arXiv subjects

Mohammad Zaeed

Publications and source records attributed to Mohammad Zaeed.

5 recordsLinked to original sources

arXiv2026

Optimas: An Intelligent Analytics-Informed Generative AI Framework for Performance Optimization

Large language models (LLMs) show promise for automated code optimization. However, without performance context, they struggle to produce correct and effective code transformations. Existing performance tools can identify bottlenecks but stop short of generating actionable code changes. Consequently, performance optimization continues to be a time-intensive and manual endeavor, typically undertaken only by experts with detailed architectural understanding. To bridge this gap, we introduce Optimas, a modular, fully automated, end-to-end generative AI framework built on a multi-agent workflow. Optimas uses LLMs to map performance diagnostics from multiple reports to established, literature-backed code transformations, while unifying insight extraction, code generation, execution, and validation within a single pipeline. Across 3,410 real-world experiments on 10 benchmarks and two HPC mini-applications, Optimas generates 100% correct code and improves performance in over 98.82% of those experiments, achieving average gains of 8.02%-79.09% on NVIDIA GPUs.

cs.PF↗

arXiv2026

COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC

HPC systems expose many configuration parameters that jointly drive competing objectives. Existing tools such as autotuners recommend good configurations but do not identify minimal changes for a near-miss configuration to meet a performance objective, and they often ignore domain-specific constraints. To address this gap, we introduce COMPASS -- a modular, programmable engine that uses operational traces to generate HPC configuration recommendations and guide tuning decisions. This paper: (1) formalizes configuration questions into query patterns; (2) develops an interactive decision-making engine that formulates these queries as Machine Learning (ML) tasks; (3) quantifies the trustworthiness of its recommendations by providing evidence and quantifying uncertainty, and -- when confidence is low -- provides guidance on which configurations to run next. We validate COMPASS using analytical ground truth, reconstruction accuracy, reproduction of published findings, and when possible, running on real hardware. When integrated with an open-source HPC scheduling simulator, COMPASS cuts average job turnaround time by 65.93% and node usage by 80.93% relative to the state-of-the-art. Moreover, COMPASS achieves up to 100x faster training and 80x faster inference than state-of-the-art generative methods, and scales to traces with 1.3B samples and 126GB of data.

cs.PF↗

arXiv2025

A Distributed Framework for Causal Modeling of Performance Variability in GPU Traces

Large-scale GPU traces play a critical role in identifying performance bottlenecks within heterogeneous High-Performance Computing (HPC) architectures. However, the sheer volume and complexity of a single trace of data make performance analysis both computationally expensive and time-consuming. To address this challenge, we present an end-to-end parallel performance analysis framework designed to handle multiple large-scale GPU traces efficiently. Our proposed framework partitions and processes trace data concurrently and employs causal graph methods and parallel coordinating chart to expose performance variability and dependencies across execution flows. Experimental results demonstrate a 67% improvement in terms of scalability, highlighting the effectiveness of our pipeline for analyzing multiple traces independently.

cs.DC↗

arXiv2025

Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs

Large Language Models (LLMs) show promise for automated code optimization but struggle without performance context. This work introduces Opal, a modular framework that connects performance analytics insights with the vast body of published by guiding LLMs to generate informed, trustworthy optimizations. Unlike traditional performance tools that identify bottlenecks but stop short of actionable suggestions, Opal bridges this long-standing gap by linking dynamic insights from hardware counters and Roofline analysis to stall events to optimization decisions. We evaluate Opal across 1640 experiments on real-world GPU kernels and find that in over 98.5% of cases, even a single insight source yields speedups, ranging on average from 19.34% to 52.3%. Our prompt template produced correct code in all but one case, where a vague diagnostic caused an unsafe suggestion. By automatically optimizing GPU kernels using performance analytics and LLMs, Opal marks a leap toward democratizing expert-level performance engineering for all.

cs.PF↗

arXiv2024

Data-Driven Analysis to Understand GPU Hardware Resource Usage of Optimizations

With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money for expensive ccelerators. While related work develops optimizations for improving application performance, none studies how these optimizations impact hardware resource usage or the average GPU utilization. This paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multi-objective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and finally (4) identifying optimization opportunities for several scientific proxy applications based on their hardware resource usage behaviors. Furthermore, we demonstrate the applicability of our methodology by applying the identified optimizations to a proxy application, which improves the execution time, device utilization and power consumption by up to 29.6%, 5.3% and 26.5% respectively.

cs.DC↗