Cover Image

Benchmarking GPUs on SVBRDF Extractor Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kandel, Narayan, Lambert, Melanie
Format:	Preprint
Published:	2023
Subjects:	Performance Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs
by: Liu, Jiahui, et al.
Published: (2024)

Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs
by: Georganas, Evangelos, et al.
Published: (2025)

Characterizing and Understanding HGNN Training on GPUs
by: Han, Dengke, et al.
Published: (2024)

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs
by: Knoop, Jonathan, et al.
Published: (2026)

Profiling LoRA/QLoRA Fine-Tuning Efficiency on Consumer GPUs: An RTX 4060 Case Study
by: Avinash, MSR
Published: (2025)

Accelerating AI Performance using Anderson Extrapolation on GPUs
by: Dajani, Saleem Abdul Fattah Ahmed Al, et al.
Published: (2024)

Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial
by: Cortes, David, et al.
Published: (2025)

CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelines
by: Sun, Wenbo, et al.
Published: (2024)

Single-Thread JPEG Decoder Benchmarks Mis-Evaluate ML Data Loaders
by: Iglovikov, Vladimir, et al.
Published: (2026)

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels
by: Wang, Han, et al.
Published: (2026)

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
by: Gardner, Jason, et al.
Published: (2025)

Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis
by: Werner, Elias, et al.
Published: (2023)

Optimizing Attention on GPUs by Exploiting GPU Architectural NUMA Effects
by: Choudhary, Mansi, et al.
Published: (2025)

An Interpretable Latency Model for Speculative Decoding in LLM Serving
by: Kong, Linghao, et al.
Published: (2026)

Biases in Edge Language Models: Detection, Analysis, and Mitigation
by: Sharma, Vinamra, et al.
Published: (2025)

Memory Analysis on the Training Course of DeepSeek Models
by: Zhang, Ping, et al.
Published: (2025)

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

lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
by: Wang, Haoxin, et al.
Published: (2025)

PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
by: An, Zihao, et al.
Published: (2025)

GPU-Accelerated INT8 Quantization for KV Cache Compression in Large Language Models
by: Taneja, Maanas, et al.
Published: (2026)

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)

GreenServ: Energy-Efficient Context-Aware Dynamic Routing for Multi-Model LLM Inference
by: Ziller, Thomas, et al.
Published: (2026)

ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
by: Wang, Liangyu, et al.
Published: (2025)

Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept
by: Lansiaux, Edouard, et al.
Published: (2025)

Pushing the Performance Envelope of DNN-based Recommendation Systems Inference on GPUs
by: Jain, Rishabh, et al.
Published: (2024)

Minuet: Accelerating 3D Sparse Convolutions on GPUs
by: Yang, Jiacheng, et al.
Published: (2023)

LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
by: Liu, Jiashuo, et al.
Published: (2025)

Sig2Model: A Boosting-Driven Model for Updatable Learned Indexes
by: Heidari, Alireza, et al.
Published: (2025)

Approximating Heavy-Tailed Distributions with a Mixture of Bernstein Phase-Type and Hyperexponential Models
by: Ziani, Abdelhakim, et al.
Published: (2025)

LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2023)

BanditQ: Fair Bandits with Guaranteed Rewards
by: Sinha, Abhishek
Published: (2023)

SENSEi: Input-Sensitive Compilation for Accelerating GNNs
by: Lenadora, Damitha, et al.
Published: (2023)

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
by: Li, Jianhui, et al.
Published: (2023)

U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
by: Huang, Zhaolan, et al.
Published: (2023)

Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
by: Xu, Mingbin, et al.
Published: (2023)

A Scalable k-Medoids Clustering via Whale Optimization Algorithm
by: Chenan, Huang, et al.
Published: (2024)

CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs
by: Elshamy, Mohamed R., et al.
Published: (2025)

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing
by: Wang, Liangyu, et al.
Published: (2025)

Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
by: You, Bozhi, et al.
Published: (2025)

MarginGate: Sparse Margin-Triggered Verification for Batch-Invariant LLM Inference
by: Chu, Kexin, et al.
Published: (2026)