1,012 research outputs found
A Parallel Monte Carlo Code for Simulating Collisional N-body Systems
We present a new parallel code for computing the dynamical evolution of
collisional N-body systems with up to N~10^7 particles. Our code is based on
the the Henon Monte Carlo method for solving the Fokker-Planck equation, and
makes assumptions of spherical symmetry and dynamical equilibrium. The
principal algorithmic developments involve optimizing data structures, and the
introduction of a parallel random number generation scheme, as well as a
parallel sorting algorithm, required to find nearest neighbors for interactions
and to compute the gravitational potential. The new algorithms we introduce
along with our choice of decomposition scheme minimize communication costs and
ensure optimal distribution of data and workload among the processing units.
The implementation uses the Message Passing Interface (MPI) library for
communication, which makes it portable to many different supercomputing
architectures. We validate the code by calculating the evolution of clusters
with initial Plummer distribution functions up to core collapse with the number
of stars, N, spanning three orders of magnitude, from 10^5 to 10^7. We find
that our results are in good agreement with self-similar core-collapse
solutions, and the core collapse times generally agree with expectations from
the literature. Also, we observe good total energy conservation, within less
than 0.04% throughout all simulations. We analyze the performance of the code,
and demonstrate near-linear scaling of the runtime with the number of
processors up to 64 processors for N=10^5, 128 for N=10^6 and 256 for N=10^7.
The runtime reaches a saturation with the addition of more processors beyond
these limits which is a characteristic of the parallel sorting algorithm. The
resulting maximum speedups we achieve are approximately 60x, 100x, and 220x,
respectively.Comment: 53 pages, 13 figures, accepted for publication in ApJ Supplement
Optimizing Irregular Communication with Neighborhood Collectives and Locality-Aware Parallelism
Irregular communication often limits both the performance and scalability of
parallel applications. Typically, applications individually implement irregular
messages using point-to-point communications, and any optimizations are added
directly into the application. As a result, these optimizations lack
portability. There is no easy way to optimize point-to-point messages within
MPI, as the interface for single messages provides no information on the
collection of all communication to be performed. However, the persistent
neighbor collective API, released in the MPI 4 standard, provides an interface
for portable optimizations of irregular communication within MPI libraries.
This paper presents methods for optimizing irregular communication within
neighborhood collectives, analyzes the impact of replacing point-to-point
communication in existing codebases such as Hypre BoomerAMG with neighborhood
collectives, and finally shows an up to 1.32x speedup on sparse matrix-vector
multiplication within a BoomerAMG solve through the use of our optimized
neighbor collectives. The authors analyze multiple implementations of
neighborhood collectives, including a standard implementation, which simply
wraps standard point-to-point communication, as well as multiple
implementations of locality-aware aggregation. All optimizations are available
in an open-source codebase, MPI Advance, which sits on top of MPI, allowing for
optimizations to be added into existing codebases regardless of the system MPI
install
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
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
A load-sharing architecture for high performance optimistic simulations on multi-core machines
In Parallel Discrete Event Simulation (PDES), the simulation model is partitioned into a set of distinct Logical Processes (LPs) which are allowed to concurrently execute simulation events. In this work we present an innovative approach to load-sharing on multi-core/multiprocessor machines, targeted at the optimistic PDES paradigm, where LPs are speculatively allowed to process simulation events with no preventive verification of causal consistency, and actual consistency violations (if any) are recovered via rollback techniques. In our approach, each simulation kernel instance, in charge of hosting and executing a specific set of LPs, runs a set of worker threads, which can be dynamically activated/deactivated on the basis of a distributed algorithm. The latter relies in turn on an analytical model that provides indications on how to reassign processor/core usage across the kernels in order to handle the simulation workload as efficiently as possible. We also present a real implementation of our load-sharing architecture within the ROme OpTimistic Simulator (ROOT-Sim), namely an open-source C-based simulation platform implemented according to the PDES paradigm and the optimistic synchronization approach. Experimental results for an assessment of the validity of our proposal are presented as well
Improving Parallel I/O Performance Using Interval I/O
Today\u27s most advanced scientific applications run on large clusters consisting of hundreds of thousands of processing cores, access state of the art parallel file systems that allow files to be distributed across hundreds of storage targets, and utilize advanced interconnections systems that allow for theoretical I/O bandwidth of hundreds of gigabytes per second. Despite these advanced technologies, these applications often fail to obtain a reasonable proportion of available I/O bandwidth. The reasons for the poor performance of application I/O include the noncontiguous I/O access patterns used for scientific computing, contention due to false sharing, and the somewhat finicky nature of parallel file system performance. We argue that a more fundamental cause of this problem is the legacy view of a file as a linear sequence of bytes. To address these issues, we introduce a novel approach for parallel I/O called Interval I/O. Interval I/O is an innovative approach that uses application access patterns to partition a file into a series of intervals, which are used as the fundamental unit for subsequent I/O operations. Use of this approach provides superior performance for the noncontiguous access patterns which are frequently used by scientific applications. In addition, the approach reduces false contention and the unnecessary serialization it causes. Interval I/O also significantly increases the performance of atomic mode operations. Finally, the Interval I/O approach includes a technique for supporting parallel I/O for cooperating applications. We provide a prototype implementation of our Interval I/O system and use it to demonstrate performance improvements of as much as 1000% compared to ROMIO when using Interval I/O with several common benchmarks
Power efficient job scheduling by predicting the impact of processor manufacturing variability
Modern CPUs suffer from performance and power consumption variability due to the manufacturing process. As a result, systems that do not consider such variability caused by manufacturing issues lead to performance degradations and wasted power. In order to avoid such negative impact, users and system administrators must actively counteract any manufacturing variability.
In this work we show that parallel systems benefit from taking into account the consequences of manufacturing variability when making scheduling decisions at the job scheduler level. We also show that it is possible to predict the impact of this variability on specific applications by using variability-aware power prediction models. Based on these power models, we propose two job scheduling policies that consider the effects of manufacturing variability for each application and that ensure that power consumption stays under a system-wide power budget. We evaluate our policies under different power budgets and traffic scenarios, consisting of both single- and multi-node parallel applications, utilizing up to 4096 cores in total. We demonstrate that they decrease job turnaround time, compared to contemporary scheduling policies used on production clusters, up to 31% while saving up to 5.5% energy.Postprint (author's final draft
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