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NEW QUESTION # 173
You are installing four NVIDIAAIOO GPUs in a server, and after installation, you observe that the PCle link speed for one of the GPUs is running at x8 instead of the expected x16. What could be the POSSIBLE causes for this reduced PCle link speed?
Answer: C
Explanation:
A reduced PCle link speed can result from multiple factors: a faulty GPU, insufficient PCle lanes from the CPU, the physical wiring of the PCle slot, or a BIOS/IJEFI configuration limiting the speed. All these are potentially viable cause. Thus answer E is correct.
NEW QUESTION # 174
You are configuring a BlueField DPU to run a custom packet processing application. You want to ensure that the application has exclusive access to certain CPU cores on the DPU. Which mechanism is best suited for isolating CPU cores for your application on the Bluefield DPU?
Answer: E
Explanation:
Cgroups provide a robust and flexible way to isolate and manage resources, including CPU cores, for applications. They allow you to create a dedicated cgroup for your application and limit its CPU usage to specific cores. 'taskset' is a viable option, but cgroups offer more comprehensive resource management capabilities. Modifying the bootloader is not a practical or recommended approach. CPU affinity settings in the application code depend on the application's design and may not be as reliable. Adjusting kernel scheduler parameters can be complex and affect other processes.
NEW QUESTION # 175
Consider a scenario where you're using GPUDirect Storage to enable direct memory access between GPUs and NVMe drives. You observe that while GPUDirect Storage is enabled, you're not seeing the expected performance gains. What are potential reasons and configurations you should check to ensure optimal GPUDirect Storage performance? Select all that apply.
Answer: A,C,D
Explanation:
Explanation:GPUDirect Storage requires PCle Gen4/Gen5 for sufficient bandwidth (B). The CUDA driver must be compatible with GPUDirect Storage (C). Direct I/O support in the file system is essential to bypass the OS cache and allow direct GPU access (D). RAID 0 (A) is about storage speed but not directly related to GDS functionality. Disabling CPU-side caching (E) is usually detrimental as it can reduce overall system performance. Note, this is not always bad but needs to be tested depending on application.
NEW QUESTION # 176
An AI engineering team trains a foundation model whose dataset resides on a high-performance parallel file system. Storage analysis reveals that a large percentage of CPU cycles are consumed copying data between storage devices and system memory before the GPUs can access it. The architect wants to reduce CPU overhead while increasing data throughput to the GPUs. Which technology should be implemented?
Answer: B
Explanation:
GPUDirect Storage enables compatible storage devices to transfer data directly into GPU memory, bypassing unnecessary CPU memory copies. This significantly reduces CPU overhead, lowers latency, and improves throughput for data-intensive AI training workloads. NVSwitch accelerates GPU interconnects, while MIG and the Device Plugin serve completely different purposes.
NEW QUESTION # 177
You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a 'CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?
Answer: A,C
Explanation:
A 'CUDA ERROR OUT OF MEMORY' error can occur due to insufficient shared memory within the container (A). Increasing the shared memory size allows the container to allocate more memory for inter-process communication and GPU data transfers. Monitoring GPU memory usage both inside and outside the container (D) is crucial to identify the source of the memory exhaustion. 'CUDA VISIBLE DEVICES' and (B & C) are primarily used for GPU selection and ordering, not memory management, although limiting GPU visibility could indirectly reduce overall memory consumption if the application is poorly designed and tries to allocate memory on all visible GPUs regardless of need. Lowering compute capability won't directly affect memory usage, although the application will need less memory to process, it might cause issues.
NEW QUESTION # 178
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