Cover Image

Analytics of Longitudinal System Monitoring Data for Performance Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Costello, Ian J., Bhatele, Abhinav
Format:	Preprint
Published:	2020
Subjects:	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

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

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

Pipit: Scripting the analysis of parallel execution traces
by: Bhatele, Abhinav, et al.
Published: (2023)

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

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

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

CloudFormer: An Attention-based Performance Prediction for Public Clouds with Unknown Workload
by: Shahbazinia, Amirhossein, et al.
Published: (2025)

Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation
by: Qin, Jianxing, et al.
Published: (2025)

A Practical Two-Stage Framework for GPU Resource and Power Prediction in Heterogeneous HPC Systems
by: Oztop, Beste, et al.
Published: (2026)

LLMPerf: GPU Performance Modeling meets Large Language Models
by: Nguyen, Khoi N. M., et al.
Published: (2025)

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

Fake Runs, Real Fixes -- Analyzing xPU Performance Through Simulation
by: Zarkadas, Ioannis, et al.
Published: (2025)

Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
by: Lin, Zhongyi, et al.
Published: (2024)

ReLATE: Learning Efficient Sparse Encoding for High-Performance Tensor Decomposition
by: Helal, Ahmed E., et al.
Published: (2025)

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
by: Sridharan, Srinivas, et al.
Published: (2026)

DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
by: Mumenin, Khondoker Mirazul, et al.
Published: (2025)

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

MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models
by: La, Hoa, et al.
Published: (2025)

BestServe: Serving Strategies with Optimal Goodput in Collocation and Disaggregation Architectures
by: Hu, Xiannan, et al.
Published: (2025)

InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update
by: Wu, Dan, et al.
Published: (2023)

Vectorized FlashAttention with Low-cost Exponential Computation in RISC-V Vector Processors
by: Titopoulos, Vasileios, et al.
Published: (2025)

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

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

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

xMem: A CPU-Based Approach for Accurate Estimation of GPU Memory in Deep Learning Training Workloads
by: Shi, Jiabo, et al.
Published: (2025)

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

When Less is More: Achieving Faster Convergence in Distributed Edge Machine Learning
by: Basani, Advik Raj, et al.
Published: (2024)

IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
by: Ghafouri, Saeid, et al.
Published: (2023)

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

CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference
by: Xu, Guanyu, et al.
Published: (2025)

Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
by: Li, Zhuojin, et al.
Published: (2025)

Multi-DNN Inference of Sparse Models on Edge SoCs
by: Luo, Jiawei, et al.
Published: (2026)

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

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

Execution time budget assignment for mixed criticality systems
by: Khelassi, Mohamed Amine, et al.
Published: (2023)

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

Distributed Matrix-Based Sampling for Graph Neural Network Training
by: Tripathy, Alok, et al.
Published: (2023)

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

MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs
by: Sarkar, Aishwarya, et al.
Published: (2024)

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