Exploiting recent SIMD architectural advances for irregular applications. - Linchuan Chen; Peng Jiang 004; Gagan Agrawal (CGO, 2016)

Reusing Data Reorganization for Efficient SIMD Parallelization of Adaptive Irregular Applications. - Peng Jiang 004; Linchuan Chen; Gagan Agrawal (ICS, 2016)

Efficient SIMD and MIMD parallelization of hash-based aggregation by conflict mitigation. - Peng Jiang 004; Gagan Agrawal (ICS, 2017)

Combining SIMD and Many/Multi-core Parallelism for Finite State Machines with Enumerative Speculation. - Peng Jiang 004; Gagan Agrawal (PPoPP, 2017)

Revealing parallel scans and reductions in recurrences through function reconstruction. - Peng Jiang 004; Linchuan Chen; Gagan Agrawal (PACT, 2018)

Conflict-free vectorization of associative irregular applications with recent SIMD architectural advances. - Peng Jiang 004; Gagan Agrawal (CGO, 2018)

A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. - Peng Jiang 004; Gagan Agrawal (NeurIPS, 2018)

Revealing parallel scans and reductions in sequential loops through function reconstruction. - Peng Jiang 004; Gagan Agrawal (PPoPP, 2018)

A Methodology for Characterizing Sparse Datasets and Its Application to SIMD Performance Prediction. - Gangyi Zhu; Peng Jiang 004; Gagan Agrawal (PACT, 2019)

Enabling prefix sum parallelism pattern for recurrences with principled function reconstruction. - Yang Xia; Peng Jiang 004; Gagan Agrawal (CC, 2019)

Accelerating distributed stochastic gradient descent with adaptive periodic parameter averaging: poster. - Peng Jiang 004; Gagan Agrawal (PPoPP, 2019)

Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning. - Masuma Akter Rumi; Xiaolong Ma; Yanzhi Wang; Peng Jiang 004 (PACT, 2020)

A novel data transformation and execution strategy for accelerating sparse matrix multiplication on GPUs. - Peng Jiang 004; Changwan Hong; Gagan Agrawal (PPoPP, 2020)

Scaling out speculative execution of finite-state machines with parallel merge. - Yang Xia; Peng Jiang 004; Gagan Agrawal (PPoPP, 2020)

Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning. - Peng Jiang 004; Gagan Agrawal (CoRR, 2020)

Combining SIMD and Many/Multi-core Parallelism for Finite-state Machines with Enumerative Speculation. - Peng Jiang 004; Yang Xia; Gagan Agrawal (ACM Trans. Parallel Comput., 2020)

Scaling Sparse Matrix Multiplication on CPU-GPU Nodes. - Yang Xia; Peng Jiang 004; Gagan Agrawal; Rajiv Ramnath (IPDPS, 2021)

Exploring PIM Architecture for High-Performance Graph Pattern Mining. - Jiya Su; Linfeng He; Peng Jiang 004; Rujia Wang (IEEE Comput. Archit. Lett., 2021)

An Efficient Graph Mining System for Large Patterns. - Peng Jiang 004; Rujia Wang; Bo Wu 002 (CoRR, 2021)

Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks. - Peng Jiang 004; Masuma Akter Rumi (CoRR, 2021)

SampleMine: A Framework for Applying Random Sampling to Subgraph Pattern Mining through Loop Perforation. - Peng Jiang 004; Yihua Wei; Jiya Su; Rujia Wang; Bo Wu 002 (PACT, 2022)

Rethinking graph data placement for graph neural network training on multiple GPUs. - Shihui Song; Peng Jiang 004 (ICS, 2022)

Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations. - Yang Xia; Peng Jiang 004; Gagan Agrawal; Rajiv Ramnath (IPDPS, 2022)

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training. - Peng Jiang 004; Lihan Hu; Shihui Song (NeurIPS, 2022)

STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimizations. - Yihua Wei; Peng Jiang 004 (SC, 2022)

End-to-End LU Factorization of Large Matrices on GPUs. - Yang Xia; Peng Jiang 004; Gagan Agrawal; Rajiv Ramnath (PPoPP, 2023)

PIMMiner: A High-performance PIM Architecture-aware Graph Mining Framework. - Jiya Su; Peng Jiang 004; Rujia Wang (CoRR, 2023)

CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2. - Shihui Song; Yafan Huang; Peng Jiang 004; Xiaodong Yu 001; Weijian Zheng; Sheng Di; Qinglei Cao; Yunhe Feng; Zhen Xie; Franck Cappello (HPDC, 2024)

cuKE: An Efficient Code Generator for Score Function Computation in Knowledge Graph Embedding. - Lihan Hu; Jing Li; Peng Jiang 004 (IPDPS, 2024)

GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs. - Yihua Wei; Peng Jiang 004 (IPDPS, 2024)

Improving Accuracy and Efficiency of Graph Embedding Training with Fine-Grained Parameter Management. - Lihan Hu; Peng Jiang 004 (IPDPS, 2025)

A Memory-Efficient and Computation-Balanced Lossy Compressor on Wafer-Scale Engine. - Shihui Song; Robert Underwood; Sheng Di; Yafan Huang; Peng Jiang 004; Franck Cappello (IPDPS, 2025)

Lossy Compression of Scientific Data: Applications Constrains and Requirements. - Franck Cappello; Allison H. Baker; Ebru Bozda; Martin Burtscher; Kyle Chard; Sheng Di; Paul Christopher O. Grady; Peng Jiang 004; Shaomeng Li; Erik Lindahl; Peter Lindstrom 001; Magnus Lundborg; et al. (CoRR, 2025)