Peng Jiang

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 25

Top Topics

Neural Network Training

Matrix Multiplication

Speculative Execution

Top Venues

Publications

Yihua Wei, Peng Jiang
GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs. IPDPS (2024)
Lihan Hu, Jing Li, Peng Jiang
cuKE: An Efficient Code Generator for Score Function Computation in Knowledge Graph Embedding. IPDPS (2024)
Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath
End-to-End LU Factorization of Large Matrices on GPUs. PPoPP (2023)
Jiya Su, Peng Jiang, Rujia Wang
PIMMiner: A High-performance PIM Architecture-aware Graph Mining Framework. CoRR (2023)
Shihui Song, Peng Jiang
Rethinking graph data placement for graph neural network training on multiple GPUs. ICS (2022)
Shihui Song, Peng Jiang
Rethinking graph data placement for graph neural network training on multiple GPUs. PPoPP (2022)
Peng Jiang, Yihua Wei, Jiya Su, Rujia Wang, Bo Wu
SampleMine: A Framework for Applying Random Sampling to Subgraph Pattern Mining through Loop Perforation. PACT (2022)
Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath
Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations. IPDPS (2022)
Peng Jiang, Lihan Hu, Shihui Song
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training. NeurIPS (2022)
Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath
Scaling Sparse Matrix Multiplication on CPU-GPU Nodes. IPDPS (2021)
Peng Jiang, Gagan Agrawal
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning. CoRR (2020)
Yang Xia, Peng Jiang, Gagan Agrawal
Scaling out speculative execution of finite-state machines with parallel merge. PPoPP (2020)
Peng Jiang, Changwan Hong, Gagan Agrawal
A novel data transformation and execution strategy for accelerating sparse matrix multiplication on GPUs. PPoPP (2020)
Peng Jiang, Yang Xia, Gagan Agrawal
Combining SIMD and Many/Multi-core Parallelism for Finite-state Machines with Enumerative Speculation. ACM Trans. Parallel Comput. 7 (3) (2020)
Yang Xia, Peng Jiang, Gagan Agrawal
Enabling prefix sum parallelism pattern for recurrences with principled function reconstruction. CC (2019)
Peng Jiang, Gagan Agrawal
Accelerating distributed stochastic gradient descent with adaptive periodic parameter averaging: poster. PPoPP (2019)
Gangyi Zhu, Peng Jiang, Gagan Agrawal
A Methodology for Characterizing Sparse Datasets and Its Application to SIMD Performance Prediction. PACT (2019)
Peng Jiang, Gagan Agrawal
Revealing parallel scans and reductions in sequential loops through function reconstruction. PPOPP (2018)
Peng Jiang, Gagan Agrawal
Conflict-free vectorization of associative irregular applications with recent SIMD architectural advances. CGO (2018)
Peng Jiang, Linchuan Chen, Gagan Agrawal
Revealing parallel scans and reductions in recurrences through function reconstruction. PACT (2018)
Peng Jiang, Gagan Agrawal
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. NeurIPS (2018)
Peng Jiang, Gagan Agrawal
Efficient SIMD and MIMD parallelization of hash-based aggregation by conflict mitigation. ICS (2017)
Peng Jiang, Gagan Agrawal
Combining SIMD and Many/Multi-core Parallelism for Finite State Machines with Enumerative Speculation. PPOPP (2017)
Peng Jiang, Linchuan Chen, Gagan Agrawal
Reusing Data Reorganization for Efficient SIMD Parallelization of Adaptive Irregular Applications. ICS (2016)
Linchuan Chen, Peng Jiang, Gagan Agrawal
Exploiting recent SIMD architectural advances for irregular applications. CGO (2016)