arXiv subjects

Shiju Li

Publications and source records attributed to Shiju Li.

4 recordsLinked to original sources

arXiv2025

Accelerating Sparse Matrix-Matrix Multiplication on GPUs with Processing Near HBMs

Sparse General Matrix-Matrix Multiplication (SpGEMM) is a fundamental operation in numerous scientific computing and data analytics applications, often bottlenecked by irregular memory access patterns. This paper presents Hash based Multi-phase SpGEMM on GPU and the Acceleration of Indirect Memory Access (AIA) technique, a novel custom near-memory processing approach to optimizing SpGEMM on GPU HBM. Our hardware-software co-designed framework for SpGEMM demonstrates significant performance improvements over state-of-the-art methods, particularly in handling complex, application-specific workloads. We evaluate our approach on various graph workloads, including graph contraction, Markov clustering, and Graph Neural Networks (GNNs), showcasing its practical applicability. For graph analytics applications, AIA demonstrates up to 17.3% time reduction from the software-only implementation, while achieving time reduction of 76.5% for Graph Contraction and 58.4% for Markov Clustering compared to cuSPARSE. For GNN training applications with structured global pruning, our hybrid approach delivers an average of 1.43x speedup over software-only implementation across six benchmark datasets and three architectures (GCN, GIN, GraphSAGE), and shows 1.95x speedup for GNN workloads when compared to cuSPARSE, with up to 4.18x gains on large-scale datasets.

cs.DC↗

arXiv2024

SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks

Recently, distributed GNN training frameworks, such as DistDGL and PyG, have been developed to enable training GNN models on large graphs by leveraging multiple GPUs in a distributed manner. Despite these advances, their memory requirements are still excessively high, thereby hindering GNN training on large graphs using commodity workstations. In this paper, we propose SDT-GNN, a streaming-based distributed GNN training framework. Unlike the existing frameworks that load the entire graph in memory, it takes a stream of edges as input for graph partitioning to reduce the memory requirement for partitioning. It also enables distributed GNN training even when the aggregated memory size of GPUs is smaller than the size of the graph and feature data. Furthermore, to improve the quality of partitioning, we propose SPRING, a novel streaming partitioning algorithm for distributed GNN training. We demonstrate the effectiveness and efficiency of SDT-GNN on seven large public datasets. SDT-GNN has up to 95% less memory footprint than DistDGL and PyG without sacrificing the prediction accuracy. SPRING also outperforms state-of-the-art streaming partitioning algorithms significantly.

cs.LG↗

arXiv2023

Controlling Epidemic Spread Under Immunization Delay Constraints

In this paper, we study the problem of minimizing the spread of a viral epidemic when immunization takes a non-negligible amount of time to take into effect. Specifically, our problem is to determine which set of nodes to be vaccinated when vaccines take a random amount of time in order to maximize the total reward, which is the expected number of saved nodes. We first provide a mathematical analysis for the reward function of vaccinating an arbitrary number of nodes when there is a single source of infection. While it is infeasible to obtain the optimal solution analytically due to the combinatorial nature of the problem, we establish that the problem is a monotone submodular maximization problem and develop a greedy algorithm that achieves a $(1\!-\!1/e)$-approximation. We further extend the scenario to the ones with multiple infection sources and discuss how the greedy algorithm can be applied systematically for the multiple-source scenarios. We finally present extensive simulation results to demonstrate the superiority of our greedy algorithm over other baseline vaccination strategies.

cs.SI↗

arXiv2022

GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on one side and signal samples on the other. GRAFICS then learns the low-dimensional embeddings of signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS finally clusters the node embeddings along with the embeddings of a few labeled samples through a proximity-based hierarchical clustering, which eases the floor identification of every new sample. We validate the effectiveness of GRAFICS based on two large-scale datasets that contain RF signal records from 204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our experiment results show that GRAFICS achieves highly accurate prediction performance with only a few labeled samples (96% in both micro- and macro-F scores) and significantly outperforms several state-of-the-art algorithms (by about 45% improvement in micro-F score and 53% in macro-F score).

cs.NI↗