ACM Home Page
Please provide us with feedback. Feedback
Digital Library logoTake a look at the new version of this page: [ beta version ]. Tell us what you think.
Spill code minimization techniques for optimizing compliers
Full text PdfPdf (607 KB)
Source Conference on Programming Language Design and Implementation archive
Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation table of contents
Portland, Oregon, United States
Pages: 258 - 263  
Year of Publication: 1989
ISBN:0-89791-306-X
Also published in ...
Authors
D. Bernstein  IBM Israel Science and Technology, Technion City, Haifa, Israel
M. Golumbic  IBM Israel Science and Technology, Technion City, Haifa, Israel
y. Mansour  IBM Israel Science and Technology, Technion City, Haifa, Israel
R. Pinter  IBM Israel Science and Technology, Technion City, Haifa, Israel
D. Goldin  IBM Israel Science and Technology, Technion City, Haifa, Israel
H. Krawczyk  IBM Israel Science and Technology, Technion City, Haifa, Israel
I. Nahshon  IBM Israel Science and Technology, Technion City, Haifa, Israel
Sponsor
SIGPLAN: ACM Special Interest Group on Programming Languages
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 9,   Downloads (12 Months): 170,   Citation Count: 37
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/73141.74841
What is a DOI?

ABSTRACT

Global register allocation and spilling is commonly performed by solving a graph coloring problem. In this paper we present a new coherent set of heuristic methods for reducing the amount of spill code generated. This results in more efficient (and shorter) compiled code. Our approach has been compared to both standard and priority-based coloring algorithms, universally outperforming them. In our approach, we extend the capability of the existing algorithms in several ways. First, we use multiple heuristic functions to increase the likelihood that less spill code will be inserted. We have found three complementary heuristic functions which together appear to span a large proportion of good spill decisions. Second, we use a specially tuned greedy heuristic for determining the order of deleting (and hence coloring) the unconstrained vertices. Third, we have developed a “cleaning” technique which avoids some of the insertion of spill code in non-busy regions.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

AH82
 
B85
Bollobas, B., Random Graphs, Academic Press, London, 1985.
B79
 
C81
Chaitin, G.J., Auslander, M.A., Chandra, A.K., Cocke, J., Hopkins, M.E., and Markstein, P.W., 'Register allocation via coloring', Computer Languages 6 (1981), 47-57.
C82
CH84
 
DGP88
 
G80
 
G85
Golumbic, M. C., ed., ~lnterval Graphs and Related Topics', a special issue of Discrete Math. 55 (1985), 113-243.
LH86
 
M72
Matula, D.W., Marble, G., and Isaacson, J.D., 'Graph coloring algorithms', in Graph Theory and Computing, (Read, R.C., ed.), Academic Press, New York, 1972.
W86

CITED BY  37

Collaborative Colleagues:
D. Bernstein: colleagues
M. Golumbic: colleagues
y. Mansour: colleagues
R. Pinter: colleagues
D. Goldin: colleagues
H. Krawczyk: colleagues
I. Nahshon: colleagues