Summary Containerisation for High Performance Computing Systems arxiv.org
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The text discusses the widespread use of containerization in cloud and HPC systems, highlighting challenges such as library mismatches and security threats, and potential research opportunities including containerizing AI apps and improving performance and security.
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Key Points
- Containerization is widely used in both cloud and high-performance computing (HPC) environments to improve application deployment efficiency.
- There are differences in containerization between cloud and HPC systems, such as security levels and container size/portability.
- HPC container engines like Singularity and Shifter have shown near-native performance in terms of CPU, memory, network bandwidth, and GPU usage.
- HPC systems typically rely on workload managers like PBS and Slurm for container orchestration, while cloud systems use platforms like Kubernetes.
- Challenges in HPC containerization include library mismatches, compatibility issues, security concerns, and performance degradation with GPUs.
- Research opportunities include containerizing AI applications, integrating DevOps practices, enabling resource elasticity, and using minimal operating systems.
- Containerization can bridge the gap between on-premise HPC clusters and public clouds, providing flexibility in resource usage.
- Further research and engineering efforts are needed to fully implement container orchestrators in HPC clusters and address challenges in containerization.
Summaries
46 word summary
Containerization is widely used in cloud and HPC systems, with engines like Shifter, Charliecloud, Singularity, SARUS, and UDocker. Challenges include library mismatches, compatibility issues, security threats, and degraded GPU performance. Research opportunities include containerizing AI apps, integrating DevOps, enabling resource elasticity, and improving performance and security.
92 word summary
Containerization is widely used in cloud and HPC systems, with engines like Shifter, Charliecloud, Singularity, SARUS, and UDocker supporting non-root privileges, MPI, and GPU. HPC systems use workload managers like PBS, LSF, Grid Engine, OAR, and Slurm. Challenges include library mismatches, compatibility issues, security threats, and degraded GPU performance. Research opportunities include containerizing AI apps, integrating DevOps, enabling resource elasticity, and improving performance and security. Containerization optimizes resource usage and bridges the gap between on-premise HPC clusters and public clouds. Further research is needed to fully implement container orchestrators in HPC clusters.
166 word summary
Containerization is widely used in both cloud and high-performance computing (HPC) systems. Efforts have been made to enable container orchestration on HPC systems, with container engines like Shifter, Charliecloud, Singularity, SARUS, and UDocker offering features such as non-root privileges and support for MPI and GPU. Performance evaluations show that HPC container engines can achieve near-native performance. HPC systems rely on workload managers like PBS, Spectrum LSF, Grid Engine, OAR, and Slurm for container orchestration. Challenges in containerization for HPC systems include library mismatches, compatibility issues, kernel optimization limitations, security threats, and degraded performance with GPUs and accelerators. Research opportunities include containerizing AI applications, integrating DevOps practices, enabling resource elasticity, and improving performance, security, and usability of containerized applications in HPC environments. Containerization can bridge the gap between on-premise HPC clusters and public clouds, optimizing resource usage. Further research and engineering are needed to fully implement container orchestrators in HPC clusters. Containerization will continue to play a crucial role in application development and simplifying HPC software stacks.
428 word summary
Containerization is a widely used technology in both cloud and high-performance computing (HPC) systems. While there are differences in containerization and container orchestration between these two types of systems, efforts have been made to enable container orchestration on HPC systems. Several container engines designed for HPC systems, such as Shifter, Charliecloud, Singularity, SARUS, and UDocker, offer features like non-root privileges and support for MPI and GPU. Performance evaluations have shown that HPC container engines can achieve near-native performance in terms of CPU, memory, network bandwidth, and GPU usage.
HPC systems typically rely on workload managers like PBS, Spectrum LSF, Grid Engine, OAR, and Slurm for container orchestration. Cloud orchestrators like Kubernetes and Docker Swarm automate configuration, coordination, and management of cloud systems. Container orchestration strategies for HPC systems often leverage the mechanisms of existing cloud orchestrators or utilize the capabilities of HPC workload managers or software tools.
Challenges in containerization for HPC systems include library mismatches, compatibility issues between container engines and images, kernel optimization limitations, security threats, and performance degradation with GPUs and accelerators. Research opportunities include containerizing AI applications on HPC systems, integrating DevOps practices, enabling resource elasticity, moving towards minimal operating systems, and improving the performance, security, and usability of containerized applications in HPC environments.
