Loop squashing transformations for amorphous slicing

Program slicing is a source code extraction technique that can be used to support reverse engineering by automatically extracting executable subprograms that preserve some aspect of the original program's semantics. Although minimal slices are not generally computable, safe approximate algorithms can be used to good effect. However, the precision of such slicing algorithms is a major factor in determining the value of slicing for reverse engineering. Amorphous slicing has been proposed as a way of reducing the size of a slice. Amorphous slices preserve the aspect of semantic interest, but not the syntax that denotes it, making them generally smaller than their syntactically restricted counterparts. Amorphous slicing is suitable for many reverse engineering applications, since reverse engineering typically abandons the existing syntax to facilitate structural improvements. Previous work on amorphous slicing has not attempted to exploit its potential to apply loop-squashing transformations. This paper presents an algorithm for amorphous slicing of loops, which identifies induction variables, transformation rule templates and iteration-determining compile-time expressions. The algorithm uses these to squash certain loops into conditional assignments. The paper also presents an inductive proof of the rule templates and illustrates the application of the algorithm with a detailed example of loop squashing

XARK: an extensible framework for automatic recognition of computational kernels

This is a post-peer-review, pre-copyedit version of an article published in ACM Transactions on Programming Languages and Systems. The final authenticated version is available online at: http://dx.doi.org/10.1145/1391956.1391959[Abstract] The recognition of program constructs that are frequently used by software developers is a powerful mechanism for optimizing and parallelizing compilers to improve the performance of the object code. The development of techniques for automatic recognition of computational kernels such as inductions, reductions and array recurrences has been an intensive research area in the scope of compiler technology during the 90's. This article presents a new compiler framework that, unlike previous techniques that focus on specific and isolated kernels, recognizes a comprehensive collection of computational kernels that appear frequently in full-scale real applications. The XARK compiler operates on top of the Gated Single Assignment (GSA) form of a high-level intermediate representation (IR) of the source code. Recognition is carried out through a demand-driven analysis of this high-level IR at two different levels. First, the dependences between the statements that compose the strongly connected components (SCCs) of the data-dependence graph of the GSA form are analyzed. As a result of this intra-SCC analysis, the computational kernels corresponding to the execution of the statements of the SCCs are recognized. Second, the dependences between statements of different SCCs are examined in order to recognize more complex kernels that result from combining simpler kernels in the same code. Overall, the XARK compiler builds a hierarchical representation of the source code as kernels and dependence relationships between those kernels. This article describes in detail the collection of computational kernels recognized by the XARK compiler. Besides, the internals of the recognition algorithms are presented. The design of the algorithms enables to extend the recognition capabilities of XARK to cope with new kernels, and provides an advanced symbolic analysis framework to run other compiler techniques on demand. Finally, extensive experiments showing the effectiveness of XARK for a collection of benchmarks from different application domains are presented. In particular, the SparsKit-II library for the manipulation of sparse matrices, the Perfect benchmarks, the SPEC CPU2000 collection and the PLTMG package for solving elliptic partial differential equations are analyzed in detail.Ministeiro de Educación y Ciencia; TIN2004-07797-C02Ministeiro de Educación y Ciencia; TIN2007-67537-C03Xunta de Galicia; PGIDIT05PXIC10504PNXunta de Galicia; PGIDIT06PXIB105228P

Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category

Hilbert's Tenth Problem in Coq (Extended Version)

We formalise the undecidability of solvability of Diophantine equations, i.e. polynomial equations over natural numbers, in Coq's constructive type theory. To do so, we give the first full mechanisation of the Davis-Putnam-Robinson-Matiyasevich theorem, stating that every recursively enumerable problem -- in our case by a Minsky machine -- is Diophantine. We obtain an elegant and comprehensible proof by using a synthetic approach to computability and by introducing Conway's FRACTRAN language as intermediate layer. Additionally, we prove the reverse direction and show that every Diophantine relation is recognisable by $\mu$-recursive functions and give a certified compiler from $\mu$-recursive functions to Minsky machines.Comment: submitted to LMC