The vertex-centric programming model is an established computational paradigm recently incorporated into distributed processing frameworks to address challenges in large-scale graph processing. Billion-node graphs that exceed the memory capacity of commodity machines are not well supported by popular Big Data tools like MapReduce, which are notoriously poor performing for iterative graph algorithms such as PageRank. In response, a new type of framework challenges one to “think like a vertex” (TLAV) and implements user-defined programs from the perspective of a vertex rather than a graph. Such an approach improves locality, demonstrates linear scalability, and provides a natural way to express and compute many iterative graph algorithms. These frameworks are simple to program and widely applicable but, like an operating system, are composed of several intricate, interdependent components, of which a thorough understanding is necessary in order to elicit top performance at scale. To this end, the first comprehensive survey of TLAV frameworks is presented. In this survey, the vertex-centric approach to graph processing is overviewed, TLAV frameworks are deconstructed into four main components and respectively analyzed, and TLAV implementations are reviewed and categorized.

https://dl.acm.org/doi/pdf/10.1145/2818185

Thinking Like a Vertex: A Survey of Vertex-Centric Frameworks for Large-Scale Distributed Graph Processing

Mit seiner Publikation “One Size Fits All: An Idea Whose Time Has Come and Gone?“ [St05] hat Michael Stonebraker 2005 die Datenbankszene provoziert. Er vertritt dort die These, dass die eingeführten und in vielen Kontexten eingesetzten „traditionellen“ Datenbanksysteme zwar für viele datenzentrische Anwendungen eingesetzt werden, aber keineswegs die optimalen Systeme für viele dieser Anwendungen (OLAP, Stream Processing, XML Datenbanken, wissenschaftliche Datenbanken, Textsuche) sind. In einer späteren Publikation [St07] stellte er sogar die These auf, dass es keine Anwendungen gibt, für die die traditionellen Datenbanksysteme die beste Alternative sind. Er sagte voraus, dass sich in der kommerziellen Welt eine Anzahl unabhängiger spezialisierter „neuer“ Datenbanksysteme aufsplitten werde, die bestenfalls durch ein gemeinsames Frontend verbunden sind.

"One Size Fits All": An Idea Whose Time Has Come and Gone?

In this paper we argue for using a "Game First" approach to teaching introductory programming. We believe that concerns over whether an OO approach or a procedural approach should be used first are secondary to the course assignment and example content. If examples are not compelling, student interest often lags thus making the OO versus procedural argument moot. We believe that game programming motivates most new programmers. Compelling assignments mean that students are far more likely to learn because they are interested, and the visual component allows students to see mistakes in their code as manifested in the resultant graphics. We describe our experiences after redesigning and offering a new introductory computer science sequence using 2D game development as a unifying theme. We teach fundamental programming concepts via two dimensional game development in Flash and ActionScript during the first quarter, transition to C++ to solidify concepts and add pointers during the second quarter, then teach a multi-phase project based game approach using C++ and openGL (2D graphics only) during the third quarter. Our surveys show that this approach improved student understanding of all seven basic topics examined.

/pdf/a-games-first-approach-to-teaching-introductory-programming-27v1qgo5oi.pdf

A games first approach to teaching introductory programming

2012 [AS13b, Wal12a]. 2013 [Alv13, Bar13, Cam13, Car13, CCS13, Cut13, Edi14, Swe13]. 2014 [CDS13, DN14, ND14, Rod14, Tym14a]. 2015 [DeL14a, DeL14b, She15b]. 2016 [AT16a, CCV16, CCV17, MR15, SDCT16a, SDCT16c, She16a]. 2017 [DG17, EC17, Fit18b, MJ18, TCSM17, TCM17]. 2018 [BG18b, DD17, Dec19b, Gol18, JI18, MKMP18, MMK18, PQH17, She18]. 2019 [Cut19, FI19, HPQ19, MP19a, MPRM19, MP19b, MS19, Rod18, She19a]. 2020 [DH19].

