Abstract
Code optimization is an intricate task that is getting more complex as computing systems evolve. Managing the program optimization process, including the implementation and evaluation of code variants, is tedious, inefficient, and errors are likely to be introduced in the process. Moreover, because each platform typically requires a different sequence of transformations to fully harness its computing power, the optimization process complexity grows as new platforms are adopted. To address these issues, systems and frameworks have been proposed to automate the code optimization process. They, however, have not been widely adopted and are primarily used by experts with deep knowledge about underlying architecture and compiler intricacies. This article describes the requirements that we believe necessary for making automatic performance tuning more broadly used, especially in complex, long-lived high-performance computing applications. Besides discussing limitations of current systems and strategies to overcome these, we describe the design of a system that is able to semi-automatically generate efficient platform-specific code. In the proposed system, the code optimization is programmer-guided, separately from application code, on an external file in what we call optimization programming. The language to program the optimization process is able to represent complex collections of transformations and, as a result, generate efficient platform-specific code. A database manages different optimized versions of code regions, providing a pragmatic approach to performance portability, and the framework itself has separate components, allowing the optimized code to be used on systems without installing all of the modules required for the code generation. We present experiments on two different platforms to illustrate the generation of efficient platform-specific code that performs comparable to hand-optimized, vendor-provided code.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1290-1306 |
| Number of pages | 17 |
| Journal | International Journal of High Performance Computing Applications |
| Volume | 33 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2019 |
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Keywords
- Code generation
- compilers
- domain-specific language
- high-performance computing
- optimization
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Hardware and Architecture
Cite this
Managing code transformations for better performance portability. / Teixeira, Thiago S.F.X.; Gropp, William D; Padua, David A.
In: International Journal of High Performance Computing Applications, Vol. 33, No. 6, 01.11.2019, p. 1290-1306.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Managing code transformations for better performance portability
AU - Teixeira, Thiago S.F.X.
AU - Gropp, William D
AU - Padua, David A
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Code optimization is an intricate task that is getting more complex as computing systems evolve. Managing the program optimization process, including the implementation and evaluation of code variants, is tedious, inefficient, and errors are likely to be introduced in the process. Moreover, because each platform typically requires a different sequence of transformations to fully harness its computing power, the optimization process complexity grows as new platforms are adopted. To address these issues, systems and frameworks have been proposed to automate the code optimization process. They, however, have not been widely adopted and are primarily used by experts with deep knowledge about underlying architecture and compiler intricacies. This article describes the requirements that we believe necessary for making automatic performance tuning more broadly used, especially in complex, long-lived high-performance computing applications. Besides discussing limitations of current systems and strategies to overcome these, we describe the design of a system that is able to semi-automatically generate efficient platform-specific code. In the proposed system, the code optimization is programmer-guided, separately from application code, on an external file in what we call optimization programming. The language to program the optimization process is able to represent complex collections of transformations and, as a result, generate efficient platform-specific code. A database manages different optimized versions of code regions, providing a pragmatic approach to performance portability, and the framework itself has separate components, allowing the optimized code to be used on systems without installing all of the modules required for the code generation. We present experiments on two different platforms to illustrate the generation of efficient platform-specific code that performs comparable to hand-optimized, vendor-provided code.
AB - Code optimization is an intricate task that is getting more complex as computing systems evolve. Managing the program optimization process, including the implementation and evaluation of code variants, is tedious, inefficient, and errors are likely to be introduced in the process. Moreover, because each platform typically requires a different sequence of transformations to fully harness its computing power, the optimization process complexity grows as new platforms are adopted. To address these issues, systems and frameworks have been proposed to automate the code optimization process. They, however, have not been widely adopted and are primarily used by experts with deep knowledge about underlying architecture and compiler intricacies. This article describes the requirements that we believe necessary for making automatic performance tuning more broadly used, especially in complex, long-lived high-performance computing applications. Besides discussing limitations of current systems and strategies to overcome these, we describe the design of a system that is able to semi-automatically generate efficient platform-specific code. In the proposed system, the code optimization is programmer-guided, separately from application code, on an external file in what we call optimization programming. The language to program the optimization process is able to represent complex collections of transformations and, as a result, generate efficient platform-specific code. A database manages different optimized versions of code regions, providing a pragmatic approach to performance portability, and the framework itself has separate components, allowing the optimized code to be used on systems without installing all of the modules required for the code generation. We present experiments on two different platforms to illustrate the generation of efficient platform-specific code that performs comparable to hand-optimized, vendor-provided code.
KW - Code generation
KW - compilers
KW - domain-specific language
KW - high-performance computing
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=85071011803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071011803&partnerID=8YFLogxK
U2 - 10.1177/1094342019865606
DO - 10.1177/1094342019865606
M3 - Article
AN - SCOPUS:85071011803
VL - 33
SP - 1290
EP - 1306
JO - International Journal of High Performance Computing Applications
JF - International Journal of High Performance Computing Applications
SN - 1094-3420
IS - 6
ER -