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 (2004)

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by Keith D. Cooper , Alexander Grosul , Timothy J. Harvey , Steve Reeves , Devika Subramanian , Linda Torczon , Todd Waterman

Venue:	Proc of the 2004 Los Alamos Computer Science Institute (LACSI) Symposium
Citations:	5 - 1 self

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Datum	Value	Source
TITLE	Exploring the structure of the space of compilation sequences using randomized search algorithms	INFERENCE
AUTHOR NAME	Keith D. Cooper	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
AUTHOR NAME	Alexander Grosul	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
AUTHOR NAME	Timothy J. Harvey	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
AUTHOR NAME	Steve Reeves	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
AUTHOR NAME	Devika Subramanian	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
AUTHOR NAME	Linda Torczon	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
AUTHOR NAME	Todd Waterman	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas, USA	SVM HeaderParse 0.2
ABSTRACT	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, 23, 7, 10, 21] 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 10of-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. 1 Compilation Sequences 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 pro-	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	Proc of the 2004 Los Alamos Computer Science Institute (LACSI) Symposium	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	24 found	ParsCit 1.0