3,592 research outputs found
SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App
Numerical simulations of fluids in astrophysics and computational fluid
dynamics (CFD) are among the most computationally-demanding calculations, in
terms of sustained floating-point operations per second, or FLOP/s. It is
expected that these numerical simulations will significantly benefit from the
future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The
performance of the SPH codes is, in general, adversely impacted by several
factors, such as multiple time-stepping, long-range interactions, and/or
boundary conditions. In this work an extensive study of three SPH
implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to
expose any limitations and characteristics of the codes. These codes are the
starting point of an interdisciplinary co-design project, SPH-EXA, for the
development of an Exascale-ready SPH mini-app. We implemented a rotating square
patch as a joint test simulation for the three SPH codes and analyzed their
performance on a modern HPC system, Piz Daint. The performance profiling and
scalability analysis conducted on the three parent codes allowed to expose
their performance issues, such as load imbalance, both in MPI and OpenMP.
Two-level load balancing has been successfully applied to SPHYNX to overcome
its load imbalance. The performance analysis shapes and drives the design of
the SPH-EXA mini-app towards the use of efficient parallelization methods,
fault-tolerance mechanisms, and load balancing approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1809.0801
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
Using migratable objects to enhance fault tolerance schemes in supercomputers
Supercomputers have seen an exponential increase in their size in the last two decades. Such a high growth rate is expected to take us to exascale in the timeframe 2018-2022. But, to bring a productive exascale environment about, it is necessary to focus on several key challenges. One of those challenges is fault tolerance. Machines at extreme scale will experience frequent failures and will require the system to avoid or overcome those failures. Various techniques have recently been developed to tolerate failures. The impact of these techniques and their scalability can be substantially enhanced by a parallel programming model called migratable objects. In this paper, we demonstrate how the migratable-objects model facilitates and improves several fault tolerance approaches. Our experimental results on thousands of cores suggest fault tolerance schemes based on migratable objects have low performance overhead and high scalability. Additionally, we present a performance model that predicts a significant benefit of using migratable objects to provide fault tolerance at extreme scale
Exploiting heterogeneity in Chip-Multiprocessor Design
In the past decade, semiconductor manufacturers are persistent in building faster and smaller transistors in order to boost the processor performance as projected by Moore’s Law. Recently, as we enter the deep submicron regime, continuing the same processor development pace becomes an increasingly difficult issue due to constraints on power, temperature, and the scalability of transistors. To overcome these challenges, researchers propose several innovations at both architecture and device levels that are able to partially solve the problems. These diversities in processor architecture and manufacturing materials provide solutions to continuing Moore’s Law by effectively exploiting the heterogeneity, however, they also introduce a set of unprecedented challenges that have been rarely addressed in prior works. In this dissertation, we present a series of in-depth studies to comprehensively investigate the design and optimization of future multi-core and many-core platforms through exploiting heteroge-neities. First, we explore a large design space of heterogeneous chip multiprocessors by exploiting the architectural- and device-level heterogeneities, aiming to identify the optimal design patterns leading to attractive energy- and cost-efficiencies in the pre-silicon stage. After this high-level study, we pay specific attention to the architectural asymmetry, aiming at developing a heterogeneity-aware task scheduler to optimize the energy-efficiency on a given single-ISA heterogeneous multi-processor. An advanced statistical tool is employed to facilitate the algorithm development. In the third study, we shift our concentration to the device-level heterogeneity and propose to effectively leverage the advantages provided by different materials to solve the increasingly important reliability issue for future processors
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