/pdf/a-complete-bibliography-of-publications-in-sigcse-bulletin-1aolnsl5m4.pdf

A Complete Bibliography of Publications in SIGCSE Bulletin: 2000{2009

The Breadth-First Search (BFS) algorithm serves as the foundation for many graph-processing applications and analytics workloads. While Graphics Processing Unit (GPU) offers massive parallelism, achieving high-performance BFS on GPUs entails efficient scheduling of a large number of GPU threads and effective utilization of GPU memory hierarchy. In this paper, we present Enterprise, a new GPU-based BFS system that combines three techniques to remove potential performance bottlenecks: (1) streamlined GPU threads scheduling through constructing a frontier queue without contention from concurrent threads, yet containing no duplicated frontiers and optimized for both top-down and bottom-up BFS. (2) GPU workload balancing that classifies the frontiers based on different out-degrees to utilize the full spectrum of GPU parallel granularity, which significantly increases thread-level parallelism; and (3) GPU based BFS direction optimization quantifies the effect of hub vertices on direction-switching and selectively caches a small set of critical hub vertices in the limited GPU shared memory to reduce expensive random data accesses. We have evaluated Enterprise on a large variety of graphs with different GPU devices. Enterprise achieves up to 76 billion traversed edges per second (TEPS) on a single NVIDIA Kepler K40, and up to 122 billion TEPS on two GPUs that ranks No. 45 in the Graph 500 on November 2014. Enterprise is also very energy-efficient as No. 1 in the GreenGraph 500 (small data category), delivering 446 million TEPS per watt.

https://dl.acm.org/doi/pdf/10.1145/2807591.2807594

Enterprise: breadth-first graph traversal on GPUs

From sensor networks to transportation infrastructure to social networks, we are awash in data. Many of these real-world networks tend to be large ("big data") and dynamic, evolving over time. Their evolution can be modeled as a series of graphs. Traditional systems that store and analyze one graph at a time cannot effectively handle the complexity and subtlety inherent in dynamic graphs. Modern analytics require systems capable of storing and processing series of graphs. We present such a system. G* compresses dynamic graph data based on commonalities among the graphs in the series for deduplicated storage on multiple servers. In addition to the obvious space-saving advantage, large-scale graph processing tends to be I/O bound, so faster reads from and writes to stable storage enable faster results. Unlike traditional database and graph processing systems, G* executes complex queries on large graphs using distributed operators to process graph data in parallel. It speeds up queries on multiple graphs by processing graph commonalities only once and sharing the results across relevant graphs. This architecture not only provides scalability, but since G* is not limited to processing only what is available in RAM, its analysis capabilities are far greater than other systems which are limited to what they can hold in memory. This paper presents G*'s design and implementation principles along with evaluation results that document its unique benefits over traditional graph processing systems.

The G* graph database: efficiently managing large distributed dynamic graphs

This paper describes initiatives at Marist College to develop a Game Concentration in the undergraduate Computer Science curriculum. These initiatives contemplate recommendations for existing courses as well as adoption of new courses. We also consider activities of the Association of Computing Machinery (ACM) in this area and opportunities for students beyond the classroom.

Game design & programming concentration within the computer science curriculum

Many of today's applications can benefit from the discovery of the most central entities in real-world networks. This paper presents a new technique that efficiently finds the k most central entities in terms of closeness centrality. Instead of computing the centrality of each entity independently, our technique shares intermediate results between centrality computations. Since the cost of each centrality computation may vary substantially depending on the choice of the previous computation, our technique schedules centrality computations in a manner that minimizes the estimated completion time. This technique also updates, with negligible overhead, an upper bound on the centrality of every entity. Using this information, our technique proactively skips entities that cannot belong to the final answer. This paper presents evaluation results for actual networks to demonstrate the benefits of our technique.

Efficient top-k closeness centrality search

Most real-world networks evolve over time. This evolution can be modeled as a series of graphs that represent a network at different points in time. Our G* system enables efficient storage and querying of these graph snapshots by taking advantage of the commonalities among them. We are extending G* for highly scalable and robust operation. This paper shows that the classic challenges of data distribution and replication are imbued with renewed significance given continuously generated graph snapshots. Our data distribution technique adjusts the set of worker servers for storing each graph snapshot in a manner optimized for popular queries. Our data replication approach maintains each snapshot replica on a different number of workers, making available the most efficient replica configurations for different types of queries.

/pdf/scalable-and-robust-management-of-dynamic-graph-data-256cgrw59i.pdf

Scalable and Robust Management of Dynamic Graph Data.

The world is full of evolving networks, many of which can be represented by a series of large graphs Neither the current graph processing systems nor database systems can efficiently store and query these graphs due to their lack of support for managing multiple graphs and lack of essential graph querying capabilities We propose to demonstrate our system, G*, that meets the new challenges of managing multiple graphs and supporting fundamental graph querying capabilities G* can store graphs on a large number of servers while compressing these graphs based on their commonalities G* also allows users to easily express queries on graphs and efficiently executes these queries by sharing computations across graphs During our demonstrations, conference attendees will run various analytic queries on large, practical data sets These demonstrations will highlight the convenience and performance benefits of G* over existing database and graph processing systems, the effectiveness of sharing in graph data storage and processing, as well as G*'s scalability

Alan G. Labouseur

Papers

The G* graph database: efficiently managing large distributed dynamic graphs

Game design & programming concentration within the computer science curriculum

Efficient top-k closeness centrality search

Scalable and Robust Management of Dynamic Graph Data.

A demonstration of the G∗ graph database system