Training & Validation in HPC is the process of breaking the "Single-GPU" barrier.

On a laptop, you run a script, and it finishes in a week. On an HPC cluster, you distribute that script across 64 GPUs to finish in 2 hours. However, simply requesting more GPUs doesn't make code faster. You must fundamentally restructure your code to handle Process Coordination, Gradient Synchronization, and Data Sharding.

Here is the detailed breakdown of Distributed Training strategies (Data vs. Model Parallelism), the critical "Data Loading" bottleneck, and the validation workflow, followed by the downloadable Word file.

1. Distributed Training Architectures

There are two main ways to scale model training on HPC.

A. Distributed Data Parallel (DDP)

• Scenario: Your model fits on one GPU, but your dataset is huge.
• Mechanism:
• We replicate the same model on 100 GPUs.
• We split the dataset into 100 chunks.
• Each GPU processes its chunk independently.
• The Sync: At the end of every step (backward pass), the GPUs talk to each other to average their "gradients" (what they learned). They then update their models identically.
• Pros: Near-linear scaling (100 GPUs = 90x speedup).
• Cons: The model must fit entirely in the VRAM of a single card.

B. Model Parallelism / FSDP / DeepSpeed

• Scenario: Your model (e.g., Llama-3-70B) is too big for one GPU.
• Mechanism:
• ZeRO (Zero Redundancy Optimizer): We slice the model itself. GPU 1 holds Layer 1-10, GPU 2 holds Layer 11-20.
• FSDP (Fully Sharded Data Parallel): We shard the model parameters, gradients, and optimizer states across all GPUs.
• Pros: Allows training of trillion-parameter models.
• Cons: Extremely high communication overhead. Requires fast interconnects (InfiniBand/NVLink).

2. The Hidden Bottleneck: Data Loading

In HPC, the GPUs are often so fast that they spend time waiting for the CPU to load images from the disk. This is IO Starvation.

• The Problem: Storing 1 million small JPEGs on a parallel file system (Lustre) is inefficient. Opening 1 million files creates a "Metadata Storm."
• The Fix:
• WebDataset / TFRecord: Pack those 1 million images into large 10GB "Tar" files (Shards). The CPU opens one big file and streams data sequentially.
• NVIDIA DALI: Move the image decoding (JPEG -> Pixel) from the CPU to the GPU.

3. Validation at Scale

Validation in HPC is tricky. If all 100 GPUs calculate accuracy on the same validation set, you are wasting resources.

• Distributed Sampler: Just like training, you split the validation set across GPUs.
• All-Gather: Each GPU calculates the accuracy for its chunk. Then, they use an all_gather command to send their results to the "Rank 0" (Master) node, which computes the final global accuracy.
• Checkpointing: HPC jobs have time limits (e.g., 24 hours). Your code must save a .pt checkpoint every hour. If the job is killed, you resubmit it, and it resumes from the last checkpoint automatically.

4. Key Applications & Tools

Category	Tool	Usage
Framework	PyTorch DDP	The standard for multi-GPU training. Native to PyTorch.
Scaling	DeepSpeed	Microsoft's library for training massive models (LLMs) that don't fit in memory.
	Horovod	An older but robust framework that works across PyTorch, TensorFlow, and Keras.
Orchestration	PyTorch Lightning	Removes the boilerplate. You change one flag (strategy="ddp"), and it handles the complex GPU syncing for you.
Container	Apptainer	You cannot use Docker (root access) on HPC. You convert Docker images to Apptainer (Singularity) images.
Tuning	Ray Tune	Runs Hyperparameter Optimization. It launches 50 different small jobs to find the best Learning Rate.