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Consider a scenario where you need to run two different deep learning models, Model A and Model B, within separate Docker containers on the same NVIDIA GPU. Model A requires CUDA 11.2, while Model B requires CUDA 11.6. How can you achieve this while minimizing conflicts and ensuring each model has access to its required CUDA version?
A. Install both CUDA 11.2 and CUDA 11.6 inside each Docker container and use 'LD LIBRARY PATH' to switch between the CUDA versions for each model.
B. Mount the CUDA libraries from the host machine into both containers using Docker volumes, ensuring each container has access to both CUDA versions.
C. Create a single Docker image with both CUDA versions and dynamically link the correct CUDA libraries at runtime using environment variables.
D. Use separate Docker images for each model, each based on the appropriate 'nvidia/cuda' image (e.g., 'nvidia/cuda:ll .2-base-ubuntu20.04' and nvidia/cuda: 1 1.6-base-ubuntu20.04 s).
E. Install both CUDA 11.2 and CUDA 11.6 on the host system and use 'CUDA VISIBLE DEVICES* to isolate each model to a specific CUDA version.
Answer: D
Explanation:
The recommended and most straightforward approach is to use separate Docker images (B), each based on the specific nvidia/cuda' image version needed. This creates isolated environments, avoiding conflicts and ensuring each model has the correct CUDA toolkit. Installing multiple CUDA versions on the host (A) can lead to conflicts and isn't necessary with Docker. Installing multiple CUDA versions within a single container (C, D) adds complexity and potential conflicts. Mounting CUDA libraries from the host (E) might work, but it's less isolated and can create dependency management issues.
NEW QUESTION # 216
You are setting up a virtualized environment (using VMware vSphere) to run GPU-accelerated workloads. You have multiple physical GPUs in your server and want to assign specific GPUs to different virtual machines (VMs) for dedicated access. Which vSphere technology would BEST support this?
A. VMware DRS (Distributed Resource Scheduler)
B. VMware vMotion
C. VMware DirectPath I/O (Passthrough)
D. VMware vGPU
E. VMware High Availability (HA)
Answer: C
Explanation:
VMware DirectPath I/O (Passthrough) allows a VM to have exclusive access to a physical PCle device, such as a GPIJ. This provides the best performance because the VM can directly access the GPU without virtualization overhead. vGPI allows sharing of a GPU among multiple VMs, but DirectPath I/O provides dedicated access. vMotion migrates VMs. HA restarts VMS after failure. DRS balances resources across hosts.
NEW QUESTION # 217
Consider an AI server equipped with two NVIDIAAI 00 GPUs interconnected with NVLink. You want to maximize the memory bandwidth available to a CUDA application. You observe that the application's performance doesn't scale linearly with the number of GPUs. Which of the following coding techniques or configurations could potentially improve inter-GPU memory access performance?
A. Use CUDA-aware MPl for inter-GPU communication to leverage NVLink.
B. Employ Unified Memory (I-JM) with prefetching to automatically migrate data between GPUs as needed.
C. Ensure all memory allocations are performed on GPU O to minimize data transfer.
D. Disable NVLink to force the application to use PCle, which might provide more consistent performance.
E. Manually manage data transfers between GPUs using 'cudaMemcpyPeer' to exploit NVLink bandwidth. Choose the GPU with more free memory for allocations.
Answer: E
Explanation:
ScudaMemcpyPeer allows explicit, optimized data transfers between GPUs using NVLink. Unified Memory with prefetching can simplify development, but might not always provide the best performance. CUDA-aware MPl is typically used for inter-node communication, not intra-node GPU-GPU. Allocating all memory on one GPU defeats the purpose of multi-GPU acceleration. PCle will be slower than NVLink. Manually managing memory transfers, while complex, gives the programmer the most control over leveraging NVLink bandwidth.
NEW QUESTION # 218
Consider a scenario where you are running a CUDA application on an NVIDIA GPU. The application compiles successfully but crashes during runtime with a *CUDA ERROR ILLEGAL ADDRESS* error. You've carefully reviewed your code and can't find any obvious out- of-bounds memory accesses. What advanced debugging techniques could help you pinpoint the source of this error?
A. Reduce the block size used in CUDA kernels to decrease the likelihood of shared memory conflicts.
B. Utilize NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.
C. Employ the CUDA Debugger (cuda-gdb) to step through the code and inspect variable values and memory contents.
D. Use 'cuda-memcheck' to detect memory access errors at runtime.
E. Enable ECC (Error Correction Code) memory on the GPU to detect and correct memory errors.
Answer: B,C,D
Explanation:
'cuda-memcheck' (A) is specifically designed to detect memory access errors. 'cuda-gdb' (B) allows for detailed code inspection. NVIDIA Nsight Systems (C) provides profiling information that can help identify memory allocation issues that lead to the error. Enabling ECC (D) might mask the symptom but doesn't fix the underlying code error, also ECC is enabled by default, so that's a red herring. Reducing block size (E) is only relevant if shared memory is involved and potentially overflowing.
NEW QUESTION # 219
You are setting up a new A1 server with multiple NVIDIA GPUs for deep learning workloads. You need to install the NVIDIA Container Toolkit. After installing the necessary packages using your distribution's package manager, what is the most crucial next step to ensure proper integration with Docker?
A. Run 'nvidia-smr to verify GPU detection. This automatically configures the container runtime.
B. Reboot the server to load the new kernel modules.
C. Manually create vetc/docker/daemon.json' and add 'nvidia' as a default runtime.
D. Restart the Docker daemon (e.g., 'systemctl restart docker'
E. Run 'nvidia-ctk runtime configure -runtime=docker' .
Answer: E
Explanation:
The 'nvidia-ctk runtime configure -runtime=docker' command is essential to configure Docker to use the NVIDIA Container Runtime. This integrates the NVIDIA drivers and libraries within the Docker environment, allowing containers to access the GPUs. While restarting Docker is often helpful, 'nvidia-ctk' performs the necessary configuration steps. Rebooting is not always necessary after just installing a package, and manually editing 'daemon.json' is generally not the recommended approach. 'nvidia-smi' simply reports the status of the GPUs, it doesn't configure the runtime.
NEW QUESTION # 220
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