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ORCIDYida Wang 0003
Person information
- affiliation: Amazon Web Services, Inc., East Palo Alto, CA, USA
- affiliation: Intel Corporation, Parallel Computing Lab, Santa Clara, CA, USA
- affiliation: Princeton University, Department of Computer Science, NJ, USA
Other persons with the same name
- Yida Wang — disambiguation page
- Yida Wang 0001
![[0000-0003-4519-9108]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0003-4519-9108")
— Technical University of Munich, CAMP, Germany (and 1 more) - Yida Wang 0002 — Nanyang Technological University, School of Computer Engineering, Centre for Advanced Information Systems, Singapore
- Yida Wang 0004 — Army Engineering University of PLA, College of Communications Engineering, Nanjing, China
- Yida Wang 0005 — Beihang University, School of Computer Science and Engineering, Beijing, China
- Yida Wang 0006 — Jilin University, Key Laboratory of Geophysical Exploration Equipment, Changchun, China
- Yida Wang 0007 — Shanghai Jiao Tong University, Shanghai, China
- Yida Wang 0008 — Chinese Academy of Sciences, CSSAR, China (and 1 more)
- Yida Wang 0009 — Tsinghua University, Tsinghua-Berkeley Shenzhen Institute, CoAI group, DCST, Institute for Artificial Intelligence, China
- Yida Wang 0010 — East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, China
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2020 – today
- 2024
[j3]Xueying Wang, Guangli Li, Zhen Jia, Xiaobing Feng, Yida Wang:
Fast Convolution Meets Low Precision: Exploring Efficient Quantized Winograd Convolution on Modern CPUs. ACM Trans. Archit. Code Optim. 21(1): 5:1-5:26 (2024)- [c20]Hongzheng Chen, Cody Hao Yu, Shuai Zheng, Zhen Zhang, Zhiru Zhang, Yida Wang:
Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training. ASPLOS (2) 2024: 1095-1111 - [c19]Chenyu Jiang, Zhen Jia, Shuai Zheng, Yida Wang, Chuan Wu:
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines. EuroSys 2024: 542-559 - [c18]Jun Huang, Zhen Zhang, Shuai Zheng, Feng Qin, Yida Wang:
DISTMM: Accelerating Distributed Multimodal Model Training. NSDI 2024 - [i17]Haozheng Fan, Hao Zhou, Guangtai Huang, Parameswaran Raman, Xinwei Fu, Gaurav Gupta, Dhananjay Ram, Yida Wang, Jun Huan:
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium. CoRR abs/2404.10630 (2024) - [i16]Chenyu Jiang, Ye Tian, Zhen Jia, Shuai Zheng, Chuan Wu, Yida Wang:
Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping. CoRR abs/2404.19429 (2024) - 2023
- [j2]Y. Peeta Li, Yida Wang, Nicholas B. Turk-Browne, Brice A. Kuhl, J. Benjamin Hutchinson:
Perception and memory retrieval states are reflected in distributed patterns of background functional connectivity. NeuroImage 276: 120221 (2023) - [c17]Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu, Yida Wang, Gennady Pekhimenko:
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs. ASPLOS (2) 2023: 370-384 - [c16]Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan:
Training Large-scale Foundation Models on Emerging AI Chips. KDD 2023: 5821-5822 - [c15]Bojian Zheng, Cody Hao Yu, Jie Wang, Yaoyao Ding, Yizhi Liu, Yida Wang, Gennady Pekhimenko:
Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs. MICRO 2023: 1364-1380 - [c14]Zhuang Wang, Zhen Jia, Shuai Zheng, Zhen Zhang, Xinwei Fu, T. S. Eugene Ng, Yida Wang:
GEMINI: Fast Failure Recovery in Distributed Training with In-Memory Checkpoints. SOSP 2023: 364-381 - [i15]Hongzheng Chen, Cody Hao Yu, Shuai Zheng, Zhen Zhang, Zhiru Zhang, Yida Wang:
Decoupled Model Schedule for Deep Learning Training. CoRR abs/2302.08005 (2023) - [i14]Cody Hao Yu, Haozheng Fan, Guangtai Huang, Zhen Jia, Yizhi Liu, Jie Wang, Zach Zheng, Yuan Zhou, Haichen Shen, Junru Shao, Mu Li, Yida Wang:
RAF: Holistic Compilation for Deep Learning Model Training. CoRR abs/2303.04759 (2023) - [i13]Chenyu Jiang, Zhen Jia, Shuai Zheng, Yida Wang, Chuan Wu:
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines. CoRR abs/2311.10418 (2023) - 2022
- [j1]Zhen Zhang, Shuai Zheng, Yida Wang, Justin Chiu, George Karypis, Trishul Chilimbi, Mu Li, Xin Jin:
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. Proc. VLDB Endow. 16(1): 37-50 (2022) - [c13]Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, Gennady Pekhimenko:
