Compiler-Driven Software Speculation for Thread-Level Parallelism

Current parallelizing compilers can tackle applications exercising regular access patterns on arrays or affine indices, where data dependencies can be expressed in a linear form. Unfortunately, there are cases that independence between statements of code cannot be guaranteed and thus the compiler conservatively produces sequential code. Programs that involve extensive pointer use, irregular access patterns, and loops with unknown number of iterations are examples of such cases. This limits the extraction of parallelism in cases where dependencies are rarely or never triggered at runtime. Speculative parallelism refers to methods employed during program execution that aim to produce a valid parallel execution schedule for programs immune to static parallelization. The motivation for this article is to review recent developments in the area of compiler-driven software speculation for thread-level parallelism and how they came about. The article is divided into two parts. In the first part the fundamentals of speculative parallelization for thread-level parallelism are explained along with a design choice categorization for implementing such systems. Design choices include the ways speculative data is handled, how data dependence violations are detected and resolved, how the correct data are made visible to other threads, or how speculative threads are scheduled. The second part is structured around those design choices providing the advances and trends in the literature with reference to key developments in the area. Although the focus of the article is in software speculative parallelization, a section is dedicated for providing the interested reader with pointers and references for exploring similar topics such as hardware thread-level speculation, transactional memory, and automatic parallelization

Follow-up monitoring in a cat with leishmaniosis and coinfections with Hepatozoon felis and ‘Candidatus Mycoplasma haemominutum’

Case summary A 6-year-old female neutered domestic shorthair cat from Cyprus was presented with multiple ulcerated skin nodules. Cytology and histopathology of the lesions revealed granulomatous dermatitis with intracytoplasmic organisms, consistent with amastigotes of Leishmania species. Biochemistry identified a mild hyperproteinaemia. Blood extraction and PCR detected Leishmania species, Hepatozoon species and ‘Candidatus Mycoplasma haemominutum’ (CMhm) DNA. Subsequent sequencing identified Hepatozoon felis. Additionally, the rRNA internal transcribed spacer 1 locus of Leishmania infantum was partially sequenced and phylogeny showed it to cluster with species derived from dogs in Italy and Uzbekistan, and a human in France. Allopurinol treatment was administered for 6 months. Clinical signs resolved in the second month of treatment with no deterioration 8 months post-treatment cessation. Quantitative PCR and ELISA were used to monitor L infantum blood DNA and antibody levels. The cat had high L infantum DNA levels pretreatment that gradually declined during treatment but increased 8 months post-treatment cessation. Similarly, ELISA revealed high levels of antibodies pretreatment, which gradually declined during treatment and increased slightly 8 months post-treatment cessation. The cat remained PCR positive for CMhm and Hepatozoon species throughout the study. There was no clinical evidence of relapse 24 months post-treatment. Relevance and novel information To our knowledge, this is the first clinical report of a cat with leishmaniosis with H felis and CMhm coinfections. The high L infantum DNA levels post-treatment cessation might indicate that although the lesions had resolved, prolonged or an alternative treatment could have been considere

Leveraging data mining techniques to understand drivers of obesity

Abstract—Substantial research has been carried out to explain\ud the effects of economic variables on obesity, typically\ud considering only a few factors at a time, using parametric\ud linear regression models. Recent studies have made a significant\ud contribution by examining economic factors affecting body\ud weight using the Behavioral Risk Factor Surveillance System\ud data with 27 state-level variables for a period of 20 years (1990-\ud 2010). As elsewhere, the authors solely focus on individual\ud effects of potential drivers of obesity than critical interactions\ud among the drivers. We take some steps to extend the literature\ud and gain a deeper understanding of the drivers of obesity.\ud We employ state-of-the-art data mining techniques to uncover\ud critical interactions that may exist among drivers of obesity\ud in a data-driven manner. The state-of-the-art techniques reveal\ud several complex interactions among economic and behavioral\ud factors that contribute to the rise of obesity. Lower levels of\ud obesity, measured by a body mass index (BMI), belong to\ud female individuals who exercise outside work, enjoy higher\ud levels of education and drink less alcohol. The highest level of\ud obesity, in contrast, belongs to those who fail to exercise outside\ud work, smoke regularly, consume more alcohol and come from\ud lower income groups. These and other complementary results\ud suggest that it is the joint complex interactions among various\ud behavioral and economic factors that gives rise to obesity or\ud lowers it; it is not simply the presence or absence of individual\ud factor

