arXiv subjects

Deniz Altinbüken

Publications and source records attributed to Deniz Altinbüken.

3 recordsLinked to original sources

arXiv2026

ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers

This paper introduces ECO, a system that automatically modifies source code to improve performance at scale. ECO overcomes the localization problem by combining fleet-wide continuous profiling to identify performance-critical code with an embedding-based search to pinpoint specific optimization candidates, guided by a mined dictionary of performance anti-patterns. It overcomes the reliability problem through a multi-stage verification approach that uses automated testing, LLM-based self-review, and post-deployment monitoring to ensure changes are both correct and effective. Fully productionized and deployed within Google's hyperscale production fleet, ECO has successfully landed over 6,400 commits, changing more than 25,000 lines of production code. Incorrect changes are caught before they are submitted to production, and 99.5% of the submitted commits did not cause any rollbacks. These optimizations have resulted in savings equivalent to several hundred thousand normalized CPU cores, showing that ECO makes LLM-based optimization both practical at scale and highly impactful in real-world settings.

cs.SE↗

arXiv2026

ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution

The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for irregular data structures (such as sparse graphs, unbalanced trees, and non-uniform meshes) where static scheduling fails and data dependencies are unpredictable. Current Large Language Models (LLMs) often fail catastrophically on these tasks, generating code plagued by subtle race conditions, deadlocks, and sub-optimal scaling. We bridge this gap with ParEVO, a framework designed to synthesize high-performance parallel algorithms for irregular data. Our contributions include: (1) The Parlay-Instruct Corpus, a curated dataset of 13,820 tasks synthesized via a "Critic-Refine" pipeline that explicitly filters for empirically performant algorithms that effectively utilize Work-Span parallel primitives; (2) specialized DeepSeek, Qwen, and Gemini models fine-tuned to align probabilistic generation with the rigorous semantics of the ParlayLib library; and (3) an Evolutionary Coding Agent (ECA) that improves the "last mile" of correctness by iteratively repairing code using feedback from compilers, dynamic race detectors, and performance profilers. On the ParEval benchmark, ParEVO achieves an average 106x speedup (with a maximum of 1103x) across the suite, and a robust 13.6x speedup specifically on complex irregular graph problems, outperforming state-of-the-art commercial models. Furthermore, our evolutionary approach matches state-of-the-art expert human baselines, achieving up to a 4.1x speedup on specific highly-irregular kernels. Source code and datasets are available at https://github.com/WildAlg/ParEVO.

cs.LG↗

arXiv2023

Kepler: Robust Learning for Faster Parametric Query Optimization

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training an ML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.

cs.DB↗