To facilitate containerization of AI applications on HPC systems, up-to-date documentation, versatile base container images, and instructions on software package installation or updates are important. Container registries can provide pre-built container images for easy access and ensure container security. Linux namespaces offer isolation and resource control, and clear instructions on their availability should be provided by HPC centers.
DevOps integration in HPC environments can be achieved through containerization with tools like Jenkins. Middleware systems can bridge container building environments with HPC resource managers and schedulers, providing a portable way to enable DevOps in HPC centers. Improving the elasticity of HPC infrastructure can be done by introducing containerization to workload managers. Containers can also replace parts of the HPC software stack, reducing complexity and enabling quick replacement of services.
Containerization can help reduce the performance gap and deployment complexity between on-premise HPC clusters and public clouds. It allows for the movement of containers between HPC and cloud environments to optimize resource usage.
The paper concludes by emphasizing the need for further research and engineering to fully implement container orchestrators within HPC clusters. Containerization will continue to play a crucial role in application development, resource elasticity, and simplifying HPC software stacks.
References to various container technologies, container orchestration platforms, container engines, middleware systems, and HPC workload managers are provided in the paper.
1495 word summary
Containerisation has become widely utilized in both cloud and high-performance computing (HPC) environments. Containers offer improved efficiency in application deployment by encapsulating complex programs with their dependencies in isolated environments. However, there are differences in containerization between cloud and HPC systems. HPC systems often have higher security levels, which restrict users' ability to customize environments. As a result, containers on HPC systems include a heavy package of libraries, making their size larger and compromising portability. In contrast, cloud containers are smaller and more portable. Additionally, container orchestration, which facilitates the deployment and management of containers at scale, is more prevalent in cloud systems compared to HPC systems. However, there have been proposals to enable container orchestration on HPC systems. This paper provides a survey of containerization and its orchestration strategies on HPC systems, highlighting the differences with cloud systems. It also discusses challenges and envisions potential directions for research and engineering.
Containerization is a virtualization technology that provides separation of application execution environments. Containers utilize the dependencies in their host kernel, resulting in faster startup times compared to virtual machines (VMs). Docker is one of the most popular container engines, supporting multiple platforms and providing resource isolation and limitation through namespaces and cgroups. Other container engines designed for HPC systems include Shifter, Charliecloud, Singularity, SARUS, and UDocker. These engines are designed to meet the high-security requirements of HPC systems and offer features such as non-root privileges and support for MPI and GPU.
Performance evaluations of HPC container engines have shown that containers can achieve near-native performance in terms of CPU, memory, network bandwidth, and GPU usage. Singularity, in particular, has been found to provide close to native performance on CPU, memory, and network bandwidth, with a slight overhead on GPU usage. Shifter has demonstrated comparable CPU performance to bare metal, while Charliecloud has shown large overhead on Lustre's MDS and OSS due to its bare tree structure. SARUS has shown strong scaling capability on Cray XC systems with hybrid GPU and CPU nodes.
In terms of container orchestration, HPC systems typically rely on workload managers such as PBS, Spectrum LSF, Grid Engine, OAR, and Slurm. These workload managers allocate resources, schedule jobs, and enforce resource limits. Cloud orchestrators, on the other hand, automate configuration, coordination, and management of cloud systems. They include platforms like Kubernetes and Docker Swarm. Container orchestration strategies for HPC systems often leverage the mechanisms of existing cloud orchestrators or utilize the capabilities of HPC workload managers or software tools.
In conclusion, containerization has been widely adopted in both cloud and HPC systems. While there are differences in containerization and container orchestration between these two types of systems, efforts have been made to enable container orchestration on HPC systems. Performance evaluations have shown that HPC container engines can achieve near-native performance in various aspects. Future research and engineering efforts should focus on addressing the challenges specific to containerization and container orchestration in HPC systems.
Containerisation is being increasingly used in high-performance computing (HPC) systems. However, there are several challenges and open issues that need to be addressed. Compatibility issues arise due to library mismatches between container images and host systems, as well as compatibility issues between container engines and images. Standardisation efforts such as the Open Container Initiative (OCI) aim to address these challenges. Kernel optimisation is another area of concern, as containers are generally not allowed to install their own kernel modules on the host. Security is also a major consideration, with threats such as privilege escalation, denial-of-service attacks, and information leaks. Performance degradation can occur when using containers with GPUs and accelerators, as customised libraries may be required for optimal performance.
To overcome these challenges, several research and engineering opportunities have been identified. One area of focus is the containerisation of AI applications in HPC systems. Leveraging the compute power and resources of HPC clusters can greatly benefit AI model training. Private container registries within HPC centres can ensure container security and provide pre-built images accessible to users. Guidelines for Linux namespaces can help ensure security within HPC environments.