Vectorized Candidate Set Selection for Parallel Ant Colony Optimization

Ant Colony Optimization (ACO) is a well-established nature-inspired heuristic, and parallel versions of the algorithm now exist to take advantage of emerging high-performance computing processors. However, careful attention must be paid to parallel components of such implementations if the full benefit of these platforms is to be obtained. One such component of the ACO algorithm is next node selection, which presents unique challenges in a parallel setting. In this paper, we present a new node selection method for ACO, Vectorized Candidate Set Selection (VCSS), which achieves significant speedup over existing selection methods on a test set of Traveling Salesman Problem instances

Towards Automatic Memory Tuning for In-Memory Big Data Analytics in Clusters

Hadoop provides a scalable solution on traditional cluster-based Big Data platforms but imposes performance overheads due to only supporting on-disk data. Data Analytic algorithms usually require multiple iterations over a dataset and thus, multiple, slow, disk accesses. In contrast, modern clusters possess increasing amounts of main memory that can provide performance benefits by efficiently using main memory caching mechanisms. Apache Spark is an innovative distributed computing framework that supports in-memory computations. Even though this type of computations is very fast, memory is a scarce resource and this can cause bottlenecks to execution or, even worse, lead to failures. Spark offers various choices for memory tuning but this requires in-depth systems-level knowledge and the choices will be different across various workloads and cluster settings. Generally, the optimal choice is achieved by adopting a trial and error approach. This work describes a first step towards an automated selection mechanism for memory optimization that assesses workload and cluster characteristics and selects an appropriate caching scheme. The proposed caching mechanism decreases execution times by up to 25% compared to the default strategy and reduces the risk of main memory exceptions

Toward a more accurate understanding of the limits of the TLS execution paradigm

Thread-Level Speculation (TLS) facilitates the extraction of parallel threads from sequential applications. Most prior work has focused on developing the compiler and architecture for this execution paradigm. Such studies often narrowly concentrated on a specific design point. On the other hand, other studies have attempted to assess how well TLS performs if some architectural/ compiler constraint is relaxed. Unfortunately, such previous studies have failed to truly assess TLS performance potential, because they have been bound to some specific TLS architecture and have ignored one or another important TLS design choice, such as support for out-of-order task spawn or support for intermediate checkpointing. In this paper we attempt to remedy some of the shortcomings of previous TLS limit studies. To this end a characterization approach is pursued that is, as much as possible, independent of specific architecture configurations. High-level TLS architectural support is explored in one common framework. In this way, a more accurate upper-bound on the performance potential of the TLS execution paradigm is obtained (as opposed to some particular architecture design point) and, moreover, relative performance gains can be related to specific high-level architectural support. Finally, in the spirit of performing a comprehensive study, applications from a variety of domains and programming styles are evaluated. Experimental results suggest that TLS performance varies significantly depending on the features provided by the architecture. Additionally, the performance of these systems is not only hindered by data dependences, but also by load imbalance and limited coverage

Molecular mechanism of stabilization of thin films for improved water evaporation protection

All-atom molecular dynamics simulations and experimental characterization have been used to examine the structure and dynamics of novel evaporation-suppressing films where the addition of a water-soluble polymer to an ethylene glycol monooctadecyl ether monolayer leads to improved water evaporation resistance. Simulations and Langmuir trough experiments demonstrate the surface activity of poly(vinyl pyrrolidone) (PVP). Subsequent MD simulations performed on the thin films supported by the PVP sublayer show that, at low surface pressures, the polymer tends to concentrate at the film/water interface. The simulated atomic concentration profiles, hydrogen bonding patterns, and mobility analyses of the water-polymer-monolayer interfaces reveal that the presence of PVP increases the atomic density near the monolayer film, improves the film stability, and reduces the mobility of interfacial waters. These observations explain the molecular basis of the improved efficacy of these monolayer/polymer systems for evaporation protection of water and can be used to guide future development of organic thin films for other applications