The Adaptive Priority Queue with Elimination and Combining

Priority queues are fundamental abstract data structures, often used to manage limited resources in parallel programming. Several proposed parallel priority queue implementations are based on skiplists, harnessing the potential for parallelism of the add() operations. In addition, methods such as Flat Combining have been proposed to reduce contention by batching together multiple operations to be executed by a single thread. While this technique can decrease lock-switching overhead and the number of pointer changes required by the removeMin() operations in the priority queue, it can also create a sequential bottleneck and limit parallelism, especially for non-conflicting add() operations. In this paper, we describe a novel priority queue design, harnessing the scalability of parallel insertions in conjunction with the efficiency of batched removals. Moreover, we present a new elimination algorithm suitable for a priority queue, which further increases concurrency on balanced workloads with similar numbers of add() and removeMin() operations. We implement and evaluate our design using a variety of techniques including locking, atomic operations, hardware transactional memory, as well as employing adaptive heuristics given the workload.Comment: Accepted at DISC'14 - this is the full version with appendices, including more algorithm

Digging Deeper: Art Museums in Las Vegas?

[Excerpt] Las Vegas has been called the “city of reinvention” (Douglass and Raento 2003). Part of its more recent reinvention efforts has included the opening of five fine-art venues. However, one of the art museums––the Las Vegas Guggenheim––was shut down in its first year due to low attendance; another, the Bellagio Fine Art Gallery, has seen attendance dwindle (Schemeligian 2004). The question addressed here is whether the museums are bringing the intended intangible benefits to the host resort, or whether the sales and attendance figures represent overall disinterest. More broadly one considers the potential “fit” between sin-city and the high-art cultural world. The difficulty in addressing these issues is that tourists might not consciously recognize the value they feel about having a worldclass art museum onsite. Within nonprofit research there has been a call for ‘‘deeper understanding’’ of tourists (Thyne 2001) as reflected within the greater interest in new qualitative methodologies (Riley and Love 2000). The Zaltman metaphor elicitation technique, a patented research method, was chosen to investigate this research issue. Many of the world’s largest companies (such as Procter & Gamble) have utilized this method for insight on brand meaning and competitive positioning

Massively parallel support for a case-based planning system

Case-based planning (CBP), a kind of case-based reasoning, is a technique in which previously generated plans (cases) are stored in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over generative planning, in which a new plan is produced from scratch. CBP thus offers a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory to reduce retrieval times. This approach requires significant domain engineering and complex memory indexing schemes to make these planners efficient. In contrast, our CBP system, CaPER, uses a massively parallel frame-based AI language (PARKA) and can do extremely fast retrieval of complex cases from a large, unindexed memory. The ability to do fast, frequent retrievals has many advantages: indexing is unnecessary; very large case bases can be used; memory can be probed in numerous alternate ways; and queries can be made at several levels, allowing more specific retrieval of stored plans that better fit the target problem with less adaptation. In this paper we describe CaPER's case retrieval techniques and some experimental results showing its good performance, even on large case bases

Massively-parallel marker-passing in semantic networks

AbstractOne approach to using the information available in a semantic network is the use of marker-passing algorithms, which propagate information through the network to determine relationships between objects. One of the primary arguments in favor of these algorithms are their ability to be implemented in parallel. Despite this, most implementations have been serial and have only sometimes gone so far as to simulate parallelism. In this paper the marker-passing approach is presented. An actual parallel implementation which shows that such programs can be written on commercially available massively parallel machines is also presented

A Qualitative Analysis of Slot Clubs as Drivers of Casino Loyalty

The slot club is a very common type of loyalty program in the casino industry. In this research, the authors look at the deep meanings and emotions of a slot club for tourists and frequent local customers at a Las Vegas mega casino resort using an in-depth interview technique, the Zaltman Metaphor Elicitation Technique (ZMET). Results indicate that the opportunities for casino loyalty programs to establish emotional bonds with the customer lie in the capacity of reflecting human qualities in the slot club service delivery process, such as memory, creativity, and flexibility. The results also indicate that the slot club brings different meanings to different customer groups and that these emotional connections (or lack thereof) are best elucidated via this qualitative research technique that uses images, rather than words, to guide the in-depth interview. The authors discuss the implications of the results for loyalty programs as well as how this research technique might be used in other sectors of the hospitality industry

Semantic Integration Portal

The Semantic Integration Portal is a demonstration of the potential capabilities of Semantic Web applications in a knowledge-rich context. Source data is taken from different online terrorist incident aggregators and marked up according to ontologies specific to those domains. Unlike other semantic web techniques, which scrape the internet for raw data and then mark-up against a standard ontology, the approach here is to allow each data source to have its own domain-specific ontology. This allows the data producers the opportunity to mark up their data in their own way, producing RDF data according to their own ontologies without the need to conform to a standard. A variety of semantic integration techniques can then be applied to these ontologies, both automatic and interactive, allowing data from both sets to be viewed in a suitable application, in this case the mspace browser. Future iterations of the semantic integration portal aim to introduce more automated ontology-mapping techniques, aligning data from a variety of diverse sources with less need for human intervention

Semantic Transformation of Web Services

Web services have become the predominant paradigm for the development of distributed software systems. Web services provide the means to modularize software in a way that functionality can be described, discovered and deployed in a platform independent manner over a network (e.g., intranets, extranets and the Internet). The representation of web services by current industrial practice is predominantly syntactic in nature lacking the fundamental semantic underpinnings required to fulfill the goals of the emerging Semantic Web. This paper proposes a framework aimed at (1) modeling the semantics of syntactically defined web services through a process of interpretation, (2) scop-ing the derived concepts within domain ontologies, and (3) harmonizing the semantic web services with the domain ontologies. The framework was vali-dated through its application to web services developed for a large financial system. The worked example presented in this paper is extracted from the se-mantic modeling of these financial web services

Design Index for Deep Neural Networks

AbstractIn this paper, we propose a Deep Neural Networks (DNN) Design Index which would aid a DNN designer during the designing phase of DNNs. We study the designing aspect of DNNs from model-specific and data-specific perspectives with focus on three performance metrics: training time, training error and, validation error. We use a simple example to illustrate the significance of the DNN design index. To validate it, we calculate the design indices for four benchmark problems. This is an elementary work aimed at setting a direction for creating design indices pertaining to deep learning