Abstract
Performance characterization of applications' hardware behavior is essential for making the best use of available hardware resources. Modern architectures offer access to many hardware events that are capable of providing information to reveal architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of the resource bottlenecks in their applications. However, interpreting these events has been a significant challenge. We present an automated system that uses machine learning to identify an application's performance problems. Our system provides programmers with insights about the performance of their applications while shielding them from the onerous task of digesting hardware events. It uses a decision tree algorithm, random forests on our micro-benchmarks to fingerprint the performance problems. Our system divides a profiled application into functions and automatically classifies each function by the dominant hardware resource bottlenecks. Using the classifications from the hotspot functions, we were able to achieve an average speedup of 1.73 from three applications in the PARSEC benchmark suite. Our system provides programmers with a guideline of where, what, and how to fix the detected performance problems in applications, which would have otherwise required considerable architectural knowledge.
| Original language | English (US) |
|---|---|
| Title of host publication | SIGMETRICS/Performance 2012 - Proceedings of the 2012 ACM SIGMETRICS/Performance, Joint International Conference on Measurement and Modeling of Computer Systems |
| Pages | 283-294 |
| Number of pages | 12 |
| Edition | 1 SPEC. ISS. |
| DOIs | |
| State | Published - Aug 13 2012 |
| Event | 12th Joint International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS/Performance 2012 - London, United Kingdom Duration: Jun 11 2012 → Jun 15 2012 |
Publication series
| Name | Performance Evaluation Review |
|---|---|
| Number | 1 SPEC. ISS. |
| Volume | 40 |
| ISSN (Print) | 0163-5999 |
Other
| Other | 12th Joint International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS/Performance 2012 |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 6/11/12 → 6/15/12 |
Fingerprint
Keywords
- fingerprint
- hardware event
- machine learning
- micro-benchmark
- performance analysis
- resource bottleneck
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications
Cite this
ADP : Automated diagnosis of performance pathologies using hardware events. / Yoo, Wucherl; Larson, Kevin; Baugh, Lee; Kim, Sangkyum; Campbell, Roy H.
SIGMETRICS/Performance 2012 - Proceedings of the 2012 ACM SIGMETRICS/Performance, Joint International Conference on Measurement and Modeling of Computer Systems. 1 SPEC. ISS. ed. 2012. p. 283-294 (Performance Evaluation Review; Vol. 40, No. 1 SPEC. ISS.).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - ADP
T2 - Automated diagnosis of performance pathologies using hardware events
AU - Yoo, Wucherl
AU - Larson, Kevin
AU - Baugh, Lee
AU - Kim, Sangkyum
AU - Campbell, Roy H.
PY - 2012/8/13
Y1 - 2012/8/13
N2 - Performance characterization of applications' hardware behavior is essential for making the best use of available hardware resources. Modern architectures offer access to many hardware events that are capable of providing information to reveal architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of the resource bottlenecks in their applications. However, interpreting these events has been a significant challenge. We present an automated system that uses machine learning to identify an application's performance problems. Our system provides programmers with insights about the performance of their applications while shielding them from the onerous task of digesting hardware events. It uses a decision tree algorithm, random forests on our micro-benchmarks to fingerprint the performance problems. Our system divides a profiled application into functions and automatically classifies each function by the dominant hardware resource bottlenecks. Using the classifications from the hotspot functions, we were able to achieve an average speedup of 1.73 from three applications in the PARSEC benchmark suite. Our system provides programmers with a guideline of where, what, and how to fix the detected performance problems in applications, which would have otherwise required considerable architectural knowledge.
AB - Performance characterization of applications' hardware behavior is essential for making the best use of available hardware resources. Modern architectures offer access to many hardware events that are capable of providing information to reveal architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of the resource bottlenecks in their applications. However, interpreting these events has been a significant challenge. We present an automated system that uses machine learning to identify an application's performance problems. Our system provides programmers with insights about the performance of their applications while shielding them from the onerous task of digesting hardware events. It uses a decision tree algorithm, random forests on our micro-benchmarks to fingerprint the performance problems. Our system divides a profiled application into functions and automatically classifies each function by the dominant hardware resource bottlenecks. Using the classifications from the hotspot functions, we were able to achieve an average speedup of 1.73 from three applications in the PARSEC benchmark suite. Our system provides programmers with a guideline of where, what, and how to fix the detected performance problems in applications, which would have otherwise required considerable architectural knowledge.
KW - fingerprint
KW - hardware event
KW - machine learning
KW - micro-benchmark
KW - performance analysis
KW - resource bottleneck
UR - http://www.scopus.com/inward/record.url?scp=84864665235&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864665235&partnerID=8YFLogxK
U2 - 10.1145/2254756.2254791
DO - 10.1145/2254756.2254791
M3 - Conference contribution
AN - SCOPUS:84864665235
SN - 9781450310970
T3 - Performance Evaluation Review
SP - 283
EP - 294
BT - SIGMETRICS/Performance 2012 - Proceedings of the 2012 ACM SIGMETRICS/Performance, Joint International Conference on Measurement and Modeling of Computer Systems
ER -