Abstract
Many computations exhibit a trade off between execution time and quality of service. A video encoder, for example, can often encode frames more quickly if it is given the freedom to produce slightly lower quality video. A developer attempting to optimize such computations must navigate a complex trade-off space to find optimizations that appropriately balance quality of service and performance. We present a new quality of service profiler that is designed to help developers identify promising optimization opportunities in such computations. In contrast to standard profilers, which simply identify time-consuming parts of the computation, a quality of service profiler is designed to identify subcomputations that can be replaced with new (and potentially less accurate) subcomputations that deliver significantly increased performance in return for acceptably small quality of service losses. Our quality of service profiler uses loop perforation (which transforms loops to perform fewer iterations than the original loop) to obtain implementations that occupy different points in the performance/quality of service trade-off space. The rationale is that optimizable computations often contain loops that perform extra iterations, and that removing iterations, then observing the resulting effect on the quality of service, is an effective way to identify such optimizable subcomputations. Our experimental results from applying our implemented quality of service profiler to a challenging set of benchmark applications show that it can enable developers to identify promising optimization opportunities and deliver successful optimizations that substantially increase the performance with only small quality of service losses.
| Original language | English (US) |
|---|---|
| Title of host publication | ICSE 2010 - Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering |
| Pages | 25-34 |
| Number of pages | 10 |
| DOIs | |
| State | Published - Jul 23 2010 |
| Event | 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010 - Cape Town, South Africa Duration: May 1 2010 → May 8 2010 |
Publication series
| Name | Proceedings - International Conference on Software Engineering |
|---|---|
| Volume | 1 |
| ISSN (Print) | 0270-5257 |
Other
| Other | 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010 |
|---|---|
| Country | South Africa |
| City | Cape Town |
| Period | 5/1/10 → 5/8/10 |
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Keywords
- loop perforation
- profiling
- quality of service
ASJC Scopus subject areas
- Software
Cite this
Quality of service profiling. / Misailovic, Sasa; Sidiroglou, Stelios; Hoffmann, Henry; Rinard, Martin.
ICSE 2010 - Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. 2010. p. 25-34 (Proceedings - International Conference on Software Engineering; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quality of service profiling
AU - Misailovic, Sasa
AU - Sidiroglou, Stelios
AU - Hoffmann, Henry
AU - Rinard, Martin
PY - 2010/7/23
Y1 - 2010/7/23
N2 - Many computations exhibit a trade off between execution time and quality of service. A video encoder, for example, can often encode frames more quickly if it is given the freedom to produce slightly lower quality video. A developer attempting to optimize such computations must navigate a complex trade-off space to find optimizations that appropriately balance quality of service and performance. We present a new quality of service profiler that is designed to help developers identify promising optimization opportunities in such computations. In contrast to standard profilers, which simply identify time-consuming parts of the computation, a quality of service profiler is designed to identify subcomputations that can be replaced with new (and potentially less accurate) subcomputations that deliver significantly increased performance in return for acceptably small quality of service losses. Our quality of service profiler uses loop perforation (which transforms loops to perform fewer iterations than the original loop) to obtain implementations that occupy different points in the performance/quality of service trade-off space. The rationale is that optimizable computations often contain loops that perform extra iterations, and that removing iterations, then observing the resulting effect on the quality of service, is an effective way to identify such optimizable subcomputations. Our experimental results from applying our implemented quality of service profiler to a challenging set of benchmark applications show that it can enable developers to identify promising optimization opportunities and deliver successful optimizations that substantially increase the performance with only small quality of service losses.
AB - Many computations exhibit a trade off between execution time and quality of service. A video encoder, for example, can often encode frames more quickly if it is given the freedom to produce slightly lower quality video. A developer attempting to optimize such computations must navigate a complex trade-off space to find optimizations that appropriately balance quality of service and performance. We present a new quality of service profiler that is designed to help developers identify promising optimization opportunities in such computations. In contrast to standard profilers, which simply identify time-consuming parts of the computation, a quality of service profiler is designed to identify subcomputations that can be replaced with new (and potentially less accurate) subcomputations that deliver significantly increased performance in return for acceptably small quality of service losses. Our quality of service profiler uses loop perforation (which transforms loops to perform fewer iterations than the original loop) to obtain implementations that occupy different points in the performance/quality of service trade-off space. The rationale is that optimizable computations often contain loops that perform extra iterations, and that removing iterations, then observing the resulting effect on the quality of service, is an effective way to identify such optimizable subcomputations. Our experimental results from applying our implemented quality of service profiler to a challenging set of benchmark applications show that it can enable developers to identify promising optimization opportunities and deliver successful optimizations that substantially increase the performance with only small quality of service losses.
KW - loop perforation
KW - profiling
KW - quality of service
UR - http://www.scopus.com/inward/record.url?scp=77954745730&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954745730&partnerID=8YFLogxK
U2 - 10.1145/1806799.1806808
DO - 10.1145/1806799.1806808
M3 - Conference contribution
AN - SCOPUS:77954745730
SN - 9781605587196
T3 - Proceedings - International Conference on Software Engineering
SP - 25
EP - 34
BT - ICSE 2010 - Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering
ER -