37,837 research outputs found
Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
There is a growing need for distributed graph processing systems that are
capable of gracefully scaling to very large graph datasets. Unfortunately, this
challenge has not been easily met due to the intense memory pressure imposed by
process-centric, message passing designs that many graph processing systems
follow. Pregelix is a new open source distributed graph processing system that
is based on an iterative dataflow design that is better tuned to handle both
in-memory and out-of-core workloads. As such, Pregelix offers improved
performance characteristics and scaling properties over current open source
systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up
to 35x speedup compared to distributed GraphLab), and makes more effective use
of available machine resources to support Big(ger) Graph Analytics
MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data
In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms
Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops
Jupyter Notebooks have become a mainstream tool for interactive computing in
every field of science. Jupyter Notebooks are suitable as companion
applications for Science Gateways, providing more flexibility and
post-processing capability to the users. Moreover they are often used in
training events and workshops to provide immediate access to a pre-configured
interactive computing environment. The Jupyter team released the JupyterHub web
application to provide a platform where multiple users can login and access a
Jupyter Notebook environment. When the number of users and memory requirements
are low, it is easy to setup JupyterHub on a single server. However, setup
becomes more complicated when we need to serve Jupyter Notebooks at scale to
tens or hundreds of users. In this paper we will present three strategies for
deploying JupyterHub at scale on XSEDE resources. All options share the
deployment of JupyterHub on a Virtual Machine on XSEDE Jetstream. In the first
scenario, JupyterHub connects to a supercomputer and launches a single node job
on behalf of each user and proxies back the Notebook from the computing node
back to the user's browser. In the second scenario, implemented in the context
of a XSEDE consultation for the IRIS consortium for Seismology, we deploy
Docker in Swarm mode to coordinate many XSEDE Jetstream virtual machines to
provide Notebooks with persistent storage and quota. In the last scenario we
install the Kubernetes containers orchestration framework on Jetstream to
provide a fault-tolerant JupyterHub deployment with a distributed filesystem
and capability to scale to thousands of users. In the conclusion section we
provide a link to step-by-step tutorials complete with all the necessary
commands and configuration files to replicate these deployments.Comment: 7 pages, 3 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
Tupleware: Redefining Modern Analytics
There is a fundamental discrepancy between the targeted and actual users of
current analytics frameworks. Most systems are designed for the data and
infrastructure of the Googles and Facebooks of the world---petabytes of data
distributed across large cloud deployments consisting of thousands of cheap
commodity machines. Yet, the vast majority of users operate clusters ranging
from a few to a few dozen nodes, analyze relatively small datasets of up to a
few terabytes, and perform primarily compute-intensive operations. Targeting
these users fundamentally changes the way we should build analytics systems.
This paper describes the design of Tupleware, a new system specifically aimed
at the challenges faced by the typical user. Tupleware's architecture brings
together ideas from the database, compiler, and programming languages
communities to create a powerful end-to-end solution for data analysis. We
propose novel techniques that consider the data, computations, and hardware
together to achieve maximum performance on a case-by-case basis. Our
experimental evaluation quantifies the impact of our novel techniques and shows
orders of magnitude performance improvement over alternative systems
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
A consistency framework for dynamic reconfiguration in AO-middleware architectures
Aspect-oriented (AO) middleware is a promising technology for the realisation of dynamic reconfiguration in distributed systems. Similar to other dynamic reconfiguration approaches, AO-middleware based reconfiguration requires that the consistency of the system is maintained across reconfigurations. AO middleware based reconfiguration is an ongoing research topic and several consistency approaches have been proposed. However, most of these approaches tend to be targeted at specific narrow contexts, whereas for heterogeneous distributed systems it is crucial to cover a wide range of operating conditions. In this paper we address this problem by exploring a flexible, framework-based consistency management approach that cover a wide range of operating conditions ensuring distributed dynamic reconfiguration in a consistent manner for AO-middleware architectures
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