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2020 – today
- 2024
[c18]Kun Wu
, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei Hwu:
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures. ASPLOS (3) 2024: 528-544- [c17]Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis:
GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. KDD 2024: 6356-6367 - [i6]Da Zheng, Xiang Song, Qi Zhu, Jiani Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis:
GraphStorm: all-in-one graph machine learning framework for industry applications. CoRR abs/2406.06022 (2024) - 2023
- [c16]Israt Nisa, Minjie Wang, Da Zheng, Qiang Fu, Ümit V. Çatalyürek, George Karypis:
Optimizing Irregular Dense Operators of Heterogeneous GNN Models on GPU. IPDPS Workshops 2023: 199-206 - [c15]Jian Zhang, Da Zheng, Xiang Song, Theodore Vasiloudis, Israt Nisa, Jim Lu:
GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes. KDD 2023: 5790-5791 - [i5]Kun Wu, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei W. Hwu:
PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks. CoRR abs/2301.06284 (2023) - 2022
- [j1]Hongkuan Zhou, Da Zheng, Israt Nisa, Vassilis N. Ioannidis, Xiang Song, George Karypis:
TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs. Proc. VLDB Endow. 15(8): 1572-1580 (2022) - [c14]Srdan Milakovic, Oguz Selvitopi, Israt Nisa, Zoran Budimlic, Aydin Buluç:
Parallel Algorithms for Masked Sparse Matrix-Matrix Products. ICPP 2022: 10:1-10:11 - [c13]Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc:
Nimble GNN Embedding with Tensor-Train Decomposition. KDD 2022: 2327-2335 - [c12]Srdan Milakovic, Oguz Selvitopi, Israt Nisa, Zoran Budimlic, Aydin Buluç:
Parallel algorithms for masked sparse matrix-matrix products. PPoPP 2022: 453-454 - [i4]Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, George Karypis:
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs. CoRR abs/2203.14883 (2022) - [i3]Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc:
Nimble GNN Embedding with Tensor-Train Decomposition. CoRR abs/2206.10581 (2022) - 2021
- [c11]Oguz Selvitopi, Benjamin Brock, Israt Nisa, Alok Tripathy, Katherine A. Yelick, Aydin Buluç:
Distributed-memory parallel algorithms for sparse times tall-skinny-dense matrix multiplication. ICS 2021: 431-442 - [c10]Israt Nisa, Prashant Pandey, Marquita Ellis, Leonid Oliker, Aydin Buluç, Katherine A. Yelick:
Distributed-Memory k-mer Counting on GPUs. IPDPS 2021: 527-536 - [i2]Srdan Milakovic, Oguz Selvitopi, Israt Nisa, Zoran Budimlic, Aydin Buluç:
Parallel Algorithms for Masked Sparse Matrix-Matrix Products. CoRR abs/2111.09947 (2021)
2010 – 2019
- 2019
- [c9]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan:
Load-Balanced Sparse MTTKRP on GPUs. IPDPS 2019: 123-133 - [c8]Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, P. Sadayappan:
Adaptive sparse tiling for sparse matrix multiplication. PPoPP 2019: 300-314 - [c7]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Prashant Singh Rawat, Sriram Krishnamoorthy, P. Sadayappan:
An efficient mixed-mode representation of sparse tensors. SC 2019: 49:1-49:25 - [i1]Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan:
Load-Balanced Sparse MTTKRP on GPUs. CoRR abs/1904.03329 (2019) - 2018
- [c6]Israt Nisa, Aravind Sukumaran-Rajam, Süreyya Emre Kurt, Changwan Hong, P. Sadayappan:
Sampled Dense Matrix Multiplication for High-Performance Machine Learning. HiPC 2018: 32-41 - [c5]Changwan Hong, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Jinsung Kim, Süreyya Emre Kurt, Israt Nisa, Shivani Sabhlok, Ümit V. Çatalyürek, Srinivasan Parthasarathy, P. Sadayappan:
Efficient sparse-matrix multi-vector product on GPUs. HPDC 2018: 66-79 - [c4]Gordon Euhyun Moon, Israt Nisa, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Srinivasan Parthasarathy, P. Sadayappan:
Parallel Latent Dirichlet Allocation on GPUs. ICCS (2) 2018: 259-272 - [c3]Israt Nisa, Charles Siegel, Aravind Sukumaran-Rajam, Abhinav Vishnu, P. Sadayappan:
Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs. IPDPS Workshops 2018: 1056-1065 - 2017
- [c2]Rakshith Kunchum, Ankur Chaudhry, Aravind Sukumaran-Rajam, Qingpeng Niu, Israt Nisa, P. Sadayappan:
On improving performance of sparse matrix-matrix multiplication on GPUs. ICS 2017: 14:1-14:11 - [c1]Israt Nisa, Aravind Sukumaran-Rajam, Rakshith Kunchum, P. Sadayappan:
Parallel CCD++ on GPU for Matrix Factorization. GPGPU@PPoPP 2017: 73-83
Coauthor Index
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