The problem describes a lack of resource isolation where a user's job can access GPUs not allocated to it. This is solved by enforcing resource containment using Linux Control Groups (cgroups). In the Slurm workload manager, which is commonly used in HPC environments with NVIDIA GPUs, the cgroup.conf file manages this integration. Setting the ConstrainDevices parameter to yes instructs Slurm to leverage the cgroup devices subsystem. This subsystem creates a specific allowlist of device files for the job, ensuring it can only access the exact GPU devices assigned by the scheduler, thereby preventing resource contention.

A. Increasing memory allocation is unrelated to device access control and will not prevent a process from accessing unallocated GPU devices.

C. Modifying job priority only changes the scheduling order; it does not enforce resource isolation for jobs that are already running.

D. Requesting additional CPU cores does not impose any restrictions on which GPU devices the job is permitted to access.

1. Official Vendor Documentation (Slurm): The official Slurm documentation for the cgroup.conf file explicitly defines the ConstrainDevices parameter. It states, "If set to 'yes', Slurm will use the devices cgroup to constrain a job's access to only the devices allocated to it." This directly addresses the question's scenario.

Source: SchedMD, Slurm Workload Manager, cgroup.conf documentation.

Reference: Parameter description for ConstrainDevices at https://slurm.schedmd.com/cgroup.conf.html.

2. Official Vendor Documentation (NVIDIA): NVIDIA's deployment guides for DGX systems, which are reference architectures for AI infrastructure, recommend using cgroups for proper resource isolation in a Slurm environment. This confirms it as a best practice.

Source: NVIDIA DGX SuperPOD with NVIDIA DGX A100 Deployment Guide.

Reference: Section 4.2.1, "Slurm Configuration," discusses the setup of cgroup.conf for resource management.

3. University Courseware/Documentation: Reputable university High-Performance Computing (HPC) centers document this configuration as essential for multi-user GPU environments. For example, the University of Florida Research Computing documentation explains that cgroups are necessary for proper GPU isolation.

Source: University of Florida Research Computing, HiPerGator User Docs.

Reference: Section "GPU Isolation" on the "Using GPUs on HiPerGator" page.