Abstract

Abstract
Several techniques for implementing Prolog in a efficient manner have been devised since the original interpreter, many of them aimed at achieving more speed. There are two main approaches to efficient Prolog implementation: (1) compilers to bytecode and then interpreting it (emulators) or (2) compilers to native code. Emulators have smaller load/compilation time and are a good solution for their simplicity when speed is not a priority. Compilers are more complex than emulators, and the difference is much more acute if some form of code analysis is performed as part of the compilation, which impacts development time. Generation of low level code promises faster programs at the expense of using more resources during the compilation phase. In our work besides using an mixed execution mode, we design an optimizing compiler that using type feedback profiling, dynamic compilation and dynamic deoptimization for improving the performance of logic programming languages. © J.UCS.

Abstract
Consider the problem of scheduling sporadic message transmission requests with deadlines. For wired channels, this has been achieved successfully using the CAN bus. For wireless channels, researchers have recently proposed a similar solution; a collision-free medium access control (AMC) protocol that implements static-priority scheduling. Unfortunately no implementation has been reported, yet. We implement and evaluate it to find that the implementation indeed is collision-free and prioritized. This allows us to develop schedulability analysis for, the implementation. We measure the response times of messages in our implementation and find that our new response-time analysis indeed offers an upper bound on the response times. This enables a new class of wireless real-time systems with timeliness guarantees for sporadic messages and it opens-up a new research area: schedulability analysis for wireless networks.

Abstract

Abstract
We introduce Computational Depth, a measure for the amount of "nonrandom" or "useful" information in a string by considering the difference of various Kolmogorov complexity measures. We investigate three instantiations of Computational Depth: center dot Basic Computational Depth, a clean notion capturing the spirit of Bennett's Logical Depth. We show that a Turing machine M runs in time polynomial on average over the time-bounded universal distribution if and only if for all inputs x, M uses time exponential in the basic computational depth of x. center dot Sublinear-time Computational Depth and the resulting concept of Shallow Sets, a generalization of sparse and random sets based on low depth properties of their characteristic sequences. We show that every computable set that is reducible to a shallow set has polynomial-size circuits. center dot Distinguishing Computational Depth, measuring when strings are easier to recognize than to produce. We show that if a Boolean formula has a nonnegligible fraction of its satisfying assignments with low depth, then we can find a satisfying assignment efficiently.

Abstract