Jiajia Li

Works (8)

Updated: November 25th, 2024 07:13

2024 article

FASTEN: Fast GPU-accelerated Segmented Matrix Multiplication for Heterogeneous Graph Neural Networks

PROCEEDINGS OF THE 38TH ACM INTERNATIONAL CONFERENCE ON SUPERCOMPUTING, ACM ICS 2024, pp. 511–524.

By: K. Zhou *, K. Subramanian  n, P. Lin n, M. Fey, B. Yin * & J. Li n

author keywords: Graph Neural Networks; GPUs; Matrix Multiplication; Batch Processing; Performance Modeling
Source: Web Of Science
Added: August 5, 2024

2024 article

POSTER: Optimizing Sparse Tensor Contraction with Revisiting Hash Table Design

PROCEEDINGS OF THE 29TH ACM SIGPLAN ANNUAL SYMPOSIUM ON PRINCIPLES AND PRACTICE OF PARALLEL PROGRAMMING, PPOPP 2024, pp. 457–459.

By: G. Feng *, W. Jia *, N. Sun *, G. Tan * & J. Li n

author keywords: sparse tensor contraction; hash table
Source: Web Of Science
Added: May 13, 2024

2023 article

Fast Parallel Tensor Times Same Vector for Hypergraphs

2023 IEEE 30TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING, DATA, AND ANALYTICS, HIPC 2023, pp. 324–334.

By: S. Shivakumar*, I. Amburg *, S. Aksoy *, J. Li n, S. Young * & S. Aluru *

author keywords: hypergraphs; sparse symmetric tensor times same vector; tensor eigenvector; generating function
Source: Web Of Science
Added: July 8, 2024

2023 journal article

Performance Implication of Tensor Irregularity and Optimization for Distributed Tensor Decomposition

ACM TRANSACTIONS ON PARALLEL COMPUTING, 10(2).

By: Z. Miao *, J. Calhoun *, R. Ge * & J. Li n

author keywords: Sparse tensor; tensor decomposition; CPD; irregularity
TL;DR: This work proposes irregularity-aware distributed Cpd that leverages the sparsity and irregularity information to identify the best tradeoff between different imbalances with low time overhead and materializes the idea with two optimization methods. (via Semantic Scholar)
Source: Web Of Science
Added: August 21, 2023

2023 journal article

Sparse Symmetric Format for Tucker Decomposition

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 34(6), 1743–1756.

By: S. Shivakumar *, J. Li n, R. Kannan * & S. Aluru *

author keywords: Tensors; Symmetric matrices; Sparse matrices; Indexes; Signal processing algorithms; Matrix decomposition; Parallel algorithms; Compressed storage; sparse tensors; symmetric tensors; tensor times matrix chain
TL;DR: The novel Compressed Sparse Symmetric (CSS) format for sparse symmetric tensors is presented, along with an efficient parallel algorithm for the S<inline-formula><tex-math notation="LaTeX") TTM operation, and it is theoretically established that S.Tensor Times Matrix chain operation achieves a better memory versus run-time trade-off compared to state-of- theart implementations. (via Semantic Scholar)
Source: Web Of Science
Added: July 3, 2023

2022 article

AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse Matrices

SC22: INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS.

By: Z. Du *, J. Li n, Y. Wang *, X. Li *, G. Tan* & N. Sun *

author keywords: auto-tuner; sparse matrix-vector multiplication; SpMV; GPU; code generator; sparse data structures
TL;DR: AlphaSparse automatically creates novel machine-designed formats and SpMV kernel implementations en-tirely from the knowledge of input sparsity patterns and hard-ware architectures, a superset of all existing works that goes beyond the scope of human-designed format(s) and implementation(s). (via Semantic Scholar)
Source: Web Of Science
Added: June 12, 2023

2022 article

BALA-CPD: BALanced and Asynchronous Distributed Tensor Decomposition

2022 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER 2022), pp. 440–450.

By: Z. Miao *, J. Li n, J. Calhoun * & R. Ge *

author keywords: sparse tensor; tensor decomposition; CPD; asynchronous algorithm
TL;DR: A novel algorithm BALA-CPD is presented, which achieves the best overall workload balance, and effectively overlaps communication and computation for the popular distributed Canonical Polyadic Decomposition (CPD) algorithms. (via Semantic Scholar)
Source: Web Of Science
Added: February 27, 2023

2022 article

Editorial: High-performance tensor computations in scientific computing and data science

Di Napoli, E., Bientinesi, P., Li, J., & Uschmajew, A. (2022, September 23). FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, Vol. 8.

By: E. Di Napoli *, P. Bientinesi *, J. Li n & A. Uschmajew*

author keywords: tensor operation; tensor decomposition; tensor network; multilinear algebra; high performance optimization; low-rank approximation; Deep Learning; tensor library
TL;DR: High-performance tensor computations in scientific computing and data science and that the original publication in this journal is cited, in accordance with accepted academic practice. (via Semantic Scholar)
Source: Web Of Science
Added: October 31, 2022

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.