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[2602.20204] Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler
Summary
This paper analyzes the effectiveness of latency hiding and parallelism techniques in an MLIR-based AI kernel compiler, focusing on vectorization, multi-threading, and double buffering.
Why It Matters
As AI applications increasingly demand efficient computation on edge devices, understanding how to optimize compiler strategies for latency and parallelism is crucial. This research provides insights into improving performance through advanced compilation techniques, which can benefit developers and researchers in AI and machine learning.
Key Takeaways
- Vectorization is the primary method for enhancing performance in bandwidth-sensitive kernels.
- Multi-threading offers significant speedup once scheduling overhead is managed.
- Double buffering can further optimize performance by overlapping data transfers with computation.
Computer Science > Programming Languages arXiv:2602.20204 (cs) [Submitted on 22 Feb 2026] Title:Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler Authors:Javed Absar, Samarth Narang, Muthu Baskaran View a PDF of the paper titled Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler, by Javed Absar and 1 other authors View PDF HTML (experimental) Abstract:AI kernel compilation for edge devices depends on the compiler's ability to exploit parallelism and hide memory latency in the presence of hierarchical memory and explicit data movement. This paper reports a benchmark methodology and corresponding results for three compiler-controlled mechanisms in an MLIR-based compilation pipeline: vectorization (Vec), multi-threading (MT) across hardware contexts, and double buffering (DB) using ping--pong scratchpad buffers to overlap DMA transfers with compute. Using Triton/Inductor-generated kernels, we present an ablation ladder that separates the contribution of Vec, MT, and DB, and we quantify how MT speedup scales with problem size using GELU as a representative activation kernel. The results show that vectorization provides the primary gain for bandwidth-sensitive kernels, MT delivers substantial improvements once scheduling overhead is amortized, and DB provides additional benefit when transfers and compute can be overlapped (i.e., outside the extremes of purely memory-bound or purely compute-bound behavior). Comments: Subjects:...
