Forum sur les GPUs dans le cluster Bluetooth - Amélioration des logiciels commerciaux

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Forum sur les GPUs dans le cluster Bluetooth - Amélioration des logiciels commerciaux

Table of Contents

1. Introduction

2. Current Status of GPUs on the Bluetooth Cluster

  • Integration of GPUs into the Cluster
  • Sharing GPUs among multiple jobs
  • Flexibility in supporting different generations and vendors

3. Evolution of GPUs in the Brutus Cluster

  • Early experiments with GPUs in 2009
  • Migration to newer versions of Cuda
  • Introduction of Fermi nodes and ATI GPUs

4. Goals for GPU Integration

  • Flexibility in supporting multiple generations and vendors
  • Maximizing resource utilization
  • Ease of use for users

5. Solution for GPU Integration

  • Integration with Platform LSF
  • Implementation of custom solution for resource utilization
  • Interception of Cuda library calls using Fairy Dust library
  • Implementation of support for AMD GPUs

6. Integration with the Queuing System

  • Role of Platform LSF in job allocation
  • Role of Fairy Daemon in allocating GPUs to jobs
  • Examples of submitting GPU jobs

7. Operational Issues and Challenges

  • Compatibility between Cuda library and kernel driver versions
  • X server requirement for AMD GPUs
  • Stability issues with AMD kernel driver
  • Hardware challenges and vendor issues

8. Conclusion

  • Summary of the presentation

💡 Highlights

  • Integration of GPUs into the Bluetooth Cluster for enhanced computing power
  • Flexibility in supporting multiple generations and vendors of GPUs
  • Maximizing resource utilization by sharing GPUs among multiple jobs
  • Challenges and solutions for integrating AMD GPUs into the cluster
  • Operational issues and hardware challenges faced during the integration process

📝 Integration of GPUs into the Bluetooth Cluster

The Bluetooth Cluster has successfully integrated GPUs into its computing infrastructure. Rather than having a separate cluster or partition for GPUs, the GPUs are fully integrated into the cluster as additional compute nodes. This means that the same network and queuing system are used for both GPU and non-GPU jobs. However, it is important to note that the compute nodes are not reserved exclusively for GPU jobs. This means that a single GPU job may end up running on a node that is also running other GPU jobs or even non-GPU jobs if there is a lack of GPU-specific tasks.

To submit a GPU job to the cluster, users simply need to define the GPU resource while submitting the job. The job will then be allocated to a node that has the required number and type of GPUs available. This integration allows for flexible usage of GPUs and utilization of resources across the cluster.

💡 Highlights

  • GPUs are fully integrated into the Bluetooth Cluster as compute nodes
  • Sharing of compute resources between GPU and non-GPU jobs
  • Submission of GPU jobs by defining the GPU resource requirement
  • Flexibility in utilization of GPUs across the cluster

📝 Evolution of GPUs in the Brutus Cluster

The integration of GPUs into the Brutus Cluster has evolved over the years. In the early stages, around 2009, two Tesla nodes were attached to the login nodes, providing a total of four GPUs for interactive use by users. Later, in February-March, four GPU compute nodes were installed, each equipped with six GPUs and 12 cores.

The initial integration was done using Cuda 2.3, with later migrations to Cuda 3.0 and Cuda 3.1. The release of Cuda 3.1 was particularly significant as it introduced a feature called 'cuda visible devices', which allows for the sharing of GPUs among multiple jobs. However, at the time of integration, this feature did not exist, and a custom solution had to be built.

In recent years, the addition of Fermi nodes with two GPUs and 24 cores each, as well as an ATI compute node with ATI GPUs, further expanded the GPU capabilities of the cluster. Throughout the integration process, the focus has been on flexibility, maximizing resource utilization, and ease of use for users.

💡 Highlights

  • Evolution of GPU integration in the Brutus Cluster over the years
  • Initial attachment of Tesla nodes for interactive use
  • Installation of GPU compute nodes with multiple GPUs
  • Migration to newer versions of Cuda for enhanced functionality
  • Addition of Fermi nodes and ATI compute node for increased GPU capabilities

📝 Goals for GPU Integration

The integration of GPUs into the cluster was driven by three primary goals: flexibility, maximizing resource utilization, and ease of use for users.

Flexibility was a crucial factor in the integration process. The aim was to support multiple generations and vendors of GPUs, rather than being locked into a specific type or brand. This allows users to have a choice in selecting the GPU model that best suits their needs.

Maximizing resource utilization was another key objective. Sharing of GPUs among multiple jobs was essential to ensure that the GPUs are utilized to their full potential. This involved developing custom solutions and implementing features like 'cuda visible devices' for efficient allocation of GPU resources.

Lastly, the integration was designed to be user-friendly and easy to use. The system was built to be reasonably straightforward and transparent for users, allowing them to easily submit and manage GPU jobs without unnecessary complications.

💡 Highlights

  • Primary goals of GPU integration: flexibility, resource utilization, and ease of use
  • Support for multiple generations and vendors of GPUs
  • Maximizing GPU resource utilization through sharing among multiple jobs
  • User-friendly system for seamless submission and management of GPU jobs

📝 Solution for GPU Integration

The integration of GPUs into the cluster required a robust solution that would meet the goals of flexibility, resource utilization, and user-friendliness.

To achieve flexibility, the integration was done using Platform LSF, with the addition of new resources to define the number and type of GPUs available on each compute node. This allowed users to select specific models of GPUs for their jobs.

