Communications - February 2009

day is anecdotal. In many disciplines, reviewers and peer scientists expect papers to include sufficient information so other groups are able to reproduce the results being obtained, but papers do not adequately capture compiler experiments where numerous implementation details determine the final outcome. With the help of open source software, the Web can be used to publish the software and data used in the evaluations being reported. Major conferences and organizations (such as ACM) must provide mechanisms for publishing software, inputs, and experimental data as metadata for the publications that report these experiments. Such repositories are useful for measuring progress and could also serve as a resource to those interested in the history of technology.

Develop curriculum recommendations on compiler technology. Compiler technology is complicated, and advances in the discipline require bright researchers and practitioners. Meanwhile, computer science has grown as a discipline with numerous exciting areas of research and studies to pursue. The compiler community must convey the importance and intellectual beauty of the discipline to each generation of students. Compiler courses must clearly demonstrate to students the extraordinary importance, range of applicability, and internal elegance of what is one of the most fundamental enabling technologies of computer science.

Whereas compiler technology used to be a core course in most undergraduate programs, many institutions now offer their compiler course as an optional upper-level course for computer science and computer engineering students and often include interesting projects that deliver a capstone experience. A good upper-level compiler course combines data structures, algorithms, and tools for students as they build a large piece of software that performs an interesting and practical function. However, these courses are considered difficult by both faculty and students, and students often have other interesting choices. Thus, fewer students are exposed to the foundational ideas in compilers or to compilers as a potential area only for graduate study.

Compiler algorithms are of tremendous educational value for anyone in-

terested in compiler implementation and machine design. Knowledge of the power and limitations of compiler algorithms is valuable to all users of compilers, debuggers, and any tool built using compiler algorithms that encapsulate many, if not most, of the important program-analysis and transformation strategies necessary for performance and correctness. Therefore, learning about compiler algorithms leads to learning about program optimization and typical programming errors in a deep and rigorous manner. For these reasons, programmers with a solid background in compilers tend to excel in their profession.

One approach to promoting knowledge of compiler algorithms involves discussion of specific compiler algorithms throughout the computer science curriculum—in automata theory, programming languages, computer architecture, algorithms, parallel programming, and software engineering. The main challenge is to define the key compiler concepts that all computer science majors must know and to suggest the content that should be included in core computer science courses.

A second complementary approach is to develop advanced courses focusing on compiler-based analyses for software engineering, scientific computing, and security. They could assume the basic concepts taught in core courses and move quickly into new material (such as virus detection based on compiler technology). The challenge is how to design courses that train students in advanced compiler techniques and apply them to areas that are interesting and relevant (such as software reliability and software engineering). They may not be called “compiler courses” but labeled in ways that reflect a particular application area (such as computer security, verification tools, program-understanding tools) or perhaps something more general like program analysis and manipulation.

Conclusion

Although the compiler field has transformed the landscape of computing, important compilation problems remain, even as new challenges (such as multicore programming) have appeared. The unsolved compiler challenges (such as how to raise the abstraction level of

parallel programming, develop secure and robust software, and verify the entire software stack) are of great practical importance and rank among the most intellectually challenging problems in computer science today.

To address them, the compiler field must develop the technologies that enable more of the progress the field has experienced over the past 50 years. Computer science educators must attract some of the brightest students to the compiler field by showing them its deep intellectual foundations, highlighting the broad applicability of compiler technology to many areas of computer science. Some challenges facing the field (such as the lack of flexible and powerful compiler infrastructures) can be solved only through communitywide effort. Funding agencies and industry must be made aware of the importance and complexity of the challenges and willing to invest long-term financial and human resources toward finding solutions.

References

1. kirkegaard, k.J., haghighat, M.r., narayanaswamy, r., shankar, b., faiman, n, and sehr, d.c. Methodology, tools, and techniques to parallelize large-scale applications: a case study. Intel Technology Journal 11, 4 (nov. 2007).

2. Mccarthy, J. a basis for a mathematical theory of computation. in Computer Programming and Formal Systems, P. braffort and d. hirschberg, eds. north-holland Publishing company, amsterdam, the netherlands, 1963, 33-70.

3. Merritt, r. computer r&d rocks on. EE Times (nov. 21, 2005); www.eetimes.com/showarticle. jhtml?articleid=174400350.

Acknowledgment We thank vikram adve, calin cascaval, susan graham, Jim larus, Wei li, greg Morrisett, and david sehr for their many valuable and thoughtful suggestions. special thanks to laurie hendren for organizing the discussion on education and preparing the text of the recommendation concerning curriculum recommendations on compiler technology. We gratefully acknowledge the support of the U.s. national science foundation under award no. 0605116. the opinions and recommendations are those of the authors and do not necessarily reflect the views of the national science foundation. We also acknowledge all workshop participants whose insight and enthusiasm made this article possible, v. adve, a. adl-tabatabatai, s. amarasinghe, a. appel, d. callahan, c. cascaval, k. cooper, a. chtchelkanova, f. darema, J. davidson, W. harrod, J. hiller, l. hendren, d. kuck, M. lam, J. larus, W. li, k. Mckinley, g. Morrisett, t. Pinkston, v. sarkar, d. sehr, k. stoodley, d. tarditi, r. tatge, and k. yelick.

Mary Hall ( mhall@cs.utah.edu) is an associate professor in the school of computing at the University of Utah, salt lake city, Ut.

David Padua ( padua@illinois.edu) is the donald biggar Willett Professor of computer science at the University of illinois at Urbana-champaign.

Keshav Pingali ( pingali@cs.utexas.edu) is the W.a. “tex” Moncrief chair of grid and distributed computing Professor in the department of computer sciences at the University of texas, austin, and a professor in the institute for computational engineering and sciences also at the University of texas, austin.