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Reword and move dispatch around
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blog/2024-11-21-optimizing-matrix-mul/index.md

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@@ -88,6 +88,22 @@ platforms, including Windows, Linux, macOS, iOS[^1], Android, and the web[^2].
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By using Rust GPU and `wgpu`, we have a clean, portable setup with everything written in
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Rust.
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## GPU program basics
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The smallest unit of execution is a thread, which executes the GPU program.
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Workgroups are groups of threads: they are grouped together and run in parallel (they’re
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called [thread blocks in
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CUDA](<https://en.wikipedia.org/wiki/Thread_block_(CUDA_programming)>)). They can access
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the same shared memory.
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We can dispatch many of these workgroups at once. CUDA calls this a grid (which is made
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of thread blocks).
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Workgroups and dispatching workgroups are defined in 3D. The size of a workgroup is
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defined by `compute(threads((x, y, z)))` where the number of threads per workgroup is
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x \* y \* z.
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## Writing the kernel
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### Kernel 1: Naive kernel
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:::
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Each workgroup, since it’s only one thread, processes one `result[i, j]`.
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To calculate the full matrix, we need to launch as many entries as there are in the
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matrix. Here we specify that (`Uvec3::new(m * n, 1, 1`) on the CPU:
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import { RustNaiveWorkgroupCount } from './snippets/naive.tsx';
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<RustNaiveWorkgroupCount/>
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The `dispatch_count()` function runs on the CPU and is used by the CPU-to-GPU API (in
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our case `wgpu`) to configure and dispatch work to the GPU:
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import { RustNaiveDispatch } from './snippets/naive.tsx';
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<RustNaiveDispatch />
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:::warning
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This code appears more complicated than it needs to be. I abstracted the CPU-side code
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that talks to the GPU using generics and traits so I could easily slot in different
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kernels and their settings while writing the blog post.
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You could just hardcode the value for simplicity.
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:::
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### Kernel 2: Moarrr threads!
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With the first kernel, we're only able to compute small square matrices due to limits on
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<RustWorkgroup256WorkgroupCount/>
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The `dispatch_count()` function runs on the CPU and is used by the CPU-to-GPU API (in
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our case `wgpu`) to configure and dispatch to the GPU:
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import { RustWorkgroup256WgpuDispatch } from './snippets/workgroup_256.tsx';
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<RustWorkgroup256WgpuDispatch />
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:::warning
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This code appears more complicated than it needs to be. I abstracted the CPU-side code
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that talks to the GPU using generics and traits so I could easily slot in different
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kernels and their settings while writing the blog post.
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You could just hardcode a value for simplicity.
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:::
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With these two small changes we can handle larger matrices without hitting hardware
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workgroup limits.
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### Kernel 3: Calculating with 2D workgroups
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However doing all the computation in "1 dimension" limits the matrix size we can
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However, doing all the computation in "1 dimension" still limits the matrix size we can
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calculate.
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Although we don't change much about our code, if we distribute our work in 2 dimensions
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Each thread now calculates a 4x4 grid of the output matrix and we see a slight
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improvement over the last kernel.
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To stay true to the spirit of Zach's original blog post, we'll wrap things up here and
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leave the "fancier" experiments for another time.
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## Reflections on porting to Rust GPU
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Porting to Rust GPU went quickly, as the kernels Zach used were fairly simple. Most of
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the time was spent with concerns that were not specifically about writing GPU code. For
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example, deciding how much to abstract vs how much to make the code easy to follow, if
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everything should be available at runtime or if each kernel should be a compilation
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target, etc. The code is not _great_ as it is still blog post code!
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target, etc. [The
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code](https://github.com/Rust-GPU/rust-gpu.github.io/tree/main/blog/2024-11-21-optimizing-matrix-mul/code)
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is not _great_ as it is still blog post code!
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My background is not in GPU programming, but I do have Rust experience. I joined the
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Rust GPU project because I tried to use standard GPU languages and knew there must be a
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better way. Writing these GPU kernels felt like writing any other Rust code (other than
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debugging, more on that later) which is a huge win to me. Not only the language itself,
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debugging, more on that later) which is a huge win to me. Not just the language itself,
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but the entire development experience.
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## Rust-specific party tricks
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Rust lets us write code for both the CPU and GPU in ways that are often impossible—or at
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least less elegant—with other languages. I'm going to highlight some benefits of Rust I
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least less elegant—with other languages. I'm going to highlight some benefits I
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experienced while working on this blog post.
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### Shared code across GPU and CPU
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it with multiple invocations across workgroups and dispatches. To test the kernel
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end-to-end, I needed a test harness that simulated this behavior on the CPU.
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Building the harness was straightforward. By enforcing the same invariants as the GPU I
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could validate the kernel under the same conditions the GPU would run it:
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Building the harness was straightforward due to the borrow checker. By enforcing the
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same invariants as the GPU I could validate the kernel under the same conditions the GPU
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would run it:
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import { RustCpuBackendHarness } from './snippets/party.tsx';
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This kernel doesn't use conditional compilation, but it's a key feature of Rust that
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works with Rust GPU. With `#[cfg(...)]`, you can adapt kernels to different hardware or
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configurations without duplicating code. GPU languages like WGSL or GLSL offer
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preprocessor directives, but these tools lack standardization across ecosystems. Rust
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GPU leverages the existing Cargo ecosystem, so conditional compilation follows the same
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standards all Rust developers already know. This makes adapting kernels for different
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targets easier and more maintainable.
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preprocessor directives, but these tools lack standardization across projects. Rust GPU
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leverages the existing Cargo ecosystem, so conditional compilation follows the same
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standards all Rust developers already know.
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## Come join us!
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blog/2024-11-21-optimizing-matrix-mul/snippets/naive.tsx

