Distributed Systems Foundations

Distributed Systems Foundations

What it is

A distributed system is a collection of independent computers that appear to the user as a single coherent system. In the context of AI, this means moving from a single GPU or server to a cluster of machines working together to train or serve a model. The core idea is that computation is spread across a network to handle workloads that are too large for one machine.

Why it matters for AI

Modern AI infrastructure, especially Large Language Models (LLMs), cannot fit on a single chip. To train these models, data and weights are partitioned across thousands of GPUs. If you apply a "single-process" mental model, you assume that memory is instant and reliable. In reality, moving data between machines introduces delays and risks that can crash a training run or cause massive performance bottlenecks.

Core Concepts

• Latency: The time it takes for data to travel between nodes. In AI, network latency often becomes the primary bottleneck, making the system wait for data rather than computing.
• Partial Failure: Unlike a single computer that either works or crashes, a distributed system can be "half-broken." One node may fail while others continue, or a network switch may drop packets, leading to unpredictable system states.
• The Eight Fallacies: These are common false assumptions developers make, such as believing the network is reliable, latency is zero, bandwidth is infinite, or the topology never changes.
• Consistency vs. Availability: The trade-off between ensuring every node has the exact same data at the same time versus ensuring the system remains responsive even when some nodes are lagging.

Common Mistakes

• Ignoring the Network: Assuming a remote function call is as fast as a local one.
• Assuming Synchronicity: Expecting all machines to reach a certain state at the exact same moment without explicit coordination.
• Overlooking Edge Cases: Failing to write logic for what happens when one machine in a 1,000-node cluster stops responding.

Track Connection

These foundations provide the necessary constraints for the rest of the Frontier Systems track. Understanding these failures is prerequisite to mastering parallelization strategies (Data, Model, and Pipeline parallelism) and designing scalable inference engines.