Peng Jiang

According to our database, Peng Jiang authored at least 27 papers between 2016 and 2023.

Collaborative distances:

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

2023

PIMMiner: A High-performance PIM Architecture-aware Graph Mining Framework.

Jiya Su, Peng Jiang, Rujia Wang

CoRR, 2023

End-to-End LU Factorization of Large Matrices on GPUs.

Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath

Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, 2023

2022

STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimizations.

Yihua Wei, Peng Jiang

Proceedings of the SC22: International Conference for High Performance Computing, 2022

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training.

Peng Jiang, Lihan Hu, Shihui Song

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations.

Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath

Proceedings of the 2022 IEEE International Parallel and Distributed Processing Symposium, 2022

Rethinking graph data placement for graph neural network training on multiple GPUs.

Shihui Song, Peng Jiang

Proceedings of the ICS '22: 2022 International Conference on Supercomputing, Virtual Event, June 28, 2022

SampleMine: A Framework for Applying Random Sampling to Subgraph Pattern Mining through Loop Perforation.

Peng Jiang, Yihua Wei, Jiya Su, Rujia Wang, Bo Wu

Proceedings of the International Conference on Parallel Architectures and Compilation Techniques, 2022

2021

Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks.

Peng Jiang, Masuma Akter Rumi

CoRR, 2021

An Efficient Graph Mining System for Large Patterns.

Peng Jiang, Rujia Wang, Bo Wu

CoRR, 2021

Exploring PIM Architecture for High-Performance Graph Pattern Mining.

Jiya Su, Linfeng He, Peng Jiang, Rujia Wang

IEEE Comput. Archit. Lett., 2021

Scaling Sparse Matrix Multiplication on CPU-GPU Nodes.

Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath

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

2020

Combining SIMD and Many/Multi-core Parallelism for Finite-state Machines with Enumerative Speculation.

Peng Jiang, Yang Xia, Gagan Agrawal

ACM Trans. Parallel Comput., 2020

Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning.

Peng Jiang, Gagan Agrawal

CoRR, 2020

Scaling out speculative execution of finite-state machines with parallel merge.

Yang Xia, Peng Jiang, Gagan Agrawal

Proceedings of the PPoPP '20: 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2020

A novel data transformation and execution strategy for accelerating sparse matrix multiplication on GPUs.

Peng Jiang, Changwan Hong, Gagan Agrawal

Proceedings of the PPoPP '20: 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2020

Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning.

Masuma Akter Rumi, Xiaolong Ma, Yanzhi Wang, Peng Jiang

Proceedings of the PACT '20: International Conference on Parallel Architectures and Compilation Techniques, 2020

2019

Accelerating distributed stochastic gradient descent with adaptive periodic parameter averaging: poster.

Peng Jiang, Gagan Agrawal

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

Enabling prefix sum parallelism pattern for recurrences with principled function reconstruction.

Yang Xia, Peng Jiang, Gagan Agrawal

Proceedings of the 28th International Conference on Compiler Construction, 2019

A Methodology for Characterizing Sparse Datasets and Its Application to SIMD Performance Prediction.

Gangyi Zhu, Peng Jiang, Gagan Agrawal

Proceedings of the 28th International Conference on Parallel Architectures and Compilation Techniques, 2019

2018

Revealing parallel scans and reductions in sequential loops through function reconstruction.

Peng Jiang, Gagan Agrawal

Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018

A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication.

Peng Jiang, Gagan Agrawal

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Conflict-free vectorization of associative irregular applications with recent SIMD architectural advances.

Peng Jiang, Gagan Agrawal

Proceedings of the 2018 International Symposium on Code Generation and Optimization, 2018

Revealing parallel scans and reductions in recurrences through function reconstruction.

Peng Jiang, Linchuan Chen, Gagan Agrawal

Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques, 2018

2017

Combining SIMD and Many/Multi-core Parallelism for Finite State Machines with Enumerative Speculation.

Peng Jiang, Gagan Agrawal

Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2017

Efficient SIMD and MIMD parallelization of hash-based aggregation by conflict mitigation.

Peng Jiang, Gagan Agrawal

Proceedings of the International Conference on Supercomputing, 2017

2016

Reusing Data Reorganization for Efficient SIMD Parallelization of Adaptive Irregular Applications.

Peng Jiang, Linchuan Chen, Gagan Agrawal

Proceedings of the 2016 International Conference on Supercomputing, 2016

Exploiting recent SIMD architectural advances for irregular applications.

Linchuan Chen, Peng Jiang, Gagan Agrawal

Proceedings of the 2016 International Symposium on Code Generation and Optimization, 2016