Making Type Inference Practical

Oxhøj, Nicholas; Palsberg, Jens; Schwartzbach, Michael I.

doi:10.7146/dpb.v21i385.6618

Cited by 27 publications

(55 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These constraints, which are presented by means of a trace graph [11,10], are all conditional set inclusions. Using the trace graph technique, we need only define constraints for local situations; the corresponding global constraints will then automatically be derived, as described below.…”

Section: Constraints For Selfmentioning

confidence: 99%

“…Such a global constraint means intuitively that if this trace is possible during execution, then these constraints must hold. For further details about the trace graph technique and how to derive global constraints, see [11,10].…”

Section: Slotsmentioning

confidence: 99%

“…The algorithms in [11,10] had safety checking as an integral part of the type constraints. As a result, type information could not be inferred for incorrect programs that could provoke a msgNotUnderstood error.…”

Section: Constraint-based Analysismentioning

confidence: 99%

“…In most other type inference algorithms for object-oriented languages, for example that of Graver and Johnson [8,7] and our own [11,10,13], inheritance is expanded away rather than dealt with directly. Using prototypes, however, expansion is impossible, since a parent may have an independent state.…”

Section: Introductionmentioning

confidence: 99%

“…In [11] we defined the basic type inference framework, and in [10] we demonstrated an efficient implementation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Type inference of SELF: Analysis of objects with dynamic and multiple inheritance

1995

Self Cite

View full text Add to dashboard Cite

Abstract. We have designed and implemented a type inference algorithm for the Self language. The algorithm can guarantee the safety and disambiguity of message sends, and provide useful information for browsers and optimizing compilers. Self features objects with dynamic inheritance. This construct has until now been considered incompatible with type inference because it allows the inheritance graph to change dynamically. Our algorithm handles this by deriving and solving type constraints that simultaneously define supersets of both the possible values of expressions and of the possible inheritance graphs. The apparent circularity is resolved by computing a global fixed-point, in polynomial time. The algorithm has been implemented and can successfully handle the Self benchmark programs, which exist in the "standard Self world" of more than 40,000 lines of code.

show abstract

Section: Constraints For Selfmentioning

confidence: 99%

Section: Slotsmentioning

confidence: 99%

Section: Constraint-based Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In [11] we defined the basic type inference framework, and in [10] we demonstrated an efficient implementation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Type inference of SELF: Analysis of objects with dynamic and multiple inheritance

1995

Self Cite

View full text Add to dashboard Cite

show abstract

Dynamic versus Static Optimization Techniques for Object‐Oriented Languages

Hölzle¹,

Agesen

1995

Theory & Prac Object Systems

View full text Add to dashboard Cite

Object‐oriented programs can be optimized either dynamically, i.e., based on run‐time information, or statically, i.e., based on program analysis alone. Two promising optimization techniques for object‐oriented languages are type feedback (dynamic) and concrete type inference (static). We directly compare the two techniques, evaluating their effectiveness on a suite of 23 Self programs while keeping other factors constant. Our results show that both systems inline >95% of all sends and deliver similar overall performance with one exception: Self's automatic coercion of machine integers to arbitrary‐precision integers upon overflow confounds type inference and slows down arithmetic‐intensive benchmarks. We also show that a system combining the two optimizations can combine their strengths and outperform each individual optimization. We discuss several other issues which, given the comparable run‐time performance, may influence the choice between type feedback and type inference.

show abstract

Towards the implementation of a uniform object model

Hauck

1993

Parallel Computer Architectures

View full text Add to dashboard Cite

Abstract. In most cases the programming models of distributed object-oriented systems have a two-stage or even a three-stage object model with different kinds of objects for values, distributable and non-distributable objects. This allows an efficient implementation for at least non-distributed objects, because traditional compilers can be used. This paper discusses some aspects of the implementation of a uniform object model that does not know of any distinction between distributable and non-distributable objects and allows an independent application description of the distribution of objects.Instead of integrating distribution later into a non-distributed language our method takes the opposite approach. For the time being a distributed object model is implemented in a general, distributed, and, of course, inefficient way. With some additional information derived by user supplied declarations or automatically by a compiler the general implementation becomes more and more optimized. With assertions like immutable or constantly and initially bound objects the implementation can be optimized such that the non-distributed case is not worse than in traditional object-oriented languages.

show abstract

Making Type Inference Practical

Cited by 27 publications

References 14 publications

Type inference of SELF: Analysis of objects with dynamic and multiple inheritance

Type inference of SELF: Analysis of objects with dynamic and multiple inheritance

Dynamic versus Static Optimization Techniques for Object‐Oriented Languages

Towards the implementation of a uniform object model

Contact Info

Product

Resources

About