiding re-calculating shared constraints for each product. Future work includes improving the accuracy of the energy model and applying the technique to different contexts like mobile or embedded systems.

1. Marco Couto
marco.l.couto@inesctec.pt
STATIC ENERGY PREDICTION
IN SOFTWARE:
A WORST-CASE SCENARIO APPROACH

2. MOTIVATION
Marco Couto @ GSL - HASLab/INESC TEC 2

3. ENERGY VS. POWER
3
• Power (w) – rate (or effort) at which that work is done
• Energy (J) – amount of work done
• Power can change constantly while Energy is the accumulation
Energy = Power x Seconds
Power
Energy
100W360,000 J = x 3,600s

4. ENERGY EFFICIENT SOFTWARE
• Programmers problems:
• How to analyze
• How to interpret
• How to improve
• Researchers problem:
• How to provide information to developers
4
Mining questions about software energy consumption
- [MSR’14]
Integrated energy-directed test suite optimization
- [ISTA’14]
Seeds: A software engineer’s energy-optimization decision
support framework - [ICSE’14]

5. THE CHALLENGE
Marco Couto @ GSL - HASLab/INESC TEC 5

6. STATIC ENERGY ANALYSIS
WHY?
• Dynamic can be too costly
1. Build application + tests
2. Intrument app and/or tests
3. Run tests
4. Gather measurements and analyze them
5. Repeat N times
• ‘M’ apps -> Repeat all ‘M’ times
• Are all cases covered?
6Marco Couto @ GSL - HASLab/INESC TEC
‘M’ times

7. STATIC ENERGY ANALYSIS
HOW?
• How is it done for execution time?
• WCET – Worst Case Execution Time
• Can we do the same for energy?
7Marco Couto @ GSL - HASLab/INESC TEC

8. STATIC ENERGY EXECUTION TIME ANALYSIS
HOW?
8Marco Couto @ GSL - HASLab/INESC TEC
How exactly does WCET works?
</>
Source Code CFG
Machine Behavior
Dataflow
Bound Computation
Analysis Steps
1
53
2
Bounded CFG
T
Execution Time
Model
ILP Solver
x1 > C0
x1+x2 = C1
x1+x3 = C2
...
max(∑ xi.ti)
IPET Constraints

9. IPET – IMPLICIT PATH ENUMERATION TECHNIQUE
A.K.A CONSTRAINT GENERATOR
• Each node has:
• A count variable, ‘x’
• A cost variable, ‘t’
• Each edge has:
• A count variable, ‘x’
• 4 Simple but strong premises:
1. Start and end nodes accessed exactly 1 time
2. #x of node i = sum (#x of all pred. of i)
3. #x of node i = sum (#x of all succ. of i)
4. Loops must have na upper bound
9Marco Couto @ GSL - HASLab/INESC TEC

10. STATIC ENERGY ANALYSIS
HOW?
10Marco Couto @ GSL - HASLab/INESC TEC
How exactly does WCEC works?
</>
Source Code CFG
Machine Behavior
Dataflow
Bound Computation
Analysis Steps
1
53
2
Bounded CFG
E
Energy Model
ILP Solver
x1 > C0
x1+x2 = C1
x1+x3 = C2
...
max(∑ xi.ti)
IPET Constraints

11. ENERGY MODEL
HOW TO BUILD IT?
Marco Couto @ GSL - HASLab/INESC TEC 11

12. BUILDING THE ENERGY MODEL
• Two types of instructions:
• Path-Independent:
• x=1;
• i++;
• a=2*b;
• return 0;
• Path-Dependent:
• Allocations: malloc(sizeof(int)) ≠ malloc(sizeof(double))
• System calls: scanf(“%d”, &a) ≠ scanf(“%f”, &a)
• Other functions: strcmp(...), atoi(...), sizeof(...), ...
• Some instructions depend on others:
• ‘int x = z;’ depends on the declaration of z
• ‘printf(“%d”, a);’ assumes a has a previous assigned value
12Marco Couto @ GSL - HASLab/INESC TEC
Estimating Mobile Application Energy Consumption using
Program Analysis - [ICSE’13]

13. BUILDING THE ENERGY MODEL
• Path-Independent:
• Execute N times
• Repeat M times, measure each time and sum all
• At the end: energy = (sum/M/N) – dependencies
• Path-Dependent:
• Execute N times
• Repeat M times, measure each time
• At the end: energy = max(all measures) – dependencies
13Marco Couto @ GSL - HASLab/INESC TEC
Estimating Mobile Application Energy Consumption using
Program Analysis - [ICSE’13]

14. SOFTWARE PRODUCT LINES
THE CASE STUDY
Marco Couto @ GSL - HASLab/INESC TEC 14

15. SOFTWARE PRODUCT LINE
15Marco Couto @ GSL - HASLab/INESC TEC
“(…) a set of related software products (…)
generated from reusable assets.”
A B
CORE
D E
C
A B
CORE
D E
C
{B, C, D}
A B
CORE
D E
C
{A, E}
A B
CORE
D E
C
{A, B, C, D}
Feature
Product
configuration

16. STATIC ENERGY ANALYSIS
THIS TIME, FOR SPL!
16Marco Couto @ GSL - HASLab/INESC TEC
C
Source Code CFG
Machine Behavior
Dataflow
Bound Computation
Analysis Steps
1
53
2
Bounded CFG
E
Energy Model
ILP Solver
x1 > C0
x1+x2 = C1
x1+x3 = C2
...
max(∑ xi.ti)
IPET ConstraintsCFG
(feature-oriented)
1
53
2
Bounded CFG
(feature-oriented)(feature-oriented)
Intraprocedural Dataflow Analysis for Software Product Lines
- [AOSD '12]

17. RESULTS
Marco Couto @ GSL - HASLab/INESC TEC 17

18. PRELIMINARY RESULTS
• A SPL with 7 (exclusive) features
Results submited to SPLC’17 – Software Product Lines Conference, 21st edition:
“Products go green: Worst-Case Energy Consumption in Software Product Lines”
18Marco Couto @ GSL - HASLab/INESC TEC

19. WHAT NOW?
• This work was focused on accuracy
• Technique’s performance could be improved...
• Main reason: shared constraints are re-calculated for every product
19Marco Couto @ GSL - HASLab/INESC TEC
A B
CORE
D E
C
{B, C, D}
A B
CORE
D E
C
{A, B, C, D}
...
#Constraints for B
...
#Constraints for C
...
#Constraints for D
...
Constraint
Generator
...
#Constraints for B
...
#Constraints for C
...
#Constraints for D
...
Constraint
Generator
*
*
*Constraints solved more than once!

20. WHAT NOW?
• Accuracy can also be improved!
• Include a machine behavior analysis
• -> different energy consumptions per instruction
• Consider lower abstraction level
• For now, we analyzed C code
• Apply technique to different contexts
• Mobile?
• Embedded?
• ???
20Marco Couto @ GSL - HASLab/INESC TEC

21. Marco Couto
marco.l.couto@inesctec.pt
STATIC ENERGY PREDICTION
IN SOFTWARE:
A WORST-CASE SCENARIO APPROACH