Maximizing resource utilization was initially challenging, as the early versions of Cuda did not provide native support for sharing GPUs among multiple jobs. To overcome this limitation, a custom solution called Fairy Dust was developed. Fairy Dust intercepted calls between user applications and the Cuda Library, implementing all the required APIs for device management and resource allocation. This ensured that GPU resources are efficiently shared among multiple jobs.

As for support for AMD GPUs, the integration leveraged the existing code for intercepting OpenCL calls. By applying similar techniques used with Cuda, support for AMD GPUs was implemented seamlessly. Users could request AMD GPUs without any noticeable difference in the submission process.

💡 Highlights

  • Platform LSF used for flexibility in GPU integration
  • Custom solution Fairy Dust for maximizing resource utilization
  • Intercepting calls between user applications and the Cuda Library
  • Support for AMD GPUs through similar techniques used with Cuda
  • Seamless integration of AMD GPUs into the cluster

📝 Integration with the Queuing System

The integration of GPUs into the queuing system involved the interaction between Platform LSF and the Fairy Daemon, which was responsible for allocating GPUs to jobs.

Platform LSF's role was to allocate resources on the cluster level and dispatch jobs to the correct GPU types based on user requirements. It ensured that jobs requesting GPU resources were assigned to nodes with the specified GPU types. However, Platform LSF did not handle physical allocations of GPUs.

The Fairy Daemon, on the other HAND, was running on each compute node and served as the intermediary between the queuing system and the user's application. The Fairy Daemon communicated with Platform LSF to receive job information and determine available GPU resources. It then allocated the appropriate GPUs to the job, ensuring that the job ran on the correct node with the required GPUs.

By reading the environment variables set in the job submission, the Fairy Daemon could determine the appropriate GPU resources to allocate. This seamless communication between the queuing system, the Fairy Daemon, and the user's application facilitated the efficient allocation and utilization of GPU resources.

💡 Highlights

  • Platform LSF responsible for resource allocation at the cluster level
  • Fairy Daemon as the intermediary between the queuing system and applications
  • Reading environment variables for determining GPU resource allocation
  • Seamless communication for efficient GPU resource utilization

📝 Operational Issues and Challenges

During the integration of GPUs into the cluster, several operational issues and challenges were encountered.

One significant challenge was ensuring compatibility between the version of the Cuda library and the kernel driver. The Cuda library and kernel driver had to be an exact match, making it impossible to have multiple Cuda versions on the same host. To address this, kernel versions were maintained on select nodes to allow users to select older Cuda versions if needed.

For AMD GPUs, an X server was required to be running on the compute node. Unlike Nvidia, the user space library for AMD GPUs communicated with the GPUs through the X server, making it necessary to have an active X server even for GPU computations. This requirement added complexity and could be a challenge to set up an X server without a monitor.

Stability issues were also encountered with the AMD kernel driver, with instances of kernel panics occurring during single OpenCL jobs. While newer releases may improve stability, the experience with the AMD kernel driver has been less reliable compared to Nvidia.

Despite these challenges, the integration of GPUs into the cluster hardware was relatively smooth, with minor issues like memory errors on Tesla nodes being addressed by vendor replacements.

💡 Highlights

  • Compatibility challenges between Cuda library and kernel driver versions
  • X server requirement for AMD GPUs and the associated complexities
  • Stability issues with the AMD kernel driver compared to Nvidia
  • Smooth integration of GPUs into the cluster hardware, with minor vendor-related issues

📝 Conclusion

The integration of GPUs into the Bluetooth Cluster has brought enhanced computing capabilities to the cluster infrastructure. By fully integrating GPUs into the cluster as compute nodes, the flexibility, resource utilization, and user-friendliness of the system have been significantly improved.

Despite challenges and operational issues encountered during the integration process, the cluster now boasts support for multiple generations and vendors of GPUs. The custom solutions implemented, such as the Fairy Dust library, have ensured efficient GPU resource sharing among multiple jobs. The seamless integration of AMD GPUs further expanded the possibilities for users.

In conclusion, the integration of GPUs into the cluster has proven to be a valuable enhancement, providing users with increased computational power and a greater choice of GPU options.

💡 Highlights

  • Enhanced computing capabilities brought by GPU integration
  • Improved flexibility, resource utilization, and user-friendliness
  • Support for multiple generations and vendors of GPUs
  • Custom solutions like Fairy Dust for efficient GPU resource sharing
  • Seamless integration of AMD GPUs for expanded possibilities

Frequently Asked Questions

Q: Can I choose the specific model of GPU for my job in the Bluetooth Cluster? A: Yes, the integration of GPUs into the cluster allows users to select a specific model of GPU based on their requirements.

Q: Can multiple jobs share the same GPU in the cluster? A: Yes, the cluster is designed to maximize resource utilization by allowing the sharing of GPUs among multiple jobs.

Q: Are there any limitations in using AMD GPUs compared to Nvidia GPUs in the cluster? A: There are some operational challenges when using AMD GPUs, such as the requirement for an X server and stability issues with the kernel driver. However, integration and compatibility have been achieved, providing users with options for AMD GPU usage.

Q: How is the allocation of GPU resources managed in the cluster's queuing system? A: The queuing system, Platform LSF, is responsible for allocating resources at the cluster level. The Fairy Daemon, running on each compute node, communicates with Platform LSF to determine available GPU resources and allocate them to jobs based on user requirements.

Q: Is it possible to run OpenCL code on AMD GPUs using the Nvidia compiler? A: While not recommended, it is possible in some cases to compile OpenCL code using the Nvidia compiler and run it on AMD GPUs. However, it is advisable to use the appropriate compiler for optimal performance and compatibility.

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