Global conditions in debugging distributed programs

Distributed Breakpoint Detection in Message-Passing Programs

Enumerating consistent global states of a computation is a fundamental problem in parallel computing with applications to debugging, testing and runtime verification of parallel programs. Breadth-first search (BFS) enumeration is especially useful for these applications as it finds an erroneous consistent global state with the least number of events possible. The total number of executed events in a global state is called its rank. BFS also allows enumeration of all global states of a given rank or within a range of ranks. If a computation on n processes has m events per process on average, then the traditional BFS (Cooper-Marzullo and its variants) requires \(\mathcal {O}(\frac{m^{n-1}}{n})\) space in the worst case, whereas our algorithm performs the BFS requires \(\mathcal {O}(m^2n^2)\) space. Thus, we reduce the space complexity for BFS enumeration of consistent global states exponentially, and give the first polynomial space algorithm for this task. In our experimental evaluation of seven benchmarks, traditional BFS fails in many cases by exhausting the 2 GB heap space allowed to the JVM. In contrast, our implementation uses less than 60 MB memory and is also faster in many cases.

Cyclic debugging of nondeterministic parallel programs requires some kind of record and replay technique, because successive executions may produce different results even if the same input is supplied. The NOndeterministic Program Evaluator NOPE is an implementation of record and replay for message-passing systems. During an initial record phase, ordering information about occurring events is stored in traces, which preserve an equivalent execution during follow-up replay phases. In comparison to other tools, NOPE produces less overhead in time and space by relying on certain properties of MPI and PVM. The key factor is the non-overtaking rule which simplifies not only tracing and replay but also race condition detection. In addition, an automatic approach to event manipulation allows extensive investigation of nondeterministic behavior.

Highly dynamic marine redox state through the Cambrian explosion highlighted by authigenic δ238U records

Parallel debugging focuses on solving four problems: nondeterminism, global states, communication related errors and performance debugging. PVM is a widely used programming environment for network of workstations, but it provides little support on debugging. The authors have developed an integrated parallel debugger (called BUSTER) for PVM programs running on workstation clusters. They introduce the ideas and implementation techniques of the debugger.

We consider a multi-agent system where the overall performance is affected by the joint actions or policies of agents. However, each agent only observes a partial view of the global state condition. This model is known as a decentralized partially-observable Markov decision process (DEC-POMDP), which can be considered more applicable in real-world applications such as communication networks. It is known that the exact solution to a DEC-POMDP is NEXP-complete and memory requirements grow exponentially even for finite-horizon problems. In this paper, we propose to address these issues by using an online model-free technique and by exploiting the locality of interaction among agents in order to approximate the joint optimal policy. Simulation results show the effectiveness and convergence of the proposed algorithm in the context of resource allocation for multiagent wireless multi-hop networks.

Histograms summarize distributions of numerical data into densities per ranges of values and are typically used for fast approximation of selectivities of range predicates. This paper introduces an approach for intuitionistic fuzzy selectivity estimation from histograms when predicates are expressed in fuzzy terms with membership functions on the numeric values, where the degree of indefiniteness corresponds to the level of uncertainty resulting from the accuracy loss due to approximation. In the case of bidimensional histograms, a method for estimating the joint selectivity of conjunctive predicates over the two dimensions is proposed in a way that the joint selectivity can be considered as a measure of correlation. The approach is validated through the use of SQL queries against a synthetically generated dataset and its corresponding bidimensional histogram.