5 papers · ranked by Valyu relevance
Michael T. Goodrich, Vinesh Sridhar
Embedded systems and Internet of Things (IoT) applications motivate in-place parallel algorithms, which avoid allocating additional shared memory past the input. Work by Gu, Obeya, and Shun [APOCS '21] defines a family of PIP (parallel in-place) models and parallel algorithms that eschew auxiliary memory at high…
Weitian Chen, Shixuan Sun, Cheng Chen, Yongmin Hu + 2 more
Subgraph matching is a core operation in graph analytics, supporting a broad spectrum of applications from social network analysis to bioinformatics. Recent GPU-based approaches accelerate subgraph matching by leveraging parallelism but rely on a coarse-grained execution model that suffers from scalability and…
Zhixin Ou, Peng Liang, Jianchen Han, Baihui Liu + 1 more
Dynamic sequences with varying lengths have been widely used in the training of Transformer-based large language models (LLMs). However, current training frameworks adopt a pre-defined static parallel strategy for these sequences, causing neither communication-parallelization cancellation on short sequences nor…
Accorsi, Luca, Laganà, Demetrio + 6 more
We propose a parallel shared-memory schema to cooperatively optimize the solution of a Capacitated Vehicle Routing Problem instance with minimal synchronization effort and without the need for an explicit decomposition. To this end, we design FILO2 x as a single-trajectory parallel adaptation of the FILO2 algorithm…
Marco Ronzani, Cristina Silvano
Hypergraph partitioning is a pervasive NP-hard problem, and accelerating its computation on GPU can both slice time-to-solution and raise quality of results. In this work, we implement a multi-level hypergraph partitioning algorithm on GPU targeting a specific set of problem constraints: bounded per-partition size and…