Sequential Monte Carlo for Graphical Models

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs

Locality and Singularity for Store-Atomic Memory Models

Robustness is a correctness notion for concurrent programs running under relaxed consistency models. The task is to check that the relaxed behavior coincides (up to traces) with sequential consistency (SC). Although computationally simple on paper (robustness has been shown to be PSPACE-complete for TSO, PGAS, and Power), building a practical robustness checker remains a challenge. The problem is that the various relaxations lead to a dramatic number of computations, only few of which violate robustness. In the present paper, we set out to reduce the search space for robustness checkers. We focus on store-atomic consistency models and establish two completeness results. The first result, called locality, states that a non-robust program always contains a violating computation where only one thread delays commands. The second result, called singularity, is even stronger but restricted to programs without lightweight fences. It states that there is a violating computation where a single store is delayed. As an application of the results, we derive a linear-size source-to-source translation of robustness to SC-reachability. It applies to general programs, regardless of the data domain and potentially with an unbounded number of threads and with unbounded buffers. We have implemented the translation and verified, for the first time, PGAS algorithms in a fully automated fashion. For TSO, our analysis outperforms existing tools

Type oriented parallel programming for Exascale

Whilst there have been great advances in HPC hardware and software in recent years, the languages and models that we use to program these machines have remained much more static. This is not from a lack of effort, but instead by virtue of the fact that the foundation that many programming languages are built on is not sufficient for the level of expressivity required for parallel work. The result is an implicit trade-off between programmability and performance which is made worse due to the fact that, whilst many scientific users are experts within their own fields, they are not HPC experts. Type oriented programming looks to address this by encoding the complexity of a language via the type system. Most of the language functionality is contained within a loosely coupled type library that can be flexibly used to control many aspects such as parallelism. Due to the high level nature of this approach there is much information available during compilation which can be used for optimisation and, in the absence of type information, the compiler can apply sensible default options thus supporting both the expert programmer and novice alike. We demonstrate that, at no performance or scalability penalty when running on up to 8196 cores of a Cray XE6 system, codes written in this type oriented manner provide improved programmability. The programmer is able to write simple, implicit parallel, HPC code at a high level and then explicitly tune by adding additional type information if required.Comment: As presented at the Exascale Applications and Software Conference (EASC), 9th-11th April 201