Wu-chun Feng

Pratheek Prakash Shetty, Thomas R. W. Scogland, Wu-chun Feng

Concurrent queues can significantly impact supercomputing performance by being critical bottlenecks for task distribution, load balancing, and resource utilization. As HPC systems move beyond 10-million processor cores, the ability to rapidly move items between producer and consumer threads without excessive locking is essential for efficient queues, preventing idle cores, maximizing utilization, and achieving high parallel speedup. While concurrent queues are well studied on CPUs, they remain largely unexplored on modern GPUs, where SIMT execution, massive parallelism, and atomic contention reshape the design space. We present three linearizable GPU concurrent queues spanning from lock-free to wait-free guarantees: (1) G-WFQ-YMC, an adaptation of Yang and Mellor-Crummey's wait-free queue using preallocated segments; (2) G-LFQ, a bounded lock-free queue that uses wave-batched fast paths to maximize throughput; and (3) G-WFQ, a bounded wait-free queue that packs shared state into 64-bit compare-and-swap operations while preserving linearizability and bounded memory.

Saikat Dey, Sonal Jha, Wu-chun Feng

Many data-mining applications use dynamic attributed graphs to represent relational information; but due to security and privacy concerns, there is a dearth of available datasets that can be represented as dynamic attributed graphs. Even when such datasets are available, they do not have ground truth that can be used to train deep-learning models. Thus, we present G2A2, an automated graph generator with attributes and anomalies, which encompasses (1) probabilistic models to generate a dynamic bipartite graph, representing time-evolving connections between two independent sets of entities, (2) realistic injection of anomalies using a novel algorithm that captures the general properties of graph anomalies across domains, and (3) a deep generative model to produce realistic attributes, learned from an existing real-world dataset. Using the maximum mean discrepancy (MMD) metric to evaluate the realism of a G2A2-generated graph against three real-world graphs, G2A2 outperforms Kronecker graph generation by reducing the MMD distance by up to six-fold (6x).

Ritvik Prabhu, Emil Vatai, Bernard Moussad et al.

Cancer typically arises not from a single genetic mutation (i.e., hit) but from multi-hit combinations that accumulate within cells. However, enumerating multi-hit combinations becomes exponentially more expensive computationally as the number of candidate hit gene combinations grow, i.e. on the order of 20,000 choose h, where 20,000 is the number of genes in the human genome and h is the number of hits. To address this challenge, we present an algorithmic framework, called Pruned Depth-First Search (P-DFS) that leverages the high sparsity in tumor mutation data to prune large portions of the search space. Specifically, P-DFS (the main contribution of this paper) - a pruning technique that exploits sparsity to drastically reduce the otherwise exponential h-hit search space for candidate combinations used by Weighted Set Cover - which is grounded in a depth-first search backtracking technique, prunes infeasible gene subsets early, while a weighted set cover formulation systematically scores and selects the most discriminative combinations. By intertwining these ideas with optimized bitwise operations and a scalable distributed algorithm on high-performance computing clusters, our algorithm can achieve approximately 90 - 98% reduction in visited combinations for 4-hits, and roughly a 183x speedup over the exhaustive set cover approach(which is algorithmically NP-complete) measured on 147,456 ranks. In doing so, our method can feasibly handle four-hit and even higher-order gene hits, achieving both speed and resource efficiency.

Garvit Goel, Jingyuan Qi, Wu-chun Feng et al.

We present a deep-learning based computing framework for fast-and-accurate CT (DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to improve the testing speed and accuracy of COVID-19 (plus its variants) via a DL-based approach for CT image enhancement and classification. The image enhancement network is adapted from DDnet, short for DenseNet and Deconvolution based network. To demonstrate its speed and accuracy, we evaluated DL-FACT across several sources of COVID-19 CT images. Our results show that DL-FACT can significantly shorten the turnaround time from days to minutes and improve the COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool for medical professionals in diagnosing and monitoring COVID-19.

Frank Wanye, Vitaliy Gleyzer, Edward Kao et al.

Community detection is a well-studied problem with applications in domains ranging from networking to bioinformatics. Due to the rapid growth in the volume of real-world data, there is growing interest in accelerating contemporary community detection algorithms. However, the more accurate and statistically robust methods tend to be hard to parallelize. One such method is stochastic block partitioning (SBP) - a community detection algorithm that works well on graphs with complex and heterogeneous community structure. In this paper, we present a sampling-based SBP (SamBaS) for accelerating SBP on sparse graphs. We characterize how various graph parameters affect the speedup and result quality of community detection with SamBaS and quantify the trade-offs therein. To evaluate SamBas on real-world web graphs without known ground-truth communities, we introduce partition quality score (PQS), an evaluation metric that outperforms modularity in terms of correlation with F1 score. Overall, SamBaS achieves speedups of up to 10X while maintaining result quality (and even improving result quality by over 150% on certain graphs, relative to F1 score).