Israt Nisa

According to our database, Israt Nisa authored at least 21 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

GraphStorm: all-in-one graph machine learning framework for industry applications.

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

CoRR, 2024

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications.

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

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures.

Kun Wu, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei Hwu

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks.

Kun Wu, Mert Hidayetoglu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-Mei W. Hwu

CoRR, 2023

GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes.

Jian Zhang, Da Zheng, Xiang Song, Theodore Vasiloudis, Israt Nisa, Jim Lu

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Optimizing Irregular Dense Operators of Heterogeneous GNN Models on GPU.

Israt Nisa, Minjie Wang, Da Zheng, Qiang Fu, Ümit V. Çatalyürek, George Karypis

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

2022

TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs.

Hongkuan Zhou, Da Zheng, Israt Nisa, Vassilis N. Ioannidis, Xiang Song, George Karypis

Proc. VLDB Endow., 2022

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs.

Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, George Karypis

CoRR, 2022

Nimble GNN Embedding with Tensor-Train Decomposition.

Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Parallel Algorithms for Masked Sparse Matrix-Matrix Products.

Srdan Milakovic, Oguz Selvitopi, Israt Nisa, Zoran Budimlic, Aydin Buluç

Proceedings of the 51st International Conference on Parallel Processing, 2022

2021

Distributed-Memory k-mer Counting on GPUs.

Israt Nisa, Prashant Pandey, Marquita Ellis, Leonid Oliker, Aydin Buluç, Katherine A. Yelick

Proceedings of the 35th IEEE International Parallel and Distributed Processing Symposium, 2021

Distributed-memory parallel algorithms for sparse times tall-skinny-dense matrix multiplication.

Oguz Selvitopi, Benjamin Brock, Israt Nisa, Alok Tripathy, Katherine A. Yelick, Aydin Buluç

Proceedings of the ICS '21: 2021 International Conference on Supercomputing, 2021

2019

An efficient mixed-mode representation of sparse tensors.

Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Prashant Singh Rawat, Sriram Krishnamoorthy, P. Sadayappan

Proceedings of the International Conference for High Performance Computing, 2019

Adaptive sparse tiling for sparse matrix multiplication.

Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, P. Sadayappan

Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2019

Load-Balanced Sparse MTTKRP on GPUs.

Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam, Richard W. Vuduc, P. Sadayappan

Proceedings of the 2019 IEEE International Parallel and Distributed Processing Symposium, 2019

2018

Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs.

Israt Nisa, Charles Siegel, Aravind Sukumaran-Rajam, Abhinav Vishnu, P. Sadayappan

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018

Parallel Latent Dirichlet Allocation on GPUs.

Gordon Euhyun Moon, Israt Nisa, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Srinivasan Parthasarathy, P. Sadayappan

Proceedings of the Computational Science - ICCS 2018, 2018

Efficient sparse-matrix multi-vector product on GPUs.

Changwan Hong, Aravind Sukumaran-Rajam, Bortik Bandyopadhyay, Jinsung Kim, Süreyya Emre Kurt, Israt Nisa, Shivani Sabhlok, Ümit V. Çatalyürek, Srinivasan Parthasarathy, P. Sadayappan

Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, 2018

Sampled Dense Matrix Multiplication for High-Performance Machine Learning.

Israt Nisa, Aravind Sukumaran-Rajam, Süreyya Emre Kurt, Changwan Hong, P. Sadayappan

Proceedings of the 25th IEEE International Conference on High Performance Computing, 2018

2017

Parallel CCD++ on GPU for Matrix Factorization.

Israt Nisa, Aravind Sukumaran-Rajam, Rakshith Kunchum, P. Sadayappan

Proceedings of the General Purpose GPUs, 2017

On improving performance of sparse matrix-matrix multiplication on GPUs.

Rakshith Kunchum, Ankur Chaudhry, Aravind Sukumaran-Rajam, Qingpeng Niu, Israt Nisa, P. Sadayappan

Proceedings of the International Conference on Supercomputing, 2017