The LOFAR radio telescope creates Petabytes of data per year. This data is important for many scientific projects. The data needs to be efficiently processed within the timespan of these projects in order to maximize the scientific impact. We present a workflow orchestration system that integrates LOFAR processing with a distributed computing platform. The system is named Automated Grid-enabled LOFAR Workflows (AGLOW). AGLOW makes it fast and easy to develop, test and deploy complex LOFAR workflows, and to accelerate them on a distributed cluster architecture. AGLOW provides a significant reduction in time for setting up complex workflows: typically, from months to days. We lay out two case studies that process the data from the LOFAR Surveys Key Science Project. We have implemented these into the AGLOW environment. We also describe the capabilities of AGLOW, paving the way for use by other LOFAR science cases. In the future, AGLOW will automatically produce multiple science products from a single dataset, serving several of the LOFAR Key Science Projects.
Currently, we can parallelize a single processing step of the pipeline using the Grid LOFAR Tools (GRID https: github.com apmechev GRID_LRT ) @cite_12 . The LOFAR Surveys science cases incorporate multiple steps with inter-linked dependencies. Resolving these dependencies can be done efficiently by a comprehensive workflow orchestration software. The purpose of such software is to resolve dependencies between the multiple tasks in a workflow, execute these tasks, and track the status, logs, output, and runtime of each task.
@cite_12
1978924650 2161692763 2206856102 2011129635
MapReduce and similar systems significantly ease the task of writing data-parallel code. However, many real-world computations require a pipeline of MapReduces, and programming and managing such pipelines can be difficult. We present FlumeJava, a Java library that makes it easy to develop, test, and run efficient data-parallel pipelines. At the core of the FlumeJava library are a couple of classes that represent immutable parallel collections, each supporting a modest number of operations for processing them in parallel. Parallel collections and their operations present a simple, high-level, uniform abstraction over different data representations and execution strategies. To enable parallel operations to run efficiently, FlumeJava defers their evaluation, instead internally constructing an execution plan dataflow graph. When the final results of the parallel operations are eventually needed, FlumeJava first optimizes the execution plan, and then executes the optimized operations on appropriate underlying primitives (e.g., MapReduces). The combination of high-level abstractions for parallel data and computation, deferred evaluation and optimization, and efficient parallel primitives yields an easy-to-use system that approaches the efficiency of hand-optimized pipelines. FlumeJava is in active use by hundreds of pipeline developers within Google. Scientific experiments and large-scale simulations produce massive amounts of data. Many of these scientific datasets are arrays, and are stored in file formats such as HDF5 and NetCDF. Although scientific data management systems, such as SciDB, are designed to manipulate arrays, there are challenges in integrating these systems into existing analysis workflows. Major barriers include the expensive task of preparing and loading data before querying, and converting the final results to a format that is understood by the existing post-processing and visualization tools. As a consequence, integrating a data management system into an existing scientific data analysis workflow is time-consuming and requires extensive user involvement. In this paper, we present the design of a new scientific data analysis system that efficiently processes queries directly over data stored in the HDF5 file format. This design choice eliminates the tedious and error-prone data loading process, and makes the query results readily available to the next processing steps of the analysis workflow. Our design leverages the increasing main memory capacities found in supercomputers through bitmap indexing and in-memory query execution. In addition, query processing over the HDF5 data format can be effortlessly parallelized to utilize the ample concurrency available in large-scale supercomputers and modern parallel file systems. We evaluate the performance of our system on a large supercomputing system and experiment with both a synthetic dataset and a real cosmology observation dataset. Our system frequently outperforms the relational database system that the cosmology team currently uses, and is more than 10X faster than Hive when processing data in parallel. Overall, by eliminating the data loading step, our query processing system is more effective in supporting in situ scientific analysis workflows. High performance fast multipole method is crucial for the numerical simulation of many physical problems. In a previous study, we have shown that task-based fast multipole method provides the flexibility required to process a wide spectrum of particle distributions efficiently on multicore architectures. In this paper, we now show how such an approach can be extended to fully exploit heterogeneous platforms. For that, we design highly tuned graphics processing unit GPU versions of the two dominant operators P2P and M2L as well as a scheduling strategy that dynamically decides which proportion of subsequent tasks is processed on regular CPU cores and on GPU accelerators. We assess our method with the StarPU runtime system for executing the resulting task flow on an Intel X5650 Nehalem multicore processor possibly enhanced with one, two, or three Nvidia Fermi M2070 or M2090 GPUs Santa Clara, CA, USA. A detailed experimental study on two 30 million particle distributions a cube and an ellipsoid shows that the resulting software consistently achieves high performance across architectures. Copyright © 2015 John Wiley & Sons, Ltd. Science applications are accumulating an ever-increasing amount of multidimensional data. Although some of it can be processed in a relational database, much of it is better suited to array-based engines. As such, it is important to optimize the query processing of these systems. This paper focuses on efficient query processing of join operations within an array database. These engines invariably chunk'' their data into multidimensional tiles that they use to efficiently process spatial queries. As such, traditional relational algorithms need to be substantially modified to take advantage of array tiles. Moreover, most n-dimensional science data is unevenly distributed in array space because its underlying observations rarely follow a uniform pattern. It is crucial that the optimization of array joins be skew-aware. In addition, owing to the scale of science applications, their query processing usually spans multiple nodes. This further complicates the planning of array joins. In this paper, we introduce a join optimization framework that is skew-aware for distributed joins. This optimization consists of two phases. In the first, a logical planner selects the query's algorithm (e.g., merge join), the granularity of the its tiles, and the reorganization operations needed to align the data. The second phase implements this logical plan by assigning tiles to cluster nodes using an analytical cost model. Our experimental results, on both synthetic and real-world data, demonstrate that this optimization framework speeds up array joins by up to 2.5X in comparison to the baseline.