36,091 research outputs found
Task-based Runtime Optimizations Towards High Performance Computing Applications
The last decades have witnessed a rapid improvement of computational capabilities in high-performance computing (HPC) platforms thanks to hardware technology scaling. HPC architectures benefit from mainstream advances on the hardware with many-core systems, deep hierarchical memory subsystem, non-uniform memory access, and an ever-increasing gap between computational power and memory bandwidth. This has necessitated continuous adaptations across the software stack to maintain high hardware utilization. In this HPC landscape of potentially million-way parallelism, task-based programming models associated with dynamic runtime systems are becoming more popular, which fosters developers’ productivity at extreme scale by abstracting the underlying hardware complexity.
In this context, this dissertation highlights how a software bundle powered by a task-based programming model can address the heterogeneous workloads engendered by HPC applications., i.e., data redistribution, geospatial modeling and 3D unstructured mesh deformation here. Data redistribution aims to reshuffle data to optimize some objective for an algorithm, whose objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal of increasing the efficiency and therefore reducing the time-to-solution for the algorithm. Geostatistical modeling, one of the prime motivating applications for exascale computing, is a technique for predicting desired quantities from geographically distributed data, based on statistical models and optimization of parameters. Meshing the deformable contour of moving 3D bodies is an expensive operation that can cause huge computational challenges in fluid-structure interaction (FSI) applications. Therefore, in this dissertation, Redistribute-PaRSEC, ExaGeoStat-PaRSEC and HiCMA-PaRSEC are proposed to efficiently tackle these HPC applications respectively at extreme scale, and they are evaluated on multiple HPC clusters, including AMD-based, Intel-based, Arm-based CPU systems and IBM-based multi-GPU system. This multidisciplinary work emphasizes the need for runtime systems to go beyond their primary responsibility of task scheduling on massively parallel hardware system for servicing the next-generation scientific applications
Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks
Recently, due to rapid development of information and communication
technologies, the data are created and consumed in the avalanche way.
Distributed computing create preconditions for analyzing and processing such
Big Data by distributing the computations among a number of compute nodes. In
this work, performance of distributed computing environments on the basis of
Hadoop and Spark frameworks is estimated for real and virtual versions of
clusters. As a test task, we chose the classic use case of word counting in
texts of various sizes. It was found that the running times grow very fast with
the dataset size and faster than a power function even. As to the real and
virtual versions of cluster implementations, this tendency is the similar for
both Hadoop and Spark frameworks. Moreover, speedup values decrease
significantly with the growth of dataset size, especially for virtual version
of cluster configuration. The problem of growing data generated by IoT and
multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye
tracking, etc.) interaction channels is presented. In the context of this
problem, the current observations as to the running times and speedup on Hadoop
and Spark frameworks in real and virtual cluster configurations can be very
useful for the proper scaling-up and efficient job management, especially for
machine learning and Deep Learning applications, where Big Data are widely
present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on
Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine
Exploring collaboration patterns among global software development teams.
This study examines communication behaviors in
global software student teams. The authors of the
paper characterize the types of communication
behaviors that occur when student teams are engaged
in a software development project. The authors
present findings from a one-semester study that
examined factors contributing to successful distributed
programming interactions among students enrolled at
the University of Atilim (Turkey), Universidad
Tecnológica de Panamá, University of North Texas,
and Middlesex University (UK). Using content and
cluster analyses techniques, we identified distinct
patterns of collaboration and examined how these
patterns were associated with task, culture, GPA, and
performance of collaborative teams. Our results
suggest that communication patterns among global
software learners may be related to task type, culture
and GPA. It is hoped that these findings will lead to the
development of new strategies for improving
communication among global software teams
PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies
The current landscape of scientific research is widely based on modeling and
simulation, typically with complexity in the simulation's flow of execution and
parameterization properties. Execution flows are not necessarily
straightforward since they may need multiple processing tasks and iterations.
Furthermore, parameter and performance studies are common approaches used to
characterize a simulation, often requiring traversal of a large parameter
space. High-performance computers offer practical resources at the expense of
users handling the setup, submission, and management of jobs. This work
presents the design of PaPaS, a portable, lightweight, and generic workflow
framework for conducting parallel parameter and performance studies. Workflows
are defined using parameter files based on keyword-value pairs syntax, thus
removing from the user the overhead of creating complex scripts to manage the
workflow. A parameter set consists of any combination of environment variables,
files, partial file contents, and command line arguments. PaPaS is being
developed in Python 3 with support for distributed parallelization using SSH,
batch systems, and C++ MPI. The PaPaS framework will run as user processes, and
can be used in single/multi-node and multi-tenant computing systems. An example
simulation using the BehaviorSpace tool from NetLogo and a matrix multiply
using OpenMP are presented as parameter and performance studies, respectively.
The results demonstrate that the PaPaS framework offers a simple method for
defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
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