Bootstrapping higher-order program transformers from interpreters

Sperber, Michael; Glück, Robert; Thiemann, Peter

doi:10.1145/331119.331417

Cited by 8 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three steps (2nd Futamura projection): When we add to (29,30) a step that generates the bindingtime improver from a self-interpreter, we obtain (40, 41, 42). This is the 2nd Futamura projection.…”

Section: Discussionmentioning

confidence: 99%

“…The overall effect of the transformation is that of a binding-time polyvariant offline specializer, even though only a binding-time monovariant offline specializer was used to produce the residual program. Similarly, it is known [21,27,30,31] that optimizing translators can be generated from suitable interpreters. Such techniques can also be used in self-interpreters to improve the specialization of programs.…”

Section: Incremental Specializationmentioning

confidence: 99%

“…Let bti be a binding-time improver and let spec 1 be a specializer as in (29,30), and suppose that spec is a specializer and sint a self-interpreter such that…”

Section: Direct Specializationmentioning

confidence: 99%

“…1, an offline partial evaluator cannot achieve this optimization, but specializing the naive matcher via an instrumented self-interpreter does the job. Another example is the simulation of an online partial evaluator by an offline partial evaluator [33], and the bootstrapping of other program transformers [30]. Note that the transformation in (37) can be optimized at another level: specializing spec with respect to sint yields a new specializer.…”

Section: Direct Specializationmentioning

confidence: 99%

“…This internalizes the techniques of the selfinterpreter in the new specializer. This is know as the 2nd specializer projection [11]; results on generating optimizing specializers are reported elsewhere (e.g., [13,33,30]). …”

Section: Direct Specializationmentioning

confidence: 99%

See 4 more Smart Citations

Jones optimality, binding-time improvements, and the strength of program specializers

Glück

2002

Proceedings of the ASIAN Symposium on Partial Evaluation and Semantics-Based Program Manipulation

Self Cite

View full text Add to dashboard Cite

Jones optimality tells us that a program specializer is strong enough to remove an entire level of self-interpretation. We show that Jones optimality, which was originally aimed at the Futamura projections, plays an important role in binding-time improvements. The main results show that, regardless of the binding-time improvements which we apply to a source program, no matter how extensively, a specializer that is not Jones-optimal is strictly weaker than a specializer which is Jones optimal. By viewing a binding-time improver as a generating extension of a self-interpreter, we can connect our results with previous work on the interpretive approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Incremental Specializationmentioning

confidence: 99%

“…Let bti be a binding-time improver and let spec 1 be a specializer as in (29,30), and suppose that spec is a specializer and sint a self-interpreter such that…”

Section: Direct Specializationmentioning

confidence: 99%

Section: Direct Specializationmentioning

confidence: 99%

Section: Direct Specializationmentioning

confidence: 99%

See 3 more Smart Citations

Jones optimality, binding-time improvements, and the strength of program specializers

Glück

2002

Proceedings of the ASIAN Symposium on Partial Evaluation and Semantics-Based Program Manipulation

Self Cite

View full text Add to dashboard Cite

show abstract

Enforcing Safety Properties Using Type Specialization

Thiemann

2001

Programming Languages and Systems

Self Cite

View full text Add to dashboard Cite

Abstract. Type specialization can serve as a powerful tool in enforcing safety properties on foreign code. Using the specification of a monitoring interpreter, polyvariant type specialization can produce compiled code that is guaranteed to obey a specified safety policy. It propagates a security state at compile-time and generates code for each different security state. The resulting code contains virtually no run-time operations on the security state, at the price of some code duplication. A novel extension of type specialization by intersection types limits the amount of code duplication considerably, thus making the approach practical.A few years back, mobile code was merely an exciting research subject. Meanwhile, the situation has changed dramatically and mobile code is about to invade our everyday lives. Many applications load parts of their code -or even thirdparty extension modules-from the network and run it on the local computer. Web browsers are the most prominent of these applications, but many others (e.g., mobile agents) are gaining importance quickly.The advent of these applications and related incidents has brought an increasing awareness of the problems involved in executing foreign and potentially hostile programs. Clearly, it should be guaranteed that foreign code does not compromise the hosting computer, by crashing the computer (data integrity), by accessing/modifying data that it is not supposed to access (memory integrity) or -more generally-by using resources that it is not supposed to use. A generally accepted way of giving this guarantee is to execute the code in a sand box. Conceptually, a sand box performs monitored execution. It tracks the execution of foreign code and stops it if it attempts an illegal sequence of actions. A property that can be enforced in this way is called a safety property.Such sand box environments have been conceived and implemented with widely different degrees of sophistication. The obvious approach to such a sand box is to perform monitoring by interpreting the code. However, while the approach is highly flexible it involves a large interpretation overhead. Another approach, taken by the JDK [14], is to equip strategic functions in a library with calls to a security manager. A user-provided instantiation of the security manager is then responsible to keep track of the actions and to prevent unwanted actions. The latter approach is less flexible, but more efficient. Java solves the problem of data and memory integrity statically by subjecting all programs to a bytecode verification process [18].

show abstract

Self-applicable online partial evaluation

Sperber

1996

Partial Evaluation

View full text Add to dashboard Cite

Bootstrapping higher-order program transformers from interpreters

Cited by 8 publications

References 24 publications

Jones optimality, binding-time improvements, and the strength of program specializers

Jones optimality, binding-time improvements, and the strength of program specializers

Enforcing Safety Properties Using Type Specialization

Self-applicable online partial evaluation

Contact Info

Product

Resources

About