arXiv subjects

Javed Absar

Publications and source records attributed to Javed Absar.

3 recordsLinked to original sources

arXiv2026

Reading AI Model Compilation in MLIR Through the Lens of Formal Theories

Compiler infrastructures such as MLIR rest on a set of design principles: IR abstractions, interfaces, match-and-rewrite, flow analysis, type conversion, staged lowering, and so on. These concepts have proven themselves in practice. Good designs typically arrive through engineering knowledge, intuition and experience. Many of them, however, have correspondences in formal theory. MLIR's match-and-rewrite engine has correspondence to a \emph{term-rewriting-system}~\cite{baadernipkow1998}; staged lowering has the structure of \emph{refinement calculus}~\cite{back1998}; and range analysis is grounded in \emph{abstract interpretation}~\cite{cousot1977,cousot1979}. Highlighting these correspondences is useful because each theory supplies vocabulary precise enough to discuss structural questions. Moreover, as coding agents lower the cost of implementation, good design and abstractions become the main concern~\cite{Lattner2026ClaudeCCompiler}. A coding agent can generate a pass, but it can only reason over the semantics the representation exposes. When essential structure is missing, the limitation is one of abstraction, not of implementation. The natural next question is how to design that substrate well. Well-chosen abstractions emerge from experience and intuition, but they often mirror concepts given a more precise treatment in formal theory. We argue that knowledge of these formal concepts clarifies what completeness means for a given abstraction, what the ideal design would be, and where practical trade-offs depart from it.

cs.PL↗

arXiv2026

Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler

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).

cs.PL↗

arXiv2025

Tensor Evolution: A Framework for Fast Evaluation of Tensor Computations using Recurrences

This paper introduces a new mathematical framework for analysis and optimization of tensor expressions within an enclosing loop. Tensors are multi-dimensional arrays of values. They are common in high performance computing (HPC) and machine learning domains. Our framework extends Scalar Evolution - an important optimization pass implemented in both LLVM and GCC - to tensors. Scalar Evolution (SCEV) relies on the theory of `Chain of Recurrences' for its mathematical underpinnings. We use the same theory for Tensor Evolution (TeV). While some concepts from SCEV map easily to TeV -- e.g. element-wise operations; tensors introduce new operations such as concatenation, slicing, broadcast, reduction, and reshape which have no equivalent in scalars and SCEV. Not all computations are amenable to TeV analysis but it can play a part in the optimization and analysis parts of ML and HPC compilers. Also, for many mathematical/compiler ideas, applications may go beyond what was initially envisioned, once others build on it and take it further. We hope for a similar trajectory for the tensor-evolution concept.

cs.PL↗