Copertina

Can Large Language Models Write Parallel Code?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nichols, Daniel, Davis, Joshua H., Xie, Zhaojun, Rajaram, Arjun, Bhatele, Abhinav
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

HPC-Coder: Modeling Parallel Programs using Large Language Models
di: Nichols, Daniel, et al.
Pubblicazione: (2023)

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks
di: Davis, Joshua H., et al.
Pubblicazione: (2025)

Performance-Aligned LLMs for Generating Fast Code
di: Nichols, Daniel, et al.
Pubblicazione: (2024)

HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages
di: Chaturvedi, Aman, et al.
Pubblicazione: (2024)

Can Large Language Models Predict Parallel Code Performance?
di: Bolet, Gregory, et al.
Pubblicazione: (2025)

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

Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Training
di: Ranjan, Aditya K., et al.
Pubblicazione: (2025)

Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
di: Wei, Cunyang, et al.
Pubblicazione: (2026)

Automated Programmatic Performance Analysis of Parallel Programs
di: Cankur, Onur, et al.
Pubblicazione: (2024)

A 4D Hybrid Algorithm to Scale Parallel Training to Thousands of GPUs
di: Singh, Siddharth, et al.
Pubblicazione: (2023)

Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
di: Yang, Haowei, et al.
Pubblicazione: (2025)

KEET: Explaining Performance of GPU Kernels Using LLM Agents
di: Davis, Joshua H., et al.
Pubblicazione: (2026)

The Big Send-off: Scalable and Performant Collectives for Deep Learning
di: Singh, Siddharth, et al.
Pubblicazione: (2025)

Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
di: Zhu, Zhanda, et al.
Pubblicazione: (2025)

Integrating Performance Tools in Model Reasoning for GPU Kernel Optimization
di: Nichols, Daniel, et al.
Pubblicazione: (2025)

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation
di: Kaplan, Erel, et al.
Pubblicazione: (2026)

Taking GPU Programming Models to Task for Performance Portability
di: Davis, Joshua H., et al.
Pubblicazione: (2024)

Characterizing Production GPU Workloads using System-wide Telemetry Data
di: Cankur, Onur, et al.
Pubblicazione: (2025)

Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems
di: Lu, Ning, et al.
Pubblicazione: (2024)

ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems
di: Xu, Yiheng, et al.
Pubblicazione: (2023)

Scaling Performance of Large Language Model Pretraining
di: Interrante-Grant, Alexander, et al.
Pubblicazione: (2025)

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
di: Xie, Jincheng, et al.
Pubblicazione: (2026)

Hierarchical Autoscaling for Large Language Model Serving with Chiron
di: Patke, Archit, et al.
Pubblicazione: (2025)

AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure
di: The AIBrix Team, et al.
Pubblicazione: (2025)

Efficient Multi-Model Orchestration for Self-Hosted Large Language Models
di: Vangala, Bhanu Prakash, et al.
Pubblicazione: (2025)

SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
di: Kim, Han-Byul, et al.
Pubblicazione: (2025)

Large Language Model Partitioning for Low-Latency Inference at the Edge
di: Kafetzis, Dimitrios, et al.
Pubblicazione: (2025)

Equinox: Holistic Fair Scheduling in Serving Large Language Models
di: Wei, Zhixiang, et al.
Pubblicazione: (2025)

AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models
di: Guo, Jihu, et al.
Pubblicazione: (2025)

Accelerating Large Language Model Training with Hybrid GPU-based Compression
di: Xu, Lang, et al.
Pubblicazione: (2024)

Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
di: Xu, Lang, et al.
Pubblicazione: (2025)

Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
di: He, Zifan, et al.
Pubblicazione: (2026)

Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving
di: Li, Rui, et al.
Pubblicazione: (2025)

A Survey on Large Language Model Acceleration based on KV Cache Management
di: Li, Haoyang, et al.
Pubblicazione: (2024)

TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training
di: Han, Shujie, et al.
Pubblicazione: (2026)

TCM-Serve: Modality-aware Scheduling for Multimodal Large Language Model Inference
di: Papaioannou, Konstantinos, et al.
Pubblicazione: (2026)

SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting
di: Xu, Jiaming, et al.
Pubblicazione: (2025)

Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments
di: Jin, Yihong, et al.
Pubblicazione: (2025)

EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
di: Chen, Yanxi, et al.
Pubblicazione: (2023)

FreeRide: Harvesting Bubbles in Pipeline Parallelism
di: Zhang, Jiashu, et al.
Pubblicazione: (2024)