Heap graph based software theft detection

published_or_final_versio

Profiling large-scale lazy functional programs

The LOLITA natural language processing system is an example of one of the ever increasing number of large-scale systems written entirely in a functional programming language. The system consists of over 50,000 lines of Haskell code and is able to perform a number of tasks such as semantic and pragmatic analysis of text, context scanning and query analysis. Such a system is more useful if the results are calculated in real-time, therefore the efficiency of such a system is paramount. For the past three years we have used profiling tools supplied with the Haskell compilers GHC and HBC to analyse and reason about our programming solutions and have achieved good results; however, our experience has shown that the profiling life-cycle is often too long to make a detailed analysis of a large system possible, and the profiling results are often misleading. A profiling system is developed which allows three types of functionality not previously found in a profiler for lazy functional programs. Firstly, the profiler is able to produce results based on an accurate method of cost inheritance. We have found that this reduces the possibility of the programmer obtaining misleading profiling results. Secondly, the programmer is able to explore the results after the execution of the program. This is done by selecting and deselecting parts of the program using a post-processor. This greatly reduces the analysis time as no further compilation, execution or profiling of the program is needed. Finally, the new profiling system allows the user to examine aspects of the run-time call structure of the program. This is useful in the analysis of the run-time behaviour of the program. Previous attempts at extending the results produced by a profiler in such a way have failed due to the exceptionally high overheads. Exploration of the overheads produced by the new profiling scheme show that typical overheads in profiling the LOLITA system are: a 10% increase in compilation time; a 7% increase in executable size and a 70% run-time overhead. These overheads mean a considerable saving in time in the detailed analysis of profiling a large, lazy functional program

Side-Effect Localization for Lazy, Purely Functional Languages via Aspects

Many side-effecting programming activities, such as profiling and tracing, can be formulated as crosscutting concerns and be framed as side-effecting aspects in the aspect-oriented programming paradigm. The benefit gained from this separation of concerns is particularly evident in purely functional programming, as adding such aspects using techniques such as monadification will generally lead to crosscutting changes. This paper presents an approach to provide side-effecting aspects for lazy purely functional languages in a user transparent fashion. We propose a simple yet direct state manipulation construct for developing side-effecting aspects and devise a systematic monadification scheme to translate the woven code to monadic style purely functional code. Furthermore, we present a static and dynamic semantics of the aspect programs and reason about the correctness of our monadification scheme with respect to them

Chisel: Reliability- and Accuracy-Aware Optimization of Approximate Computational Kernels

The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy. We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification. We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees.National Science Foundation (U.S.) (Grant CCF-1036241)National Science Foundation (U.S.) (Grant CCF-1138967)National Science Foundation (U.S.) (Grant IIS-0835652)United States. Dept. of Energy (Grant DE-SC0008923)United States. Defense Advanced Research Projects Agency (Grant FA8650-11-C-7192)United States. Defense Advanced Research Projects Agency (Grant FA8750-12-2-0110)United States. Defense Advanced Research Projects Agency (Grant FA-8750-14-2-0004