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Automatically generating the back en...

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Automatically generating the back end of a compiler using declarative machine descriptions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automatically generating the back end of a compiler using declarative machine descriptions./
作者:	Dias, Joao.
面頁冊數:	236 p.
附註:	Adviser: Norman Ramsey.
Contained By:	Dissertation Abstracts International70-03B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3350954
ISBN:	9781109066418

Automatically generating the back end of a compiler using declarative machine descriptions.
Dias, Joao.

Automatically generating the back end of a compiler using declarative machine descriptions. - 236 p.

Adviser: Norman Ramsey.

Thesis (Ph.D.)--Harvard University, 2009.

Although I have proven that the general problem is undecidable, I show how, for machines of practical interest, to generate the back end of a compiler. Unlike previous work on generating back ends, I generate the machine-dependent components of the back end using only information that is independent of the compiler's internal data structures and intermediate form. My techniques substantially reduce the burden of retargeting the compiler: although it is still necessary to master the target machine's instruction set, it is not necessary to master the data structures and algorithms in the compiler's back end. Instead, the machine-dependent knowledge is isolated in the declarative machine descriptions.

ISBN: 9781109066418Subjects--Topical Terms:

626642
Computer Science.

Automatically generating the back end of a compiler using declarative machine descriptions.
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520 $a Although I have proven that the general problem is undecidable, I show how, for machines of practical interest, to generate the back end of a compiler. Unlike previous work on generating back ends, I generate the machine-dependent components of the back end using only information that is independent of the compiler's internal data structures and intermediate form. My techniques substantially reduce the burden of retargeting the compiler: although it is still necessary to master the target machine's instruction set, it is not necessary to master the data structures and algorithms in the compiler's back end. Instead, the machine-dependent knowledge is isolated in the declarative machine descriptions.
520 $a The largest machine-dependent component in a back end is the instruction selector. Previous work has shown that it is difficult to generate a high-quality instruction selector. But by adopting the compiler architecture developed by Davidson and Fraser (1984), I can generate a naive instruction selector and rely upon a machine-independent optimizer to improve the machine instructions. Unlike previous work, my generated back ends produce code that is as good as the code produced by hand-written back ends.
520 $a My code generator translates a source program into tiles, where each tile implements a simple computation like addition. To implement the tiles, I compose machine instructions in sequence and use equational reasoning to identify sequences that implement tiles. Because it is undecidable whether a tile can be implemented, I use a heuristic to limit the set of sequences considered. Unlike standard heuristics, which may limit the length of a sequence, the number of sequences considered, or the complexity of the result computed by a sequence, my heuristic uses a new idea: to limit the amount of reasoning required to show that a sequence of instructions implements a tile. The limit, which is chosen empirically, enables my search to find instruction selectors for the x86, PowerPC, and ARM in a few minutes each.
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