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You are observing intermittent failures in your NVSHMEM application, and you suspect memory corruption. What is a good first step to debug this issue using NVSHMEM's debugging tools?
A. Increase the 'ulimit -r value
B. Set NVSHMEM DEBUG-MEMORY TRACE
C. Run the application with 'valgrind'
D. Enable NCCL DEBUG-INFO
E. Use 'cuda-memcheck'
Answer: B
Explanation:
Setting enables detailed memory allocation and deallocation tracing within NVSHMEM, which can help identify memory corruption issues. NCCL DEBUG is for NCCL issues, not NVSHMEM. Scuda-memcheck' is a good general tool for CUDA memory errors, but 'NVSHMEM_DEBUG' is more specific to NVSHMEM's managed memory. 'valgrind' is a general-purpose memory debugger, but NVSHMEM's built-in tracing is usually more effective for NVSHMEM-specific problems. The 'ulimit' value affects resource limits, but it doesn't directly help debug memory corruption.
NEW QUESTION # 18
Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?
A. Worker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.
B. The control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller-manager, while worker nodes run kubelet and kube-proxy.
C. The control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.
D. The control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Kubernetes architecture, thecontrol planeis composed of several core components including thekube- apiserver,etcd(the cluster's key-value store),kube-scheduler, andkube-controller-manager. These manage the overall cluster state, scheduling, and orchestration of workloads. Theworker nodesare responsible for running the actual containers and include thekubelet(agent that communicates with the control plane) and kube-proxy(handles network routing for services). Other options incorrectly assign these components or roles.
NEW QUESTION # 19
You have deployed the NVIDIA Device Plugin for Kubernetes on your BCM-managed cluster. After a kernel update on one of the worker nodes, the device plugin fails to discover the GPUs. The error messages indicate a mismatch between the driver version expected by the device plugin and the actual driver version installed on the node. What is the MOST reliable way to resolve this issue without disrupting other workloads?
A. Use a DaemonSet to manage the NVIDIA driver installation on all worker nodes, ensuring a consistent driver version across the cluster and compatibility with the device plugin.
B. Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.
C. Remove the NVIDIA Device Plugin and replace it with the 'nvidia-driver-installer' helm chart
D. Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.
E. Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.
Answer: A
Explanation:
Using a DaemonSet to manage the NVIDIA driver installation is the MOST reliable and scalable solution. It ensures that all worker nodes have the correct driver version and simplifies driver updates. Manually downgrading or updating individual nodes (A, B) is not sustainable. Reinstalling the toolkit (D) might not update the driver. Simply removing and replacing the plugin (E) doesn't address driver mismatch and would likely use a similar deployment method that would lead to the same error.
NEW QUESTION # 20
A data scientist reports that a Run.ai job is consistently crashing with a 'SIGKILL' signal. After verifying that the job is not exceeding its resource limits (CPU, memory, GPU), what is the MOST likely reason for this signal, and how can you diagnose it further within the Run.ai environment?
A. The Run.ai agent is terminating the job due to exceeding a pre-defined time limit. Check the job's configuration for any time limits or deadlines.
B. The job is being preempted due to higher priority tasks, resulting in SIGKILL. Check the job's priority, quota, and resource usage history with ACM.
C. The process is consuming a lot of disk I/O.
D. The Kubernetes liveness probe is failing, causing the pod to be restarted. Inspect the pod's events and liveness probe configuration.
E. The job is experiencing a kernel panic on the node. Check the system logs on the node for kernel-related errors.
Answer: D
Explanation:
A 'SIGKILL' signal often indicates that the process was forcibly terminated by the operating system or a container runtime. A failing Kubernetes liveness probe is a common cause. If the probe fails, Kubernetes will restart the pod, sending a SIGKILL to the existing process. You can diagnose this by inspecting the pod's events using 'kubectl describe pod or 'runai describe job and examining the liveness probe configuration in the pod's YAML definition. Kernel panics, Run.ai agent time limits, and preemption are less likely to result directly in a SIGKILL signal.
NEW QUESTION # 21
You are using NVIDIA MPS (Multi-Process Service) to allow multiple CUDA applications to share a single GPU. One of the applications is consistently crashing. How can you isolate the faulty application using MPS?
A. Use 'nvidia-smi' to monitor the GPU's utilization and identify the application with the highest memory usage.
B. Restart the entire server to clear the GPU memory.
C. Disable MPS and run each application in isolation to identify the crashing application.
D. Run each application with a reduced number of threads to minimize potential conflicts.
E. Analyze the system logs for error messages associated with the application's process ID (PID).
Answer: C,E
Explanation:
The most direct approach is to disable MPS and run each application independently to pinpoint the source of the crashes. Examining the system logs for error messages linked to specific PIDs helps identify the failing process. Monitoring GPU utilization (B) might provide hints, but it doesn't directly isolate the faulty application. Reducing threads (D) might mask the issue, but it doesn't solve it. Restarting the server (E) is a temporary solution and doesn't address the root cause.
NEW QUESTION # 22
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