Experience-based planning domains (EBPDs) have been proposed to improve problem solving by learning from experience. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend our previous work to generate a set of conditions that determine the scope of applicability of an activity schema. The inferred scope is an abstract representation of a potentially unbounded set of problems, in the form of a 3-valued logical structure, which is used to test the applicability of the respective activity schema for solving different task problems. We validate this work on two classical planning domains and a simulated PR2 in Gazebo.

Formal verification serves as the theoretical basis for the engineering task of correctness and performance (quality) assurance. State of the art model checking, automatic specification refinement and theorem proving are employed to tackle the often undecidable (as imposed by the halting problem) task of complete verification. In this paper we formalize, prove and demonstrate a new unobtrusive way to test a system during runtime. Invocation of actions and examination of reaction of the system and components of the system are examined in run time in an holistic abstract manner, namely, the current state (e.g., state snapshot) the executable (e.g., program) and the environmental current condition (e.g., operating system, hypervisor) are examined by invoking actions and examining the reactions without influencing the actual execution semantics.

We describe a system that uses automated planning to synthesize correct and efficient parallel graph programs from high-level algorithmic specifications. Automated planning allows us to use constraints to declaratively encode program transformations such as scheduling, implementation selection, and insertion of synchronization. Each plan emitted by the planner satisfies all constraints simultaneously, and corresponds to a composition of these transformations. In this way, we obtain an integrated compilation approach for a very challenging problem domain. We have used this system to synthesize parallel programs for four graph problems: triangle counting, maximal independent set computation, preflow-push maxflow, and connected components. Experiments on a variety of inputs show that the synthesized implementations perform competitively with hand-written, highly-tuned code.

Algorithms in new application areas like machine learning and network analysis use "irregular" data structures such as graphs, trees and sets. Writing efficient parallel code in these problem domains is very challenging because it requires the programmer to make many choices: a given problem can usually be solved by several algorithms, each algorithm may have many implementations, and the best choice of algorithm and implementation can depend not only on the characteristics of the parallel platform but also on properties of the input data such as the structure of the graph. One solution is to permit the application programmer to experiment with different algorithms and implementations without writing every variant from scratch. Auto-tuning to find the best variant is a more ambitious solution. These solutions require a system for automatically producing efficient parallel implementations from high-level specifications. Elixir, the system described in this paper, is the first step towards this ambitious goal. Application programmers write specifications that consist of an operator, which describes the computations to be performed, and a schedule for performing these computations. Elixir uses sophisticated inference techniques to produce efficient parallel code from such specifications.

This paper addresses the problem of reducing unnecessary conflicts in optimistic synchronization. Optimistic synchronization must ensure that any two concurrently executing transactions that commit are properly synchronized. Conflict detection is an approximate check for this condition. For efficiency, the traditional approach to conflict detection conservatively checks that the memory locations mutually accessed by two concurrent transactions are accessed only for reading.

For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in "regular" algorithms that use dense arrays, such as finite-differences and FFTs. In this paper, we argue that the dependence graph is not a suitable abstraction for algorithms in new application areas like machine learning and network analysis in which the key data structures are "irregular" data structures like graphs, trees, and sets.

Computations on unstructured graphs are challenging to parallelize because dependences in the underlying algorithms are usually complex functions of runtime data values, thwarting static parallelization. One promising general-purpose parallelization strategy for these algorithms is optimistic parallelization.

We describe an automatic verification method to check whether transactional memories ensure strict serializability a key property assumed of the transactional interface. Our main contribution is a technique for effectively verifying parameterized systems. The technique merges ideas from parameterized hardware and protocol verification--verification by invisible invariants and symmetry reduction--with ideas from software verification--template-based invariant generation and satisfiability checking for quantified formulæ (modulo theories). The combination enables us to precisely model and analyze unbounded systems while taming state explosion.

In this paper, we describe PSE (Postmortem Symbolic Evaluation), a static analysis algorithm that can be used by programmers to diagnose software failures. The algorithm requires minimal information about a failure, namely its kind (e.g. NULL dereference), and its location in the program's source code. It produces a set of execution traces along which the program can be driven to the given failure.