Finding effective compilation sequences

Almagor, Lior; Cooper, Keith D.; Grosul, Alexander; Harvey, Timothy J.; Reeves, Steven W.; Subramanian, Devika; Torczon, Linda; Waterman, Todd

doi:10.1145/997163.997196

Cited by 126 publications

(91 citation statements)

References 23 publications

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“…These adaptive compilers discover a good compilation sequence tailored to the input program, the target machine, and a user-chosen objective function. We have shown, as have others, that program-specific sequences can produce better results than any single universal sequence [1,7,10, 21,23] Our adaptive compiler looks for compilation sequences in a large and complex search space. Its typical compilation sequence includes 10 passes (with possible repeats) chosen from the 16 available-there are 16 10 or 1,099,511,627,776 such sequences.…”

supporting

confidence: 60%

“…We have shown that program-specific sequences produce consistently better results than any universal sequence [1]. This paper reports on our preliminary analysis of several 10-of-5 subspaces.…”

mentioning

confidence: 94%

“…Our adaptive compiler uses a program-specific compilation sequence to optimize each program. Our work has shown that program-specific compilation sequences can produce better code than can a "universal" sequence [1,8]. Today, our prototype compiler must find these program-specific sequences with search techniques.…”

mentioning

confidence: 97%

“…Thus, these sequences tend to include optimizations that are broadly applicable rather than high-payoff techniques with a more narrow focus. 1 As Robison observes: "Compile-time program optimizations are similar to poetry: more are written than are actually published in commercial compilers" [19].…”

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confidence: 99%

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Exploring the structure of the space of compilation sequences using randomized search algorithms

et al. 2006

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Modern optimizing compilers apply a fixed sequence of optimizations, which we call a compilation sequence, to each program that they compile. These compilers let the user modify their behavior in a small number of specified ways, using command-line flags (e.g., -O1, -O2, . . . ). For five years, we have been working with compilers that automatically select an appropriate compilation sequence for each input program. These adaptive compilers discover a good compilation sequence tailored to the input program, the target machine, and a user-chosen objective function. We have shown, as have others, that program-specific sequences can produce better results than any single universal sequence [1,7,10, 21,23] Our adaptive compiler looks for compilation sequences in a large and complex search space. Its typical compilation sequence includes 10 passes (with possible repeats) chosen from the 16 available-there are 16 10 or 1,099,511,627,776 such sequences. To learn about the properties of such spaces, we have studied subspaces that consist of 10 passes drawn from a set of 5 (5 10 or 9,765,625 sequences). These 10-of-5 subspaces are small enough that we can analyze them thoroughly but large enough to reflect important properties of the full spaces.This paper reports, in detail, on our analysis of several of these subspaces and on the consequences of those observed properties for the design of search algorithms.Compilers operate by applying a fixed sequence of optimizations, called a compilation sequence, to all programs. The compiler writer must select ten to twenty optimizations from the hundreds that have been proposed in the literature; then the compiler writer must select †

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confidence: 60%

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confidence: 94%

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confidence: 97%

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confidence: 99%

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Exploring the structure of the space of compilation sequences using randomized search algorithms

et al. 2006

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“…the effect of an optimization is determined by the presence of other optimizations and cannot be measured in isolation. Understanding the nature of these optimizations and their interactions is critical for problems such as phase ordering [2,10] and important for the design of efficient compilers in general.…”

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confidence: 99%

Microarchitecture Sensitive Empirical Models for Compiler Optimizations

Vaswani

Thazhuthaveetil

Srikant

et al. 2007

International Symposium on Code Generation and Optimization (CGO'07)

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This paper proposes the use of empirical modeling techniques for building microarchitecture sensitive models for compiler optimizations. The models we build relate program performance to settings of compiler optimization flags, associated heuristics and key microarchitectural parameters. Unlike traditional analytical modeling methods, this relationship is learned entirely from data obtained by measuring performance at a small number of carefully selected compiler/microarchitecture configurations. We evaluate three different learning techniques in this context viz. linear regression, adaptive regression splines and radial basis function networks. We use the generated models to a) predict program performance at arbitrary compiler/microarchitecture configurations, b) quantify the significance of complex interactions between optimizations and the microarchitecture, and c) efficiently search for 'optimal' settings of optimization flags and heuristics for any given microarchitectural configuration.Our evaluation using benchmarks from the SPEC CPU2000 suits suggests that accurate models (< 5% average error in prediction) can be generated using a reasonable number of simulations. We also find that using compiler settings prescribed by a model-based search can improve program performance by as much as 19% (with an average of 9.5%) over highly optimized binaries.

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Finding near‐perfect parameters for hardware and code optimizations with automatic multi‐objective design space explorations

Jahr

Calborean

Vintan

et al. 2012

Concurrency and Computation

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SummaryIn the design process of computer systems or processor architectures, typically many different parameters are exposed to configure, tune, and optimize every component of a system. For evaluations and before production, it is desirable to know the best setting for all parameters. Processing speed is no longer the only objective that needs to be optimized; power consumption, area, and so on have become very important. Thus, the best configurations have to be found in respect to multiple objectives. In this article, we use a multi‐objective design space exploration tool called Framework for Automatic Design Space Exploration (FADSE) to automatically find near‐optimal configurations in the vast design space of a processor architecture together with a tool for code optimizations and hence evaluate both automatically. As example, we use the Grid ALU Processor (GAP) and its postlink optimizer called GAPtimize, which can apply feedback‐directed and platform‐specific code optimizations. Our results show that FADSE is able to cope with both design spaces. Less than 25% of the maximal reasonable hardware effort for the scalable elements of the GAP is enough to achieve the processor's performance maximum. With a performance reduction tolerance of 10%, the necessary hardware complexity can be further reduced by about two‐thirds. The found high‐quality configurations are analyzed, exhibiting strong relationships between the parameters of the GAP, the distribution of complexity, and the total performance. These performance numbers can be improved by applying code optimizations concurrently to optimizing the hardware parameters. FADSE can find near‐optimal configurations by effectively combining and selecting parameters for hardware and code optimizations in a short time. The maximum observed speedup is 15%. With the use of code optimizations, the maximum possible reduction of the hardware resources, while sustaining the same performance level, is 50%.Copyright © 2012 John Wiley & Sons, Ltd.

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Finding effective compilation sequences

Cited by 126 publications

References 23 publications

Exploring the structure of the space of compilation sequences using randomized search algorithms

Exploring the structure of the space of compilation sequences using randomized search algorithms

Microarchitecture Sensitive Empirical Models for Compiler Optimizations

Finding near‐perfect parameters for hardware and code optimizations with automatic multi‐objective design space explorations

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