Ümit V. Çatalyürek
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View article: A Scalable and Effective Alternative to Graph Transformers
A Scalable and Effective Alternative to Graph Transformers Open
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs)…
View article: A Scalable and Effective Alternative to Graph Transformers
A Scalable and Effective Alternative to Graph Transformers Open
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs)…
View article: Cooperative Minibatching in Graph Neural Networks
Cooperative Minibatching in Graph Neural Networks Open
Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with gra…
View article: Open Problems in (Hyper)Graph Decomposition
Open Problems in (Hyper)Graph Decomposition Open
Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final ap…
View article: SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning
SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning Open
Partitioning for load balancing is a crucial first step to parallelize any type of computation. In this work, we propose SGORP, a new spatial partitioning method based on Subgradient Optimization, to solve the $d$-dimensional Rectilinear P…
View article: Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs Open
Graph Neural Networks (GNNs) have received significant attention recently, but training them at a large scale remains a challenge. Mini-batch training coupled with sampling is used to alleviate this challenge. However, existing approaches …
View article: PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms
PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms Open
Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural advances and variety. In this work, we propose a novel gra…
View article: On Symmetric Rectilinear Partitioning
On Symmetric Rectilinear Partitioning Open
Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalize…
View article: MG-GCN: A Scalable multi-GPU GCN Training Framework
MG-GCN: A Scalable multi-GPU GCN Training Framework Open
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has shown that, for the graphs used in the machine learning commu…
View article: A Simple and Elegant Mathematical Formulation for the Acyclic DAG Partitioning Problem
A Simple and Elegant Mathematical Formulation for the Acyclic DAG Partitioning Problem Open
This work addresses the NP-Hard problem of acyclic directed acyclic graph (DAG) partitioning problem. The acyclic partitioning problem is defined as partitioning the vertex set of a given directed acyclic graph into disjoint and collective…
View article: FIST-HOSVD
FIST-HOSVD Open
In this paper, several novel methods of improving the memory locality of the Sequentially Truncated Higher Order Singular Value Decomposition (ST-HOSVD) algorithm for computing the Tucker decomposition are presented. We show how the two pr…
View article: More Recent Advances in (Hyper)Graph Partitioning
More Recent Advances in (Hyper)Graph Partitioning Open
In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the last decade in practical algorithms for balanced (hyper)graph partitioning together…
View article: BOA: A Partitioned View of Genome Assembly
BOA: A Partitioned View of Genome Assembly Open
De novo genome assembly is a fundamental problem in computational molecular biology that aims to reconstruct an unknown genome sequence from a set of short DNA sequences (or reads ) obtained from the genome. High throughput sequencers coul…
View article: Batch dynamic algorithm to find <i>k</i> -core hierarchies
Batch dynamic algorithm to find <i>k</i> -core hierarchies Open
Finding k-cores in graphs is a valuable and effective strategy for extracting dense regions of otherwise sparse graphs. We focus on the important problem of maintaining cores on rapidly changing dynamic graphs, where batches of edge change…
View article: Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph Partitioning
Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph Partitioning Open
Early but promising results in quantum computing have been enabled by the concurrent development of quantum algorithms, devices, and materials. Classical simulation of quantum programs has enabled the design and analysis of algorithms and …
View article: An Efficient Parallel Implementation of a Perfect Hashing Method for Hypergraphs
An Efficient Parallel Implementation of a Perfect Hashing Method for Hypergraphs Open
to appear
View article: Batch Dynamic Algorithm to Find k-Cores and Hierarchies
Batch Dynamic Algorithm to Find k-Cores and Hierarchies Open
Finding $k$-cores in graphs is a valuable and effective strategy for extracting dense regions of otherwise sparse graphs. We focus on the important problem of maintaining cores on rapidly changing dynamic graphs, where batches of edge chan…
View article: Scheduling Series-Parallel Task Graphs to Minimize Peak Memory
Scheduling Series-Parallel Task Graphs to Minimize Peak Memory Open
We consider a variant of the well-known, NP-complete problem of minimum cut linear arrangement for directed acyclic graphs. In this variant, we are given a directed acyclic graph and asked to find a topological ordering such that the maxim…
View article: ElGA
ElGA Open
Modern graphs are not only large, but rapidly changing. The rate of change can vary significantly along with the computational cost. Existing distributed graph analysis systems have largely been designed to operate on static graphs. Infras…
View article: MG-GCN: Scalable Multi-GPU GCN Training Framework
MG-GCN: Scalable Multi-GPU GCN Training Framework Open
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has shown that, for the graphs used in the machine learning commu…
View article: An Evaluation of Task-Parallel Frameworks for Sparse Solvers on Multicore and Manycore CPU Architectures
An Evaluation of Task-Parallel Frameworks for Sparse Solvers on Multicore and Manycore CPU Architectures Open
Recently, several task-parallel programming models have emerged to address the high synchronization and load imbalance issues as well as data movement overheads in modern shared memory architectures. OpenMP, the most commonly used shared m…
View article: A Block-Based Triangle Counting Algorithm on Heterogeneous Environments
A Block-Based Triangle Counting Algorithm on Heterogeneous Environments Open
Triangle counting is a fundamental building block in graph algorithms. In this article, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation…
View article: Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs
Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs Open
Computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph's dense and cohesive regions. Computing k-cores on hypergraphs has seen recent interest, as many datasets naturally pr…
View article: PIGO: A Parallel Graph Input/Output Library
PIGO: A Parallel Graph Input/Output Library Open
Graph and sparse matrix systems are highly tuned, able to run complex graph analytics in fractions of seconds on billion-edge graphs. For both developers and researchers, the focus has been on computational kernels and not end-to-end runti…
View article: Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs.
Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs. Open
large-scale graphs, we developed and optimized a triad census algorithm to efficiently execute on shared memory architectures. We will retrace the development and evolution of a parallel triad census algorithm. Over the course of several v…
View article: PIGO: A Parallel Graph Input/Output Library.
PIGO: A Parallel Graph Input/Output Library. Open
has been designed in order to meet the strict dynamic voltage regulation requirement. A laboratory prototype has been built, and experimental results have been provided to verify the proposed HV POL with a single power conversion stage can…
View article: Parallel graph algorithms by blocks
Parallel graph algorithms by blocks Open
In this poster presentation we briefly explain our generalized algorithmic framework for parallel block-based graph algorithms, called PGAbB. PGAbB proposes that block-based graph algorithms offer a sweet spot between efficient parallelism…