DietCode: Automatic Optimization for Dynamic Tensor Programs. MLSys 2022 - [i12]Zhen Zhang, Shuai Zheng, Yida Wang, Justin Chiu, George Karypis, Trishul Chilimbi, Mu Li, Xin Jin:
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. CoRR abs/2205.00119 (2022) - [i11]Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu, Yida Wang, Gennady Pekhimenko:
Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs. CoRR abs/2210.09603 (2022) - 2021
- [c12]Jian Weng, Animesh Jain, Jie Wang, Leyuan Wang, Yida Wang, Tony Nowatzki:
UNIT: Unifying Tensorized Instruction Compilation. CGO 2021: 77-89 - [c11]Cody Hao Yu, Xingjian Shi, Haichen Shen, Zhi Chen, Mu Li, Yida Wang:
Lorien: Efficient Deep Learning Workloads Delivery. SoCC 2021: 18-32 - [c10]Guangli Li, Zhen Jia, Xiaobing Feng, Yida Wang:
LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs. ICPP 2021: 81:1-81:11 - [c9]Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang:
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. MLSys 2021 - [i10]Jian Weng, Animesh Jain, Jie Wang, Leyuan Wang, Yida Wang, Tony Nowatzki:
UNIT: Unifying Tensorized Instruction Compilation. CoRR abs/2101.08458 (2021) - [i9]Zhi Chen, Cody Hao Yu, Trevor Morris, Jorn Tuyls, Yi-Hsiang Lai, Jared Roesch, Elliott Delaye, Vin Sharma, Yida Wang:
Bring Your Own Codegen to Deep Learning Compiler. CoRR abs/2105.03215 (2021) - 2020
- [c8]Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica:
Ansor: Generating High-Performance Tensor Programs for Deep Learning. OSDI 2020: 863-879 - [c7]Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang:
FeatGraph: a flexible and efficient backend for graph neural network systems. SC 2020: 71 - [i8]Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang:
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. CoRR abs/2006.03031 (2020) - [i7]Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica:
Ansor : Generating High-Performance Tensor Programs for Deep Learning. CoRR abs/2006.06762 (2020) - [i6]Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda, Vin Sharma, Yida Wang:
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach. CoRR abs/2006.10226 (2020) - [i5]Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang:
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems. CoRR abs/2008.11359 (2020)
2010 – 2019
- 2019
- [c6]Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang:
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. ICPP 2019: 99:1-99:10 - [c5]Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang:
Optimizing CNN Model Inference on CPUs. USENIX Annual Technical Conference 2019: 1025-1040 - [i4]Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang:
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. CoRR abs/1907.02154 (2019) - 2018
- [i3]Linpeng Tang, Yida Wang, Theodore L. Willke, Kai Li:
Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs. CoRR abs/1807.09667 (2018) - [i2]Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang:
Optimizing CNN Model Inference on CPUs. CoRR abs/1809.02697 (2018) - 2017
- [c4]Dipanjan Sengupta, Yida Wang, Narayanan Sundaram, Theodore L. Willke:
High-Performance Incremental SVM Learning on Intel® Xeon Phi™ Processors. ISC 2017: 120-138 - 2016
- [c3]Michael J. Anderson, Mihai Capota, Javier S. Turek, Xia Zhu, Theodore L. Willke, Yida Wang, Po-Hsuan Chen, Jeremy R. Manning, Peter J. Ramadge, Kenneth A. Norman:
Enabling factor analysis on thousand-subject neuroimaging datasets. IEEE BigData 2016: 1151-1160 - [c2]Yida Wang, Bryn Keller, Mihai Capota, Michael J. Anderson, Narayanan Sundaram, Jonathan D. Cohen, Kai Li, Nicholas B. Turk-Browne, Theodore L. Willke:
Real-time full correlation matrix analysis of fMRI data. IEEE BigData 2016: 1242-1251 - [i1]Michael J. Anderson, Mihai Capota, Javier S. Turek, Xia Zhu, Theodore L. Willke, Yida Wang, Po-Hsuan Chen, Jeremy R. Manning, Peter J. Ramadge, Kenneth A. Norman:
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets. CoRR abs/1608.04647 (2016) - 2015
- [c1]Yida Wang, Michael J. Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Nadathur Satish, Narayanan Sundaram, Nicholas B. Turk-Browne, Theodore L. Willke:
Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. SC 2015: 23:1-23:12
Coauthor Index
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last updated on 2024-05-28 23:17 CEST by the dblp team
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