Peng Jiang

36 papers A* 2A 15B 12Journal 7
YearRankTypeTitle / Venue / Authors
2025 A conf
A Memory-Efficient and Computation-Balanced Lossy Compressor on Wafer-Scale Engine.
IPDPS
Shihui Song, Robert Underwood, Sheng Di, Yafan Huang, Peng Jiang, Franck Cappello
2025 A conf
Improving Accuracy and Efficiency of Graph Embedding Training with Fine-Grained Parameter Management.
IPDPS
Lihan Hu, Peng Jiang
2025 J jnl
Lossy Compression of Scientific Data: Applications Constrains and Requirements.
CoRR
Franck Cappello, Allison H. Baker, Ebru Bozdag, Martin Burtscher, Kyle Chard, Sheng Di, Paul Christopher O'Grady, Peng Jiang, Shaomeng Li, Erik Lindahl, Peter Lindstrom, Magnus Lundborg, Kai Zhao, Xin Liang, Masaru Nagaso, Kento Sato, Amarjit Singh, Seung Woo Son, Dingwen Tao, Jiannan Tian, Robert Underwood, Kazutomo Yoshii, Danylo Lykov, Yuri Alexeev, Kyle Gerard Felker
2025 A conf
Matcha: A Language and Compiler for Backtracking-Based Subgraph Matching.
IPDPS
Yihua Wei, Lihan Hu, Peng Jiang
2025 A conf
What to Support When You're Compressing: The State of Practice Gaps and Opportunities for Scientific Data Compression.
SC
Franck Cappello, Robert Underwood, Yuri Alexeev, Allison H. Baker, Ebru Bozdag, Martin Burtscher, Kyle Chard, Sheng Di, Kyle Gerard Felker, Paul Christopher O'Grady, Hanqi Guo, Yafan Huang, Peng Jiang, Sian Jin, Petter Johansson, Shaomeng Li, Xin Liang, Erik Lindahl, Peter Lindstrom, Zarija Lukic, Magnus Lundborg, Danylo Lykov, Masaru Nagaso, Kento Sato, Amarjit Singh, Seung Woo Son, Shihui Song, William Tang, Dingwen Tao, Jiannan Tian, Kazutomo Yoshii, Kai Zhao
2024 A conf
CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2.
HPDC
Shihui Song, Yafan Huang, Peng Jiang, Xiaodong Yu, Weijian Zheng, Sheng Di, Qinglei Cao, Yunhe Feng, Zhen Xie, Franck Cappello
2024 A conf
GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs.
IPDPS
Yihua Wei, Peng Jiang
2024 A conf
cuKE: An Efficient Code Generator for Score Function Computation in Knowledge Graph Embedding.
IPDPS
Lihan Hu, Jing Li, Peng Jiang
2023 B conf
End-to-End LU Factorization of Large Matrices on GPUs.
PPoPP
Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath
2023 J jnl
PIMMiner: A High-performance PIM Architecture-aware Graph Mining Framework.
CoRR
Jiya Su, Peng Jiang, Rujia Wang
2022 A* conf
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training.
NeurIPS
Peng Jiang, Lihan Hu, Shihui Song
2022 A conf
Rethinking graph data placement for graph neural network training on multiple GPUs.
ICS
Shihui Song, Peng Jiang
2022 B conf
Rethinking graph data placement for graph neural network training on multiple GPUs.
PPoPP
Shihui Song, Peng Jiang
2022 A conf
STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimizations.
SC
Yihua Wei, Peng Jiang
2022 B conf
SampleMine: A Framework for Applying Random Sampling to Subgraph Pattern Mining through Loop Perforation.
PACT
Peng Jiang, Yihua Wei, Jiya Su, Rujia Wang, Bo Wu
2022 A conf
Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations.
IPDPS
Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath
2021 J jnl
An Efficient Graph Mining System for Large Patterns.
CoRR
Peng Jiang, Rujia Wang, Bo Wu
2021 J jnl
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks.
CoRR
Peng Jiang, Masuma Akter Rumi
2021 J jnl
Exploring PIM Architecture for High-Performance Graph Pattern Mining.
IEEE Comput. Archit. Lett.
Jiya Su, Linfeng He, Peng Jiang, Rujia Wang
2021 A conf
Scaling Sparse Matrix Multiplication on CPU-GPU Nodes.
IPDPS
Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath
2020 B conf
A novel data transformation and execution strategy for accelerating sparse matrix multiplication on GPUs.
PPoPP
Peng Jiang, Changwan Hong, Gagan Agrawal
2020 B conf
Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning.
PACT
Masuma Akter Rumi, Xiaolong Ma, Yanzhi Wang, Peng Jiang
2020 J jnl
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning.
CoRR
Peng Jiang, Gagan Agrawal
2020 J jnl
Combining SIMD and Many/Multi-core Parallelism for Finite-state Machines with Enumerative Speculation.
ACM Trans. Parallel Comput.
Peng Jiang, Yang Xia, Gagan Agrawal
2020 B conf
Scaling out speculative execution of finite-state machines with parallel merge.
PPoPP
Yang Xia, Peng Jiang, Gagan Agrawal
2019 B conf
A Methodology for Characterizing Sparse Datasets and Its Application to SIMD Performance Prediction.
PACT
Gangyi Zhu, Peng Jiang, Gagan Agrawal
2019 B conf
Accelerating distributed stochastic gradient descent with adaptive periodic parameter averaging: poster.
PPoPP
Peng Jiang, Gagan Agrawal
2019 B conf
Enabling prefix sum parallelism pattern for recurrences with principled function reconstruction.
CC
Yang Xia, Peng Jiang, Gagan Agrawal
2018 A* conf
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication.
NeurIPS
Peng Jiang, Gagan Agrawal
2018 A conf
Conflict-free vectorization of associative irregular applications with recent SIMD architectural advances.
CGO
Peng Jiang, Gagan Agrawal
2018 B conf
Revealing parallel scans and reductions in recurrences through function reconstruction.
PACT
Peng Jiang, Linchuan Chen, Gagan Agrawal
2018 B conf
Revealing parallel scans and reductions in sequential loops through function reconstruction.
PPoPP
Peng Jiang, Gagan Agrawal
2017 B conf
Combining SIMD and Many/Multi-core Parallelism for Finite State Machines with Enumerative Speculation.
PPoPP
Peng Jiang, Gagan Agrawal
2017 A conf
Efficient SIMD and MIMD parallelization of hash-based aggregation by conflict mitigation.
ICS
Peng Jiang, Gagan Agrawal
2016 A conf
Exploiting recent SIMD architectural advances for irregular applications.
CGO
Linchuan Chen, Peng Jiang, Gagan Agrawal
2016 A conf
Reusing Data Reorganization for Efficient SIMD Parallelization of Adaptive Irregular Applications.
ICS
Peng Jiang, Linchuan Chen, Gagan Agrawal