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Jones optimality implies that a program specializer is strong enough to remove an entire level of self-interpretation. This paper argues that Jones optimality, which was originally devised as a criterion for self-applicable specializers, plays a fundamental role in the use of a binding-time improvement prepass prior to specialization. We establish that, regardless of the binding-time improvements applied to a subject program (no matter how extensively), a specializer that is not Jones-optimal is strictly weaker than a specializer that is Jones-optimal. We describe the main approaches that increase the strength of a specializer without requiring its modification, namely incremental specialization and the interpretive approach, and show that they are equally powerful when the specializer is bti-universal. Since this includes the generation of program specializers from interpreters, the theoretical possibility of bootstrapping powerful specializers is established.

Generalized Partial Computation (GPC) is a program transformation method utilizing partial information about input data, abstract data types of auxiliary functions and the logical structure of a source program. GPC uses both an inference engine such as a theorem prover and a classical partial evaluator to optimize programs. Therefore, GPC is more powerful than classical partial evaluators but harder to implement and control. We have implemented an experimental GPC system called WSDFU (Waseda Simplify-Distribute-Fold-Unfold). This paper demonstrates the power of the program transformation system as well as its theorem prover and discusses some future works.

We survey fundamental concepts for inverse programming and then present the Universal Resolving Algorithm, an algorithm for inverse computation in a first-order, functional programming language. We discuss the key concepts of the algorithm, including a three-step approach based on the notion of a perfect process tree, and demonstrate our implementation with several examples of inverse computation.

Structures using constructors are of ordinary use in functional programming to represent data structures of unbound size. Lack of associativity of constructors, however, hinders program analyses or efficient executions. This paper describes ideas of abstraction toward constructors, and similarly abstraction from constructing functions, which we call functional constructors. We demonstrate our ideas making program analyses easier and enable transformation to efficient execution.

Generalized Partial Computation (GPC) is a program transformation method utilizing partial information about input data, properties of auxiliary functions and the logical structure of a source program. GPC uses both an inference engine such as a theorem prover and a classical partial evaluator to optimize programs. Therefore, GPC is more powerful than classical partial evaluators but harder to implement and control. We have implemented an experimental GPC system called WSDFU (Waseda Simplify-Distribute-Fold-Unfold). This paper discusses the power of the program transformation system, its theorem prover and future works.

Loop optimization plays an important role in compiler optimization and program transformation. Many sophisticated techniques such as loop-invariance code motion have been developed. Loop peeling is a technique to assist parallelization of loops by unfolding loops a few times. This paper introduces a novel technique called loop peeling based on quasi-invariance/induction variables. It aims at finding a general and automatic method to derive how many times a given loop should be peeled. Our technique allows for a number of iterations before some variables assigned inside a given loop become invariance or induction variables. In this paper we define the notion of quasi-invariance/induction variables, present an algorithm for statically computing the optimal peeling length of a given loop. Our technique can increase the accuracy of program analyses, improve the effectiveness of loop peeling and is well-suited as supporting other optimization techniques in the context of supercomputers.

One of the main discoveries in the seventies was that the concept of a generating extension covers a very wide class of apparently different program generators. Program specialization, or partial evaluation, is powerful because it provides uniform techniques for the automatic implementation of generating extensions from ordinary programs. The Futamura projections stand as the cornerstone of the development of program specialization. This paper takes the idea of the Futamura projections further. Threedegeneration projections are formulated which tell us how to achieve the reverse goal by program composition, namely turning a generating extension into an ordinary program. The fact that program composition can invert the effect of program specialization shows that these projections are dual in a sense. The degeneration projections complete a missing link between programs and generating extensions and allow for novel applications of program transformation.

Program specialization can divide a computation into several computation stages. This paper investigates the theoretical limitations and practical problems of standard specialization tools, presents multi-level specialization, and demonstrates that, in combination with the cogen approach, it is far more practical than previously supposed. The program generator which we designed and implemented for a higher-order functional language converts programs into very compact multi-level generating extensions that guarantee fast successive specialization. Experimental results show a remarkable reduction of generation time and generator size compared to previous attempts of multi-level specialization by self-application. Our approach to multi-level specialization seems well-suited for applications where generation time and program size are critical.