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import CodeBlock from "@theme/CodeBlock";
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import Snippet from "@site/src/components/Snippet";
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import RustKernelSource from "!!raw-loader!../code/crates/gpu/naive/src/lib.rs";
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import RustWorkgroupCount from "!!raw-loader!../code/crates/cpu/matmul/src/variants.rs";
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import RustWgpuBackend from "!!raw-loader!../code/crates/cpu/matmul/src/backends/wgpu.rs";
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export const WebGpuInputs: React.FC = () => (
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<CodeBlock language="wgsl" title="WGSL" className="text-xs">
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</Snippet>
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);
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export const RustNaiveWorkgroupCount: React.FC = () => (
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<Snippet
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language="rust"
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className="text-xs"
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lines="26-34"
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title="Calculating how many workgroup dispatches are needed on the CPU"
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>
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{RustWorkgroupCount}
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</Snippet>
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);
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export const RustNaiveDispatch: React.FC = () => (
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<Snippet
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language="rust"
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className="text-xs"
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lines="145,147"
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strip_leading_spaces
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title="Using wgpu on the CPU to dispatch to the GPU"
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>
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{RustWgpuBackend}
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</Snippet>
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);
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export const RustNaiveWorkgroup: React.FC = () => (
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<Snippet language="rust" className="text-xs" lines="7">
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{RustKernelSource}

blog/2024-11-21-optimizing-matrix-mul/snippets/workgroup_256.tsx

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@@ -2,7 +2,6 @@ import React from "react";
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import Snippet from "@site/src/components/Snippet";
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import RustKernelSource from "!!raw-loader!../code/crates/gpu/workgroup_256/src/lib.rs";
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import VariantsSource from "!!raw-loader!../code/crates/cpu/matmul/src/variants.rs";
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import WgpuBackendSource from "!!raw-loader!../code/crates/cpu/matmul/src/backends/wgpu.rs";
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export const RustWorkgroup256Workgroup: React.FC = () => (
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<Snippet language="rust" className="text-xs" lines="7">
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{VariantsSource}
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</Snippet>
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);
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export const RustWorkgroup256WgpuDispatch: React.FC = () => (
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<Snippet
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language="rust"
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className="text-xs"
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lines="144,145,147"
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strip_leading_spaces
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title="Using wgpu on the CPU to dispatch to the GPU"
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>
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{WgpuBackendSource}
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</Snippet>
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);

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