Forecasting GPU Performance for Deep Learning Training and Inference
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Seonho, Phanishayee, Amar, Mahajan, Divya |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Integrated Hardware Architecture and Device Placement Search
by: Wang, Irene, et al.
Published: (2024)
by: Wang, Irene, et al.
Published: (2024)
Anatomizing Deep Learning Inference in Web Browsers
by: Wang, Qipeng, et al.
Published: (2024)
by: Wang, Qipeng, et al.
Published: (2024)
Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
by: Lin, Zhongyi, et al.
Published: (2024)
by: Lin, Zhongyi, et al.
Published: (2024)
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
by: You, Bozhi, et al.
Published: (2025)
by: You, Bozhi, et al.
Published: (2025)
Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
by: Chrapek, Marcin, et al.
Published: (2025)
by: Chrapek, Marcin, et al.
Published: (2025)
PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU
by: McInroe, Trevor, et al.
Published: (2025)
by: McInroe, Trevor, et al.
Published: (2025)
xMem: A CPU-Based Approach for Accurate Estimation of GPU Memory in Deep Learning Training Workloads
by: Shi, Jiabo, et al.
Published: (2025)
by: Shi, Jiabo, et al.
Published: (2025)
GPU Cluster Scheduling for Network-Sensitive Deep Learning
by: Sharma, Aakash, et al.
Published: (2024)
by: Sharma, Aakash, et al.
Published: (2024)
An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference
by: Yao, Feiyu, et al.
Published: (2026)
by: Yao, Feiyu, et al.
Published: (2026)
An Inquiry into Datacenter TCO for LLM Inference with FP8
by: Kim, Jiwoo, et al.
Published: (2025)
by: Kim, Jiwoo, et al.
Published: (2025)
DEEP-GAP: Deep-learning Evaluation of Execution Parallelism in GPU Architectural Performance
by: Palaniappan, Kathiravan
Published: (2026)
by: Palaniappan, Kathiravan
Published: (2026)
Memory Analysis on the Training Course of DeepSeek Models
by: Zhang, Ping, et al.
Published: (2025)
by: Zhang, Ping, et al.
Published: (2025)
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
by: Li, Jianhui, et al.
Published: (2023)
by: Li, Jianhui, et al.
Published: (2023)
DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference
by: Holmes, Connor, et al.
Published: (2024)
by: Holmes, Connor, et al.
Published: (2024)
Efficient GPU implementation of randomized SVD and its applications
by: Struski, Łukasz, et al.
Published: (2021)
by: Struski, Łukasz, et al.
Published: (2021)
Forecasting LLM Inference Performance via Hardware-Agnostic Analytical Modeling
by: Patwari, Rajeev, et al.
Published: (2025)
by: Patwari, Rajeev, et al.
Published: (2025)
MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)
ModeSwitch-LLM: A Lightweight Phase-Aware Controller for Cross-Mode LLM Inference on a Single GPU
by: Sunesh, Aman, et al.
Published: (2026)
by: Sunesh, Aman, et al.
Published: (2026)
GPU-Accelerated INT8 Quantization for KV Cache Compression in Large Language Models
by: Taneja, Maanas, et al.
Published: (2026)
by: Taneja, Maanas, et al.
Published: (2026)
KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels
by: Wang, Han, et al.
Published: (2026)
by: Wang, Han, et al.
Published: (2026)
AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search
by: Jaber, Jaber, et al.
Published: (2026)
by: Jaber, Jaber, et al.
Published: (2026)
Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers
by: Maczan, Jędrzej
Published: (2026)
by: Maczan, Jędrzej
Published: (2026)
Tabular and Deep Reinforcement Learning for Gittins Index
by: Dhankhar, Harshit, et al.
Published: (2024)
by: Dhankhar, Harshit, et al.
Published: (2024)
Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach
by: Zhang, Yijia, et al.
Published: (2024)
by: Zhang, Yijia, et al.
Published: (2024)
USEFUSE: Uniform Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks
by: Ibrahim, Muhammad Sohail, et al.
Published: (2024)
by: Ibrahim, Muhammad Sohail, et al.
Published: (2024)
ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
by: Wang, Liangyu, et al.
Published: (2025)
by: Wang, Liangyu, et al.
Published: (2025)
Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
by: Li, Zhuojin, et al.
Published: (2025)
by: Li, Zhuojin, et al.
Published: (2025)
LLMPerf: GPU Performance Modeling meets Large Language Models
by: Nguyen, Khoi N. M., et al.
Published: (2025)
by: Nguyen, Khoi N. M., et al.
Published: (2025)
GCL-Sampler: Discovering Kernel Similarity for Sampled GPU Simulation via Graph Contrastive Learning
by: Wang, Jiaqi, et al.
Published: (2026)
by: Wang, Jiaqi, et al.
Published: (2026)
Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
by: Van Thanh, Nguyen, et al.
Published: (2025)
by: Van Thanh, Nguyen, et al.
Published: (2025)
Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
by: Gardner, Jason, et al.
Published: (2025)
by: Gardner, Jason, et al.
Published: (2025)
Feature Optimization for Time Series Forecasting via Novel Randomized Uphill Climbing
by: Van Thanh, Nguyen
Published: (2025)
by: Van Thanh, Nguyen
Published: (2025)
lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
by: Wang, Haoxin, et al.
Published: (2025)
by: Wang, Haoxin, et al.
Published: (2025)
Ragged Paged Attention: A High-Performance and Flexible LLM Inference Kernel for TPU
by: Jiang, Jevin, et al.
Published: (2026)
by: Jiang, Jevin, et al.
Published: (2026)
Quantum Neural Networks for Wind Energy Forecasting: A Comparative Study of Performance and Scalability with Classical Models
by: Hangun, Batuhan, et al.
Published: (2025)
by: Hangun, Batuhan, et al.
Published: (2025)
PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
by: An, Zihao, et al.
Published: (2025)
by: An, Zihao, et al.
Published: (2025)
MarginGate: Sparse Margin-Triggered Verification for Batch-Invariant LLM Inference
by: Chu, Kexin, et al.
Published: (2026)
by: Chu, Kexin, et al.
Published: (2026)
Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis
by: Werner, Elias, et al.
Published: (2023)
by: Werner, Elias, et al.
Published: (2023)
Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms
by: Shen, Jingran, et al.
Published: (2023)
by: Shen, Jingran, et al.
Published: (2023)
GreenServ: Energy-Efficient Context-Aware Dynamic Routing for Multi-Model LLM Inference
by: Ziller, Thomas, et al.
Published: (2026)
by: Ziller, Thomas, et al.
Published: (2026)
Similar Items
-
Integrated Hardware Architecture and Device Placement Search
by: Wang, Irene, et al.
Published: (2024) -
Anatomizing Deep Learning Inference in Web Browsers
by: Wang, Qipeng, et al.
Published: (2024) -
Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
by: Lin, Zhongyi, et al.
Published: (2024) -
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
by: You, Bozhi, et al.
Published: (2025) -
Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
by: Chrapek, Marcin, et al.
Published: (2025)