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Peng Jiang 0004
Person information
- affiliation: University of Iowa, Iowa City, IA, USA
- affiliation (PhD 2019): Ohio State University, Department of Computer Science and Engineering, OH, USA
Other persons with the same name
- Peng Jiang — disambiguation page
- Peng Jiang 0001 — Queen Mary University of London, UK
- Peng Jiang 0002
— Kwai Inc. / Kuaishou Inc., Beijing, China (and 3 more)
- Peng Jiang 0005 — University of Illinois Urbana-Champaign, Department of Computer Science, IL, USA
- Peng Jiang 0006
- Peng Jiang 0007
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2020 – today
- 2024
- [c25]Shihui Song
CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2. HPDC 2024: 309-321 - [c24]Lihan Hu, Jing Li, Peng Jiang:
cuKE: An Efficient Code Generator for Score Function Computation in Knowledge Graph Embedding. IPDPS 2024: 903-914 - [c23]Yihua Wei, Peng Jiang:
GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs. IPDPS 2024: 1046-1057 - 2023
- [c22]Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath:
End-to-End LU Factorization of Large Matrices on GPUs. PPoPP 2023: 288-300 - [i4]Jiya Su, Peng Jiang, Rujia Wang:
PIMMiner: A High-performance PIM Architecture-aware Graph Mining Framework. CoRR abs/2306.10257 (2023) - 2022
- [c21]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: 185-197 - [c20]Shihui Song, Peng Jiang:
Rethinking graph data placement for graph neural network training on multiple GPUs. ICS 2022: 39:1-39:10 - [c19]Yang Xia, Peng Jiang, Gagan Agrawal, Rajiv Ramnath:
Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations. IPDPS 2022: 190-200 - [c18]Peng Jiang, Lihan Hu, Shihui Song:
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training. NeurIPS 2022 - [c17]Shihui Song, Peng Jiang:
Rethinking graph data placement for graph neural network training on multiple GPUs. PPoPP 2022: 455-456 - [c16]Yihua Wei, Peng Jiang:
STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimizations. SC 2022: 53:1-53:13 - 2021
- [j2]Jiya Su, Linfeng He, Peng Jiang
Exploring PIM Architecture for High-Performance Graph Pattern Mining. IEEE Comput. Archit. Lett. 20(2): 114-117 (2021) - [c15]Yang Xia, Peng Jiang
Scaling Sparse Matrix Multiplication on CPU-GPU Nodes. IPDPS 2021: 392-401 - [i3]Peng Jiang, Rujia Wang, Bo Wu:
An Efficient Graph Mining System for Large Patterns. CoRR abs/2101.07690 (2021) - [i2]Peng Jiang, Masuma Akter Rumi:
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks. CoRR abs/2101.07706 (2021) - 2020
- [j1]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): 15:1-15:26 (2020) - [c14]Masuma Akter Rumi, Xiaolong Ma, Yanzhi Wang, Peng Jiang:
Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning. PACT 2020: 267-278 - [c13]Yang Xia, Peng Jiang
Scaling out speculative execution of finite-state machines with parallel merge. PPoPP 2020: 160-172 - [c12]Peng Jiang
A novel data transformation and execution strategy for accelerating sparse matrix multiplication on GPUs. PPoPP 2020: 376-388 - [i1]Peng Jiang, Gagan Agrawal:
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning. CoRR abs/2007.06134 (2020)
2010 – 2019
- 2019
- [c11]Gangyi Zhu, Peng Jiang
A Methodology for Characterizing Sparse Datasets and Its Application to SIMD Performance Prediction. PACT 2019: 445-456 - [c10]Yang Xia, Peng Jiang
Enabling prefix sum parallelism pattern for recurrences with principled function reconstruction. CC 2019: 17-28 - [c9]Peng Jiang, Gagan Agrawal:
Accelerating distributed stochastic gradient descent with adaptive periodic parameter averaging: poster. PPoPP 2019: 403-404 - 2018
- [c8]Peng Jiang
Revealing parallel scans and reductions in recurrences through function reconstruction. PACT 2018: 10:1-10:13 - [c7]Peng Jiang
Conflict-free vectorization of associative irregular applications with recent SIMD architectural advances. CGO 2018: 175-187 - [c6]Peng Jiang, Gagan Agrawal:
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. NeurIPS 2018: 2530-2541 - [c5]Peng Jiang, Gagan Agrawal:
Revealing parallel scans and reductions in sequential loops through function reconstruction. PPoPP 2018: 395-396 - 2017
- [c4]Peng Jiang
Efficient SIMD and MIMD parallelization of hash-based aggregation by conflict mitigation. ICS 2017: 24:1-24:11 - [c3]Peng Jiang, Gagan Agrawal:
Combining SIMD and Many/Multi-core Parallelism for Finite State Machines with Enumerative Speculation. PPoPP 2017: 179-191 - 2016
- [c2]Linchuan Chen, Peng Jiang
Exploiting recent SIMD architectural advances for irregular applications. CGO 2016: 47-58 - [c1]Peng Jiang
Reusing Data Reorganization for Efficient SIMD Parallelization of Adaptive Irregular Applications. ICS 2016: 16:1-16:10
Coauthor Index
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