GPU Scaling from 1 to 261120 threads Part 2

2026-06-21 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, medium

Summary

GPU performance analysis for a 4096x4096 matrix multiplication on a single Streaming Multiprocessor (SM) reveals critical scaling behaviors when increasing warps from 1 to 32. Initial warp scaling from 1 to 4 warps shows near-ideal speedup, attributed to latency hiding. However, speedup plateaus at 16 warps (12.2x instead of 16x) and 32 warps (13.3x instead of 32x), with only a 9% improvement from 294ms to 269ms. This bottleneck is identified as L2 bandwidth saturation, with the single SM issuing L2 traffic at 58.8 GB/s, exceeding its ~5 GB/s allocation on the RTX 5090's shared 877 GB/s L2 fabric by 11x. The kernel is fundamentally memory-bound, spending 8 cycles in memory latency for every 1 cycle of compute at 32 warps, and L1TEX stalls remain at 52%. Occupancy, though reaching 33% at 16 warps, is necessary but not sufficient. Register usage (40,960 registers for 32 warps) also limits scaling. The analysis concludes that reducing memory traffic per output element through tiled matrix multiplication is essential.

Key takeaway

For AI Engineers optimizing GPU kernels, understand that increasing warps beyond a certain point may not yield proportional speedup due to L2 bandwidth saturation and register limits. If you are encountering performance plateaus, analyze L2 traffic and register usage to identify throughput bottlenecks. You should prioritize implementing techniques like tiled matrix multiplication to reduce memory traffic and increase arithmetic intensity, rather than solely relying on latency hiding through more warps. This shifts the bottleneck from memory latency to compute.

Key insights

GPU performance scaling plateaus due to L2 bandwidth saturation and memory-bound operations, despite latency hiding.

Principles

• Latency hiding improves performance by overlapping stalls.
• Occupancy is necessary but not sufficient for performance.
• Bottlenecks shift from latency to throughput under load.

In practice

• Analyze L2 bandwidth saturation for warp scaling.
• Monitor register usage to avoid occupancy ceilings.
• Prioritize arithmetic intensity for memory-bound kernels.

Topics

• GPU Performance
• Warp Scaling
• L2 Bandwidth
• Latency Hiding
• Matrix Multiplication
• Kernel Optimization
• Register Usage

Best for: Machine Learning Engineer, AI Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.