GPU kernels
Beyond Market Intelligence keeps GPU kernels in one place: 2 stories so far. The section currently leads with “GPU performance starts with CPU thinking: lessons from dependency chains and memory loops” and “Explore how Triton simplifies custom GPU kernels for faster ML workflows.”. A single accumulator creates a dependency chain, and most GPU performance ideas are just CPU ideas turned up. Triton shines when the bottleneck is memory-bound rather than compute-bound. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every GPU kernels story on Beyond Market Intelligence, newest first.
GPU performance starts with CPU thinking: lessons from dependency chains and memory loops
A single accumulator creates a dependency chain, and most GPU performance ideas are just CPU ideas turned up. This session skips the GPU code entirely to focus on CPU parallelism, which is refreshingly practical. The sorted-vs-random loop branch cost example is a standout: check what the compiler actually generated before blaming the hardware. For those exploring deeper context in coding, our related piece on "Smarter Context, Not More Context, Unlocks Smarter Coding Agents" complements this thinking well.
Explore how Triton simplifies custom GPU kernels for faster ML workflows.
Triton shines when the bottleneck is memory-bound rather than compute-bound. Operations like element-wise fusions, reductions, or attention that stall on data movement are prime candidates for custom kernels. Harshwardhan Fartale's early-access book gets this right: it focuses on practical acceleration, not theory. If you're stuck waiting on a stubborn layer, that's the signal to explore. For those new to kernel work, pairing this with broader distributed training context, like the guide on LLM training algorithms, helps frame when optimization matters most.