This paper presents a generalization of standard effect systems that we call contextual effects. A traditional effect system computes the effect of an expression e. Our system additionally computes the effects of the computational context in which e occurs. More specifically, we compute the effect of the computation that has already occurred (the prior effect) and the effect of the computation yet to take place (the future effect).Contextual effects are useful when the past or future computation of the program is relevant at various program points. We present two substantial examples. First, we show how prior and future effects can be used to enforce transactional version consistency (TVC), a novel correctness property for dynamic software updates. TVC ensures that programmer-designated transactional code blocks appear to execute entirely at the same code version, even if a dynamic update occurs in the middle of the block. Second, we show how future effects can be used in the analysis of multi-threaded programs to find thread-shared locations. This is an essential step in applications such as data race detection.

We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) for Big Data of high dimensionality. PFBP partitions the data matrix both in terms of rows as well as columns. By employing the concepts of p-values of conditional independence tests and meta-analysis techniques, PFBP relies only on computations local to a partition while minimizing communication costs, thus massively parallelizing computations. Similar techniques for combining local computations are also employed to create the final predictive model. PFBP employs asymptotically sound heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores. An extensive comparative evaluation also demonstrates the effectiveness of PFBP against other algorithms in its class. The heuristics presented are general and could potentially be employed to other greedy-type of FS algorithms. An application on simulated Single Nucleotide Polymorphism (SNP) data with 500K samples is provided as a use case.

Abstract. We present BDDT, a task-parallel runtime system that dynamically discovers and resolves dependencies among parallel tasks. BDDT allows the programmer to specify detailed task footprints on any memory address range, multidimensional array tile or dynamic region. BDDT uses a block-based dependence analysis with arbitrary granularity. The analysis is applicable to existing C programs without having to restructure object or array allocation, and provides flexibility in array layouts and tile dimensions. We evaluate BDDT using a representative set of benchmarks, and we compare it to SMPSs (the equivalent runtime system in StarSs) and OpenMP. BDDT performs comparable to or better than SMPSs and is able to cope with task granularity as much as one order of magnitude finer than SMPSs. Compared to OpenMP, BDDT performs up to 3.9× better for benchmarks that benefit from dynamic dependence analysis. BDDT provides additional data annotations to bypass dependence analysis. Using these annotations, BDDT outperforms OpenMP also in benchmarks where dependence analysis does not discover additional parallelism, thanks to a more efficient implementation of the runtime system.

Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis, sarcasm correction and topic modeling. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.

The currently dominant programming models to write software for multicore processors use threads that run over shared memory. However, as the core count increases, cache coherency protocols get very complex and ineffective, and maintaining a shared memory abstraction becomes expensive and impractical. Moreover, writing multithreaded programs is notoriously difficult, as the programmer needs to reason about all the possible thread interleavings and interactions, including the myriad of implicit, non-obvious, and often unpredictable thread interactions through shared memory. Overall, as processors get more cores and parallel software becomes mainstream, the shared memory model reaches its limits regarding ease of programming and efficiency.This position paper presents two ideas aiming to solve the problem. First, we restrict the way the programmer expresses parallelism: The program is a collection of possibly recursive tasks, where each task is atomic and cannot communicate with any other task during its execution. Second, we relax the requirement for coherent shared memory: Each task defines its memory footprint, and is guaranteed to have exclusive access to that memory during its execution. Using this model, we can then define a runtime system that transparently performs the data transfers required among cores without cache coherency, and also produces a deterministic execution of the program, provably equivalent to its sequential elision.

Label flow analysis is a fundamental static analysis problem with a wide variety of applications. Previous work by Mossin developed a polynomial time subtyping-based label flow inference that supports Hindley-Milner style polymorphism with polymorphic recursion. Rehof et al have developed an efficient O(n 3 ) inference algorithm for Mossin's system based on context-free language (CFL) reachability. In this paper, we extend these results to a system that also supports existential polymorphism, which is important for precisely describing correlations among members of a structured type, even when values of that type are part of dynamic data structures. We first develop a provably sound checking system based on polymorphicallyconstrained types. As usual, we restrict universal quantification to the top level of a type, but existential quantification is first class, with subtyping allowed between existentials with the same binding structure. We then develop a CFL-based inference system. Programmers specify which positions in a type are existentially quantified, and the algorithm infers the constraints bound in the type, or rejects a program if the annotations are inconsistent.

A proxy object is a surrogate or placeholder that controls access to another target object. Proxies can be used to support distributed programming, lazy or parallel evaluation, access control, and other simple forms of behavioral reflection. However, wrapper proxies (like futures or suspensions for yet-to-be-computed results) can require significant code changes to be used in statically-typed languages, while proxies more generally can inadvertently violate assumptions of transparency, resulting in subtle bugs.To solve these problems, we have designed and implemented a simple framework for proxy programming that employs a static analysis based on qualifier inference, but with additional novelties. Code for using wrapper proxies is automatically introduced via a classfile-to-classfile transformation, and potential violations of transparency are signaled to the programmer. We have formalized our analysis and proven it sound. Our framework has a variety of applications, including support for asynchronous method calls returning futures. Experimental results demonstrate the benefits of our framework: programmers are relieved of managing and/or checking proxy usage, analysis times are reasonably fast, overheads introduced by added dynamic checks are negligible, and performance improvements can be significant. For example, changing two lines in a simple RMI-based peer-to-peer application and then using our framework resulted in a large performance gain.

We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS).