The HPC community is actively researching and evaluating tools to
support execution of scientific applications in cloud-based environ-
ments. Among the various technologies, containers have recently
gained importance as they have significantly better performance
compared to full-scale virtualization, support for microservices and
DevOps, and work seamlessly with workflow and orchestration
tools. Docker is currently the leader in containerization technology
because it offers low overhead, flexibility, portability of applications,
and reproducibility. Singularity is another container solution that
is of interest as it is designed specifically for scientific applications.
It is important to conduct performance and feature analysis of the
container technologies to understand their applicability for each
application and target execution environment.
This paper presents a (1) performance evaluation of Docker and
Singularity on bare metal nodes in the Chameleon cloud (2) mecha-
nism by which Docker containers can be mapped with InfiniBand
hardware with RDMA communication and (3) analysis of mapping
elements of parallel workloads to the containers for optimal re-
source management with container-ready orchestration tools. Our
experiments are targeted toward application developers so that
they can make informed decisions on choosing the container tech-
nologies and approaches that are suitable for their HPC workloads
on cloud infrastructure. Our performance analysis shows that sci-
entific workloads for both Docker and Singularity based containers
can achieve near-native performance.
Singularity is designed specifically for HPC workloads. However,
Docker still has advantages over Singularity for use in clouds as
it provides overlay networking and an intuitive way to run MPI
applications with one container per rank for fine-grained resources
allocation. Both Docker and Singularity make it possible to directly
use the underlying network fabric from the containers for coarse-
grained resource allocation.
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A Layered Approach for Modular Container Construction and Orchestration in HPC Environments
Large-scale, high-throughput computational science faces an accelerating convergence of software and hardware. Software container-based solutions have become common in cloud-based datacenter environments, and are considered promising tools for addressing heterogeneity and portability concerns. However, container solutions reflect a set of assumptions which complicate their adoption by developers and users of scientific workflow applications. Nor are containers a universal solution for deployment in high-performance computing (HPC) environments which have specialized and vertically integrated scheduling and runtime software stacks. In this paper, we present a container design and deployment approach which uses modular layering to ease the deployment of containers into existing HPC environments. This layered approach allows operating system integrations, support for different communication and performance monitoring libraries, and application code to be defined and interchanged in isolation. We describe in this paper the details of our approach, including specifics about container deployment and orchestration for different HPC scheduling systems. We also describe how this layering method can be used to build containers for two separate applications, each deployed on clusters with different batch schedulers, MPI networking support, and performance monitoring requirements. Our experience indicates that the layered approach is a viable strategy for building applications intended to provide similar behavior across widely varying deployment targets.
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- Award ID(s):
- 1807563
- NSF-PAR ID:
- 10284483
- Date Published:
- Journal Name:
- ScienceCloud '21: Proceedings of the 11th Workshop on Scientific Cloud Computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3452370.3466001
