Cover Image

Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Merouani, Massinissa, Bernou, Islem Kara, Baghdadi, Riyadh
Format:	Preprint
Published:	2025
Subjects:	Programming Languages Distributed, Parallel, and Cluster Computing Machine Learning Performance
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers
by: Merouani, Massinissa, et al.
Published: (2024)

Data-efficient Performance Modeling via Pre-training
by: Liu, Chunting, et al.
Published: (2025)

LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization
by: Merouani, Massinissa, et al.
Published: (2025)

Scheduling Languages: A Past, Present, and Future Taxonomy
by: Hall, Mary, et al.
Published: (2024)

OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms
by: Bhattacharjee, Arijit, et al.
Published: (2025)

Iterating Pointers: Enabling Static Analysis for Loop-based Pointers
by: Lepori, Andrea, et al.
Published: (2025)

Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning
by: Willemsen, Floris-Jan, et al.
Published: (2025)

AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism
by: Gupta, Ahan, et al.
Published: (2026)

pPython Performance Study
by: Byun, Chansup, et al.
Published: (2023)

ISO: Overlap of Computation and Communication within Seqenence For LLM Inference
by: Xiao, Bin, et al.
Published: (2024)

Toward Smart Scheduling in Tapis
by: Stubbs, Joe, et al.
Published: (2024)

LOOPRAG: Enhancing Loop Transformation Optimization with Retrieval-Augmented Large Language Models
by: Zhi, Yijie, et al.
Published: (2025)

Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents
by: Zhang, Qizheng, et al.
Published: (2025)

Less is More: Optimizing Function Calling for LLM Execution on Edge Devices
by: Paramanayakam, Varatheepan, et al.
Published: (2024)

GPU Cluster Scheduling for Network-Sensitive Deep Learning
by: Sharma, Aakash, et al.
Published: (2024)

AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence Inference
by: Zhao, Xuanlei, et al.
Published: (2024)

Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?
by: Dou, Xinglei, et al.
Published: (2025)

iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
by: Anik, Md Saidul Hoque, et al.
Published: (2024)

Comparing Parallel Functional Array Languages: Programming and Performance
by: van Balen, David, et al.
Published: (2025)

CoNST: Code Generator for Sparse Tensor Networks
by: Raje, Saurabh, et al.
Published: (2024)

Simplicity Scales
by: Sampson, Andrew, et al.
Published: (2026)

LEGO: A Layout Expression Language for Code Generation of Hierarchical Mapping
by: Tavakkoli, Amir Mohammad, et al.
Published: (2025)

pSTL-Bench: A Micro-Benchmark Suite for Assessing Scalability of C++ Parallel STL Implementations
by: Laso, Ruben, et al.
Published: (2024)

Towards a Linear-Algebraic Hypervisor
by: Considine, Breandan
Published: (2026)

Prompt-Aware Scheduling for Low-Latency LLM Serving
by: Tao, Yiheng, et al.
Published: (2025)

KVDirect: Distributed Disaggregated LLM Inference
by: Chen, Shiyang, et al.
Published: (2024)

EPIC: Efficient Position-Independent Caching for Serving Large Language Models
by: Hu, Junhao, et al.
Published: (2024)

PolyTOPS: Reconfigurable and Flexible Polyhedral Scheduler
by: Consolaro, Gianpietro, et al.
Published: (2024)

Glinthawk: A Two-Tiered Architecture for Offline LLM Inference
by: Hamadanian, Pouya, et al.
Published: (2025)

THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling
by: Muhammad, Said, et al.
Published: (2025)

TaxBreak: Unmasking the Hidden Costs of LLM Inference Through Overhead Decomposition
by: Vellaisamy, Prabhu, et al.
Published: (2026)

KVPR: Efficient LLM Inference with I/O-Aware KV Cache Partial Recomputation
by: Jiang, Chaoyi, et al.
Published: (2024)

cedar: Optimized and Unified Machine Learning Input Data Pipelines
by: Zhao, Mark, et al.
Published: (2024)

MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
by: Dutta, Akash, et al.
Published: (2024)

Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
by: Paramanayakam, Varatheepan, et al.
Published: (2025)

Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers
by: Maczan, Jędrzej
Published: (2026)

A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies
by: Korndörfer, Jonas H. Müller, et al.
Published: (2025)

Developing a Modular Compiler for a Subset of a C-like Language
by: Dutta, Debasish, et al.
Published: (2025)

Optimizing Agentic Language Model Inference via Speculative Tool Calls
by: Nichols, Daniel, et al.
Published: (2025)

Inductive Loop Analysis for Practical HPC Application Optimization
by: Schaad, Philipp, et al.
Published: (2025)