Horovod Benchmarks: Evaluating Distributed Training Performance

Horovod is a popular framework for distributed deep learning, designed to optimize training efficiency across multiple GPUs and nodes. This page explores its benchmarking capabilities, highlighting key metrics and comparisons.

Key Features of Horovod Benchmarks

• Scalability: Horovod scales seamlessly from single-node to multi-node setups, supporting up to thousands of GPUs.
• Performance Optimization: Benchmarks show up to 2x speedup compared to traditional methods like torch.distributed.
• Cross-Platform Support: Works with TensorFlow, PyTorch, and other frameworks.

Benchmark Results Overview

Use Cases

• Large-Scale Model Training: Ideal for distributed training of models like BERT or ResNet.
• Multi-Node Clusters: Optimized for HPC environments and cloud-based GPU farms.

For deeper insights into Horovod's architecture, visit our introduction page.

Further Reading

• Horovod Documentation
• Benchmarking Tools Comparison
• Performance Tuning Guide