Abstract Learning Frameworks for Synthesis

We develop abstract learning frameworks (ALFs) for synthesis that embody the principles of CEGIS (counter-example based inductive synthesis) strategies that have become widely applicable in recent years. Our framework defines a general abstract framework of iterative learning, based on a hypothesis space that captures the synthesized objects, a sample space that forms the space on which induction is performed, and a concept space that abstractly defines the semantics of the learning process. We show that a variety of synthesis algorithms in current literature can be embedded in this general framework. While studying these embeddings, we also generalize some of the synthesis problems these instances are of, resulting in new ways of looking at synthesis problems using learning. We also investigate convergence issues for the general framework, and exhibit three recipes for convergence in finite time. The first two recipes generalize current techniques for convergence used by existing synthesis engines. The third technique is a more involved technique of which we know of no existing instantiation, and we instantiate it to concrete synthesis problems

Mangrove: an Inference-based Dynamic Invariant Mining for GPU Architectures

Likely invariants model properties that hold in operating conditions of a computing system. Dynamic mining of invariants aims at extracting logic formulas representing such properties from the system execution traces, and it is widely used for verification of intellectual property (IP) blocks. Although the extracted formulas represent likely invariants that hold in the considered traces, there is no guarantee that they are true in general for the system under verification. As a consequence, to increase the probability that the mined invariants are true in general, dynamic mining has to be performed to large sets of representative execution traces. This makes the execution-based mining process of actual IP blocks very time-consuming due to the trace lengths and to the large sets of monitored signals. This article presents extit{Mangrove}, an efficient implementation of a dynamic invariant mining algorithm for GPU architectures. Mangrove exploits inference rules, which are applied at run time to filter invariants from the execution traces and, thus, to sensibly reduce the problem complexity. Mangrove allows users to define invariant templates and, from these templates, it automatically generates kernels for parallel and efficient mining on GPU architectures. The article presents the tool, the analysis of its performance, and its comparison with the best sequential and parallel implementations at the state of the art