285 research outputs found
Hierarchical Dynamic Loop Self-Scheduling on Distributed-Memory Systems Using an MPI+MPI Approach
Computationally-intensive loops are the primary source of parallelism in
scientific applications. Such loops are often irregular and a balanced
execution of their loop iterations is critical for achieving high performance.
However, several factors may lead to an imbalanced load execution, such as
problem characteristics, algorithmic, and systemic variations. Dynamic loop
self-scheduling (DLS) techniques are devised to mitigate these factors, and
consequently, improve application performance. On distributed-memory systems,
DLS techniques can be implemented using a hierarchical master-worker execution
model and are, therefore, called hierarchical DLS techniques. These techniques
self-schedule loop iterations at two levels of hardware parallelism: across and
within compute nodes. Hybrid programming approaches that combine the message
passing interface (MPI) with open multi-processing (OpenMP) dominate the
implementation of hierarchical DLS techniques. The MPI-3 standard includes the
feature of sharing memory regions among MPI processes. This feature introduced
the MPI+MPI approach that simplifies the implementation of parallel scientific
applications. The present work designs and implements hierarchical DLS
techniques by exploiting the MPI+MPI approach. Four well-known DLS techniques
are considered in the evaluation proposed herein. The results indicate certain
performance advantages of the proposed approach compared to the hybrid
MPI+OpenMP approach
An Approach for Realistically Simulating the Performance of Scientific Applications on High Performance Computing Systems
Scientific applications often contain large, computationally-intensive, and
irregular parallel loops or tasks that exhibit stochastic characteristics.
Applications may suffer from load imbalance during their execution on
high-performance computing (HPC) systems due to such characteristics. Dynamic
loop self-scheduling (DLS) techniques are instrumental in improving the
performance of scientific applications on HPC systems via load balancing.
Selecting a DLS technique that results in the best performance for different
problems and system sizes requires a large number of exploratory experiments. A
theoretical model that can be used to predict the scheduling technique that
yields the best performance for a given problem and system has not yet been
identified. Therefore, simulation is the most appropriate approach for
conducting such exploratory experiments with reasonable costs. This work
devises an approach to realistically simulate computationally-intensive
scientific applications that employ DLS and execute on HPC systems. Several
approaches to represent the application tasks (or loop iterations) are compared
to establish their influence on the simulative application performance. A novel
simulation strategy is introduced, which transforms a native application code
into a simulative code. The native and simulative performance of two
computationally-intensive scientific applications are compared to evaluate the
realism of the proposed simulation approach. The comparison of the performance
characteristics extracted from the native and simulative performance shows that
the proposed simulation approach fully captured most of the performance
characteristics of interest. This work shows and establishes the importance of
simulations that realistically predict the performance of DLS techniques for
different applications and system configurations
Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale
The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian
method, used in numerical simulations of fluids in astrophysics and
computational fluid dynamics, among many other fields. SPH simulations with
detailed physics represent computationally-demanding calculations. The
parallelization of SPH codes is not trivial due to the absence of a structured
grid. Additionally, the performance of the SPH codes can be, in general,
adversely impacted by several factors, such as multiple time-stepping,
long-range interactions, and/or boundary conditions. This work presents
insights into the current performance and functionalities of three SPH codes:
SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an
interdisciplinary co-design project, SPH-EXA, for the development of an
Exascale-ready SPH mini-app. To gain such insights, a rotating square patch
test was implemented as a common test simulation for the three SPH codes and
analyzed on two modern HPC systems. Furthermore, to stress the differences with
the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an
additional test case, the Evrard collapse, has also been carried out. This work
extrapolates the common basic SPH features in the three codes for the purpose
of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app.
Moreover, the outcome of this serves as direct feedback to the parent codes, to
improve their performance and overall scalability.Comment: 18 pages, 4 figures, 5 tables, 2018 IEEE International Conference on
Cluster Computing proceedings for WRAp1
Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra
Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries
Recommended from our members
Weighted locality-sensitive scheduling for mitigating noise on multi-core clusters
- …