45,016 research outputs found
Evaluating the Potential of Disaggregated Memory Systems for HPC applications
Disaggregated memory is a promising approach that addresses the limitations
of traditional memory architectures by enabling memory to be decoupled from
compute nodes and shared across a data center. Cloud platforms have deployed
such systems to improve overall system memory utilization, but performance can
vary across workloads. High-performance computing (HPC) is crucial in
scientific and engineering applications, where HPC machines also face the issue
of underutilized memory. As a result, improving system memory utilization while
understanding workload performance is essential for HPC operators. Therefore,
learning the potential of a disaggregated memory system before deployment is a
critical step. This paper proposes a methodology for exploring the design space
of a disaggregated memory system. It incorporates key metrics that affect
performance on disaggregated memory systems: memory capacity, local and remote
memory access ratio, injection bandwidth, and bisection bandwidth, providing an
intuitive approach to guide machine configurations based on technology trends
and workload characteristics. We apply our methodology to analyze thirteen
diverse workloads, including AI training, data analysis, genomics, protein,
fusion, atomic nuclei, and traditional HPC bookends. Our methodology
demonstrates the ability to comprehend the potential and pitfalls of a
disaggregated memory system and provides motivation for machine configurations.
Our results show that eleven of our thirteen applications can leverage
injection bandwidth disaggregated memory without affecting performance, while
one pays a rack bisection bandwidth penalty and two pay the system-wide
bisection bandwidth penalty. In addition, we also show that intra-rack memory
disaggregation would meet the application's memory requirement and provide
enough remote memory bandwidth.Comment: The submission builds on the following conference paper: N. Ding, S.
Williams, H.A. Nam, et al. Methodology for Evaluating the Potential of
Disaggregated Memory Systems,2nd International Workshop on RESource
DISaggregation in High-Performance Computing (RESDIS), November 18, 2022. It
is now submitted to the CCPE journal for revie
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
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