GPU Acceleration
- GPU Acceleration on MediaWiki Servers
This article details the configuration and benefits of GPU acceleration for MediaWiki 1.40 servers. GPU acceleration can significantly improve performance for resource-intensive tasks such as image resizing, video transcoding, and complex search queries. This guide is intended for system administrators and server engineers unfamiliar with integrating GPUs into a MediaWiki environment.
Understanding the Benefits
Traditionally, MediaWiki relies heavily on the CPU for most processing tasks. However, GPUs are highly optimized for parallel processing, making them ideal for specific workloads. Utilizing a GPU can:
- Reduce processing time for image manipulations.
- Accelerate video transcoding, critical for video extensions.
- Enhance the performance of full-text search through specialized libraries.
- Improve rendering speeds for complex pages with numerous extensions.
Hardware Requirements
Choosing the correct GPU is crucial for optimal performance. Consider the following specifications. The table below outlines recommended GPU options.
| GPU Model | VRAM | CUDA Cores (NVIDIA) / Stream Processors (AMD) | Estimated Cost (USD) |
|---|---|---|---|
| NVIDIA GeForce RTX 3060 | 12GB | 3584 | $300 - $400 |
| NVIDIA GeForce RTX 3070 | 8GB | 5888 | $400 - $500 |
| NVIDIA Tesla T4 | 16GB | 2560 | $600 - $800 |
| AMD Radeon RX 6700 XT | 12GB | 4608 | $350 - $450 |
Ensure that the server has a compatible PCIe slot and sufficient power supply capacity to support the chosen GPU. Consider the Server Power Supply requirements. The GPU driver must also be compatible with the server's operating system (typically Linux distributions like Ubuntu or CentOS).
Software Configuration
This section details the software components necessary for enabling GPU acceleration. We will focus on NVIDIA GPUs as they are currently the most widely supported. Similar principles apply to AMD GPUs with appropriate driver and library adjustments.
1. **Driver Installation:** Install the latest NVIDIA drivers for your GPU and operating system. Refer to the NVIDIA Driver Installation Guide for detailed instructions.
2. **CUDA Toolkit:** Download and install the CUDA Toolkit from the NVIDIA website. The CUDA Toolkit provides the necessary libraries and tools for developing and running GPU-accelerated applications. Ensure the CUDA version is compatible with your MediaWiki extensions and other software. See CUDA Toolkit Documentation.
3. **FFmpeg with NVIDIA NVENC/NVDEC:** If you are using video extensions like Extension:Video, configure FFmpeg to utilize the NVIDIA NVENC and NVDEC hardware encoders/decoders. This significantly speeds up video transcoding. Edit your FFmpeg configuration file (usually `ffmpeg.conf`) and add the following:
``` --enable-nvenc --enable-nvdec ```
4. **Full-Text Search (Elasticsearch/Solr):** If you are using Elasticsearch or Solr for full-text search, configure them to leverage GPU acceleration if supported. Both Elasticsearch and Solr have plugins or configurations that allow for GPU-accelerated search. See the Elasticsearch Documentation and Solr Documentation for details.
MediaWiki Extension Configuration
Several MediaWiki extensions can benefit from GPU acceleration.
| Extension | GPU Acceleration Benefit | Configuration Notes |
|---|---|---|
| Extension:ImageMagick | Faster image resizing, thumbnail generation, and format conversion. | Configure ImageMagick to use CUDA-enabled OpenCL libraries if available. |
| Extension:Video | Accelerated video transcoding and streaming. | Ensure FFmpeg is configured with NVENC/NVDEC (see above). |
| Extension:VectorMagic | Faster vectorization of images. | VectorMagic may require specific GPU configurations; consult the extension's documentation. |
| Extension:Semantic MediaWiki | Improved performance of complex queries involving image or video data. | Requires appropriate indexing and configuration of the underlying search engine (Elasticsearch/Solr). |
Carefully review the documentation for each extension to understand the specific configuration steps required to enable GPU acceleration.
Monitoring and Troubleshooting
After configuring GPU acceleration, it's essential to monitor its performance and troubleshoot any issues. Tools like `nvidia-smi` (for NVIDIA GPUs) can provide real-time information about GPU utilization, temperature, and memory usage.
| Command | Description |
|---|---|
| `nvidia-smi` | Displays GPU utilization, temperature, and memory usage. |
| `ffmpeg -encoders` | Lists available FFmpeg encoders, including NVENC/NVDEC. |
| System Logs | Check system logs for any errors related to the GPU driver or CUDA Toolkit. |
| MediaWiki Logs | Review MediaWiki's error logs for any issues encountered during image processing or video transcoding. |
If you encounter problems, common troubleshooting steps include:
- Verify that the GPU driver and CUDA Toolkit are correctly installed.
- Ensure that the GPU is properly seated in the PCIe slot.
- Check the power supply to ensure it can provide sufficient power to the GPU.
- Review the extension documentation for specific troubleshooting guidance.
- Consult the MediaWiki Troubleshooting page.
Further Resources
- NVIDIA Developer Zone
- AMD Developer Central
- MediaWiki Extension Directory
- Server Administration
- Performance Optimization
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️