Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance

Hadoop Image Processing Framework

With the rapid growth of social media, the number of images being uploaded to the internet is exploding. Massive quantities of images are shared through multi-platform services such as Snapchat, Instagram, Facebook and WhatsApp; recent studies estimate that over 1.8 billion photos are uploaded every day. However, for the most part, applications that make use of this vast data have yet to emerge. Most current image processing applications, designed for small-scale, local computation, do not scale well to web-sized problems with their large requirements for computational resources and storage. The emergence of processing frameworks such as the Hadoop MapReduce\cite{dean2008} platform addresses the problem of providing a system for computationally intensive data processing and distributed storage. However, to learn the technical complexities of developing useful applications using Hadoop requires a large investment of time and experience on the part of the developer. As such, the pool of researchers and programmers with the varied skills to develop applications that can use large sets of images has been limited. To address this we have developed the Hadoop Image Processing Framework, which provides a Hadoop-based library to support large-scale image processing. The main aim of the framework is to allow developers of image processing applications to leverage the Hadoop MapReduce framework without having to master its technical details and introduce an additional source of complexity and error into their programs.Computer Scienc

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images

A new algorithm to split and merge ultra-high resolution 3D images

Splitting and merging ultra-high resolution 3D images is a requirement for parallel or distributed processing operations. Naive algorithms to split and merge 3D blocks from ultra-high resolution images perform very poorly, due to the number of seeks required to reconstruct spatially-adjacent blocks from linear data organizations on disk. The current solution to deal with this problem is to use file formats that preserve spatial proximity on disk, but this comes with additional complexity. We introduce a new algorithm called Multiple reads/writes to split and merge ultra-high resolution 3D images efficiently from simple file formats. Multiple reads/writes only access contiguous bytes in the reconstructed image, which leads to substantial performance improvements compared to existing algorithms. We parallelize our algorithm using multi-threading, which further improves the performance for data stored on a Hadoop cluster. We also show that on-the-fly lossless compression with the lz4 algorithm reduces the split and merge time further

Performance Evaluation of Job Scheduling and Resource Allocation in Apache Spark

Advancements in data acquisition techniques and devices are revolutionizing the way image data are collected, managed and processed. Devices such as time-lapse cameras and multispectral cameras generate large amount of image data daily. Therefore, there is a clear need for many organizations and researchers to deal with large volume of image data efficiently. On the other hand, Big Data processing on distributed systems such as Apache Spark are gaining popularity in recent years. Apache Spark is a widely used in-memory framework for distributed processing of large datasets on a cluster of inexpensive computers. This thesis proposes using Spark for distributed processing of large amount of image data in a time efficient manner. However, to share cluster resources efficiently, multiple image processing applications submitted to the cluster must be appropriately scheduled by Spark cluster managers to take advantage of all the compute power and storage capacity of the cluster. Spark can run on three cluster managers including Standalone, Mesos and YARN, and provides several configuration parameters that control how resources are allocated and scheduled. Using default settings for these multiple parameters is not enough to efficiently share cluster resources between multiple applications running concurrently. This leads to performance issues and resource underutilization because cluster administrators and users do not know which Spark cluster manager is the right fit for their applications and how the scheduling behaviour and parameter settings of these cluster managers affect the performance of their applications in terms of resource utilization and response times. This thesis parallelized a set of heterogeneous image processing applications including Image Registration, Flower Counter and Image Clustering, and presents extensive comparisons and analyses of running these applications on a large server and a Spark cluster using three different cluster managers for resource allocation, including Standalone, Apache Mesos and Hodoop YARN. In addition, the thesis examined the two different job scheduling and resource allocations modes available in Spark: static and dynamic allocation. Furthermore, the thesis explored the various configurations available on both modes that control speculative execution of tasks, resource size and the number of parallel tasks per job, and explained their impact on image processing applications. The thesis aims to show that using optimal values for these parameters reduces jobs makespan, maximizes cluster utilization, and ensures each application is allocated a fair share of cluster resources in a timely manner

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images