ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2305.14516
  4. Cited By
Chakra: Advancing Performance Benchmarking and Co-design using
  Standardized Execution Traces
v1v2 (latest)

Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

23 May 2023
Srinivas Sridharan
Taekyung Heo
Louis Feng
Zhaodong Wang
M. Bergeron
Wenyin Fu
Shengbao Zheng
Brian Coutinho
Saeed Rashidi
Changhai Man
T. Krishna
ArXiv (abs)PDFHTML

Papers citing "Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces"

8 / 8 papers shown
Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Seokjin Go
Joongun Park
Spandan More
Hanjiang Wu
Irene Wang
Aaron Jezghani
Tushar Krishna
Divya Mahajan
209
2
0
12 Sep 2025
Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
Jinsun Yoo
ChonLam Lao
Lianjie Cao
Bob Lantz
Minlan Yu
Tushar Krishna
Puneet Sharma
194
0
0
29 Apr 2025
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph GenerationInternational Conference on Learning Representations (ICLR), 2024
Mufei Li
Viraj Shitole
Eli Chien
Changhai Man
Zhaodong Wang
Srinivas Sridharan
Ying Zhang
Tushar Krishna
P. Li
341
5
0
04 Nov 2024
Towards a Standardized Representation for Deep Learning Collective
  Algorithms
Towards a Standardized Representation for Deep Learning Collective AlgorithmsIEEE Symposium on High-Performance Interconnects (HOTI), 2024
Jinsun Yoo
William Won
Meghan Cowan
Nan Jiang
Benjamin Klenk
Srinivas Sridharan
Tushar Krishna
315
3
0
20 Aug 2024
LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference
  Serving at Scale
LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at ScaleIEEE International Symposium on Workload Characterization (IISWC), 2024
Jaehong Cho
Minsu Kim
Hyunmin Choi
Guseul Heo
Jongse Park
353
22
0
10 Aug 2024
Towards Universal Performance Modeling for Machine Learning Training on
  Multi-GPU Platforms
Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
Zhongyi Lin
Ning Sun
Pallab Bhattacharya
Xizhou Feng
Louis Feng
John Douglas Owens
249
4
0
19 Apr 2024
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model
  Acceleration on Distributed Systems
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed SystemsInternational Symposium on Computer Architecture (ISCA), 2023
Samuel Hsia
Alicia Golden
Bilge Acun
Newsha Ardalani
Zach DeVito
Gu-Yeon Wei
David Brooks
Carole-Jean Wu
MoE
320
14
0
04 Oct 2023
Proteus: Simulating the Performance of Distributed DNN Training
Proteus: Simulating the Performance of Distributed DNN TrainingIEEE Transactions on Parallel and Distributed Systems (TPDS), 2023
Jiangfei Duan
Xiuhong Li
Ping Xu
Xingcheng Zhang
Shengen Yan
Yun Liang
Dahua Lin
213
13
0
04 Jun 2023
1