DevOps practices can also be integrated into HPC systems to improve reproducibility and streamline application deployment. Singularity containers can be integrated with DevOps tools such as Jenkins for automated workflows. Middleware systems that are flexible and easy to plugin or plugout new components can further enhance DevOps capabilities in HPC environments.
Resource elasticity is another important area of research, with the goal of enabling flexible usage of hardware resources in HPC systems. Integrating container orchestration platforms like Kubernetes with HPC workload managers can introduce resource elasticity to traditional batch scheduling systems.
Moving towards minimal operating systems (OS) can help reduce maintenance efforts in HPC environments. By maintaining a minimal OS kernel and containerising the rest of the HPC software stack, administrators can simplify system management and updates.
Overall, containerisation in HPC systems offers numerous benefits, but also presents several challenges. Ongoing research efforts are focused on addressing these challenges and finding innovative solutions to improve the performance, security, and usability of containerised applications in HPC environments.
Containerization is a valuable solution for deploying AI applications on high-performance computing (HPC) systems. Unlike traditional programming languages like C/C++, AI applications written in Python cannot be compiled into an executable file with all dependencies included. This poses a challenge for deploying AI applications on HPC infrastructures, which often rely on closed-source applications and have restricted user privileges and security restrictions. Containerization offers a way to customize execution environments while taking advantage of HPC hardware and optimized AI libraries. To facilitate containerization of AI applications on HPC systems, it is important to provide up-to-date documentation and tutorials, maintain versatile base container images, and give instructions on software package installation or updates.
A container registry is a useful repository for providing pre-built container images that can be easily accessed by users. It can be portable for deploying applications on cloud clusters and can ensure container security by signing and pulling images from trusted registries. To simplify usage, future work can enable HPC workload managers to boot default containers on compute nodes, matching the environments of user login nodes. This allows jobs to be started without user awareness of the presence of containers or additional user intervention.
Linux namespaces are used within an implementation to provide isolation and resource control. Clear instructions on the availability of namespaces should be provided by HPC centers, with a minimal set of namespaces enabled for general user groups. Additional sets of namespaces may be required for advanced use cases. Workload managers can start containers with appropriate namespaces enabled when users submit container jobs.
DevOps, which integrates development and operations, has been widely adopted in cloud computing but is not well suited for HPC environments. HPC-specific DevOps tools are needed to overcome the inflexibility and optimization challenges of HPC environments. Containerization can provision DevOps environments in HPC systems, enabling the integration of DevOps workflows. Singularity has been integrated with Jenkins, a popular automation platform, to bring continuous integration, delivery, and deployment practices into HPC workflows.
Middleware systems can bridge container building environments with HPC resource managers and schedulers. They perform job deployment, management, and data staging, and can be located on an HPC cluster or connected to it with secure authentication. Middleware systems provide a portable way to enable DevOps in HPC centers and can be a future research direction.
Improving the elasticity of HPC infrastructure is a major difference between HPC and cloud computing. Containerization can contribute to improving the elasticity of HPC infrastructure by introducing it to workload managers. Kubernetes has been used to instantiate containerized HPC workload managers dynamically, creating a single-tenant or multi-tenant environment.
Containers can partially substitute the current HPC software stack by using minimal operating system (OS) base images on compute nodes. This reduces the number of components in the kernel image and simplifies post-boot configurations. Containerized services can be quickly replaced without affecting the entire system when failures occur. Long-term research is needed to control the software stack and workloads that are partially native and partially containerized on HPC systems.
Containerization plays a vital role in reducing the performance gap and deployment complexity between on-premise HPC clusters and public clouds. With advancements in low-latency networks and accelerators like GPUs and TPUs, containers can be moved from HPC to cloud to temporarily relieve peak demands or from cloud to HPC to exploit powerful hardware resources.
The paper concludes by discussing the opportunities and challenges of containerization in HPC systems. It emphasizes the need for further research and engineering to fully implement container orchestrators within HPC clusters. Containerization will continue to play an essential role in application development, resource elasticity, and reducing the complexity of HPC software stacks. The authors acknowledge the funding received for their projects and express gratitude to Dr. Joseph Schuchart for proofreading the contents.
References: The paper includes references to various container technologies, container orchestration platforms, container engines, middleware systems, and HPC workload managers. It also references specific software frameworks like TensorFlow, Singularity