arXiv subjects

Zacharias Fisches

Publications and source records attributed to Zacharias Fisches.

2 recordsLinked to original sources

arXiv2025

Agentic Operator Generation for ML ASICs

We present TritorX, an agentic AI system designed to generate functionally correct Triton PyTorch ATen kernels at scale for emerging accelerator platforms. TritorX integrates open-source large language models with a custom linter, JIT compilation, and a PyTorch OpInfo-based test harness. This pipeline is compatible with both real Meta Training and Inference Accelerator (MTIA) silicon and in hardware simulation environments for next-generation devices. In contrast to previous kernel-generation approaches that prioritize performance for a limited set of high-usage kernels, TritorX prioritizes coverage. Our system emphasizes correctness and generality across the entire operator set, including diverse data types, shapes, and argument patterns. In our experiments, TritorX successfully generated kernels and wrappers for 481 unique ATen operators that pass all corresponding PyTorch OpInfo tests (over 20,000 in total). TritorX paves the way for overnight generation of complete PyTorch ATen backends for new accelerator platforms.

cs.DC↗

arXiv2020

Deep Data Flow Analysis

Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture, and other optimizations, is alluring. However, most machine learning methods cannot replicate even the simplest of the abstract interpretations of data flow analysis that are critical to making good optimization decisions. This must change for machine learning to become the dominant technology in compiler heuristics. To this end, we propose ProGraML - Program Graphs for Machine Learning - a language-independent, portable representation of whole-program semantics for deep learning. To benchmark current and future learning techniques for compiler analyses we introduce an open dataset of 461k Intermediate Representation (IR) files for LLVM, covering five source programming languages, and 15.4M corresponding data flow results. We formulate data flow analysis as an MPNN and show that, using ProGraML, standard analyses can be learned, yielding improved performance on downstream compiler optimization tasks.

cs.PL↗