Why This Post Exists

When people decide to study GPU systems, many of them jump straight into CUDA syntax or Triton examples. Those things matter, but if you start too early at the code level, it is easy to follow the syntax without really understanding why a kernel is fast, why it is slow, or what the bottleneck actually is.

This series is about more than usage. It is about how GPUs really execute work, where bottlenecks appear, and how to reason about kernel performance. That makes it worth clarifying the prerequisites before diving in.

The Minimum Background You Really Want

1. Basic Python and PyTorch familiarity

You do not need to be a PyTorch expert, but some practical familiarity helps a lot. In real life, GPU performance questions usually show up while training or serving models, not in total isolation.

It helps if you are already comfortable with things like:

  • creating tensors and reading shapes
  • understanding a simple training loop
  • recognizing operators like matmul, softmax, and layernorm

You can study GPUs without PyTorch, but the motivation for many kernels will feel more abstract.

2. Basic linear algebra

GPU systems work keeps coming back to vectors and matrices. In particular, matrix multiplication intuition is close to essential.

At minimum, you should be comfortable with:

  • what vectors and matrices are
  • what matrix multiplication means structurally
  • how shapes line up for tensor operations

The important part is not doing advanced proofs. It is being able to picture the computation shape. That intuition makes thread mapping, tiling, and tensor core discussions much easier to follow.

3. Basic systems performance intuition

GPU systems is fundamentally a performance topic, so these ideas help a lot:

  • the difference between latency and throughput
  • what parallelism means
  • why memory hierarchy matters
  • why data movement can dominate arithmetic

You do not need to know all of this deeply on day one, but if every term is brand new, the series will feel much heavier.

4. Ability to read C/C++-style code

You do not need advanced C++. But it helps a lot if you can comfortably read:

  • loops
  • indexing-heavy code
  • conditionals
  • function calls in a low-level style

CUDA examples and extension code regularly use that shape of syntax. Reading matters more than writing at first.

Things That Help but Are Not Strictly Required

1. Basic computer architecture knowledge

If you already know a little about caches, registers, memory hierarchy, and SIMD-style thinking, GPU concepts connect faster. The hardware details differ, but the performance mindset carries over.

2. Basic deep learning training experience

If forward, backward, mixed precision, activation functions, and optimizer steps are already familiar, operators like softmax, layernorm, and attention kernels will feel much more concrete.

3. A profiling mindset

You do not need to know Nsight already. But it helps if you already think in terms of:

  • not just β€œdoes it work?”
  • but also β€œwhy is it slow?”

That attitude matters a lot in GPU study.

Things You Do Not Need First

These can help later, but they are not required before starting:

  • advanced compiler theory
  • deep operating systems internals
  • serious distributed systems knowledge
  • Triton experience
  • prior CUDA project experience

Those topics may connect naturally later, but they should not block you from starting.

Who Can Start Right Away?

You are probably ready if:

  • you have used Python and PyTorch a bit
  • tensor shapes and matrix multiplication are not foreign
  • performance bottlenecks and memory hierarchy are not completely new ideas
  • C-style indexing code does not immediately scare you

You may want more preparation first if:

  • tensor shapes still confuse you often
  • matmul or softmax still feel very vague
  • terms like throughput and memory hierarchy feel unfamiliar
  • indexing-heavy code is still hard to read

A Fast Preparation Plan

If you want a quick prep pass before the main series, this is enough:

  1. review PyTorch tensor shapes and matrix multiplication
  2. make sure you roughly know what softmax, layernorm, and attention do
  3. review memory hierarchy and latency vs throughput
  4. read a few simple C-style loop and indexing examples

That alone lowers the learning friction quite a lot.

How to Study This Series Well

This series is not really about memorizing CUDA syntax. The real goal is to be able to explain:

  • what bottleneck a kernel has
  • whether memory or compute is limiting it
  • why a certain optimization direction makes sense

So while reading, it helps to keep asking:

  • what is this operator computing?
  • where is the data moving?
  • how are threads and warps working on it?
  • where is the likely bottleneck?

If that becomes a habit, CUDA and Triton examples will feel much easier to retain.

The next post starts the actual series with the GPU thread model, warps, blocks, and memory hierarchy.