Spark (mathematics)

About: Spark (mathematics) is a research topic. Over the lifetime, 7304 publications have been published within this topic receiving 63322 citations.

Spark - A generic simulator for physical multi-agent simulations.

Abstract: In this paper we describe a new multi-agent simulation system, called Spark, for physical agents in three-dimensional environments. Our goal in creating Spark was to provide a great amount of flexibility for creating new types of agents and simulations. To achieve this, we implemented a flexible application framework and exhausted the idea of replaceable components in the resulting system. In comparison to specialized simulators, users can effortlessly create new simulations by using a scene description language. Spark is a powerful and flexible tool to state different multi-agent research questions. It is used as official simulator for the first three-dimensional RoboCup Simulation League competition. We present the concepts we used to achieve the flexibility in our system and show how we seamlessly integrated the different subsystems into one user-friendly framework.

Spark-DIY: A Framework for Interoperable Spark Operations with High Performance Block-Based Data Models

Abstract: Today's scientific applications are increasingly relying on a variety of data sources, storage facilities, and computing infrastructures, and there is a growing demand for data analysis and visualization for these applications. In this context, exploiting Big Data frameworks for scientific computing is an opportunity to incorporate high-level libraries, platforms, and algorithms for machine learning, graph processing, and streaming; inherit their data awareness and fault-tolerance; and increase productivity. Nevertheless, limitations exist when Big Data platforms are integrated with an HPC environment, namely poor scalability, severe memory overhead, and huge development effort. This paper focuses on a popular Big Data framework -Apache Spark- and proposes an architecture to support the integration of highly scalable MPI block-based data models and communication patterns with a map-reduce-based programming model. The resulting platform preserves the data abstraction and programming interface of Spark, without conducting any changes in the framework, but allows the user to delegate operations to the MPI layer. The evaluation of our prototype shows that our approach integrates Spark and MPI efficiently at scale, so end users can take advantage of the productivity facilitated by the rich ecosystem of high-level Big Data tools and libraries based on Spark, without compromising efficiency and scalability.

Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark

Abstract: Collaborative filtering algorithms are important building blocks in many practical recommendation systems. For example, many large-scale data processing environments include collaborative filtering models for which the Alternating Least Squares (ALS) algorithm is used to compute latent factor matrix decompositions. In this paper, we propose an approach to accelerate the convergence of parallel ALS-based optimization methods for collaborative filtering using a nonlinear conjugate gradient (NCG) wrapper around the ALS iterations. We also provide a parallel implementation of the accelerated ALS-NCG algorithm in the Apache Spark distributed data processing environment, and an efficient line search technique as part of the ALS-NCG implementation that requires only one pass over the data on distributed datasets. In serial numerical experiments on a linux workstation and parallel numerical experiments on a 16 node cluster with 256 computing cores, we demonstrate that the combined ALS-NCG method requires many fewer iterations and less time than standalone ALS to reach movie rankings with high accuracy on the MovieLens 20M dataset. In parallel, ALS-NCG can achieve an acceleration factor of 4 or greater in clock time when an accurate solution is desired; furthermore, the acceleration factor increases as greater numerical precision is required in the solution. Furthermore, the NCG acceleration mechanism is efficient in parallel and scales linearly with problem size on synthetic datasets with up to nearly 1 billion ratings. The acceleration mechanism is general and may also be applicable to other optimization methods for collaborative filtering.