Hi everyone,
I’m currently working on optimizing ParaView builds for use in a rack server cluster setup, and I wanted to get some input on best practices and recommendations for improving performance and resource utilization.
We’re running ParaView across a multi-node rack server cluster with a mix of CPU and GPU nodes, and the goal is to achieve fast and efficient parallel processing for large-scale simulations. Here are some areas I’m focusing on:
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Parallel Build Configuration:
I’m using CMake to build ParaView, and I’ve read that enabling MPI (Message Passing Interface) and utilizing C++ compiler flags can make a big difference. Has anyone tried using OpenMPI or MVAPICH2 for better inter-node communication? -
GPU Support:
Our rack setup has a few nodes equipped with NVIDIA GPUs, and I want to ensure ParaView can leverage CUDA or OpenCL effectively for visualization tasks. Any tips on enabling GPU support in the build process for better GPU acceleration? -
Cluster Communication:
For the MPI configuration, what’s the best approach to ensure efficient communication between nodes? Should I tweak the TCP settings or is there a better network configuration I should consider? -
Disk I/O and Data Handling:
We’ve encountered some bottlenecks related to disk I/O when loading large datasets. Are there any tricks for optimizing ParaView’s ability to handle massive datasets distributed across the cluster’s storage? -
Performance Tuning:
Lastly, any insights on profiling and fine-tuning ParaView’s performance on a cluster? I’ve seen tools like vtune and gprof mentioned, but I’m wondering if there are any ParaView-specific methods for performance analysis.
Any help or suggestions on these areas would be greatly appreciated!