arXiv subjects

Mohsen Koohi Esfahani

Publications and source records attributed to Mohsen Koohi Esfahani.

6 recordsLinked to original sources

arXiv2026

Skipper: Maximal Matching with a Single Pass over Edges

Maximal Matching (MM) is a fundamental graph problem with diverse applications. While state-of-the-art parallel MM algorithms have a total expected work linear in number of edges, they require randomization, iterative graph processing, and graph pruning after each iteration. These overheads increase execution time and demand additional memory, reducing applicability to large-scale graphs. In this paper, we introduce Skipper, an asynchronous Maximal Matching algorithm that resolves conflicts instantaneously using a parallel reservation strategy, which merges both reservation and committing steps into a single step. Skipper processes each edge only once, definitively determining whether the edge is selected as a match. Skipper does not require graph pruning and minimizes memory space utilization, requiring only a single byte of memory space per vertex. Furthermore, Skipper operates in the asynchronous parallel random access machine (APRAM) model, relaxing synchronization between threads, and facilitating better parallelization gains. Our evaluation, conducted on real-world and synthetic graphs with up to 224 billion edges, shows that Skipper achieves a speedup of 4.9--15.6 times, with a geometric mean of 8.0 times.

cs.DC↗

arXiv2026

On Optimizing Resource Utilization in Distributed Connected Components

Connected Components (CC) is a core graph problem with numerous applications. This paper investigates accelerating distributed CC by optimizing memory and network bandwidth utilization. We present two novel distributed CC algorithms, SiskinCC and RobinCC, which are built upon the Jayanti-Tarjan disjoint set union algorithm. To optimize memory utilization, SiskinCC and RobinCC are designed to facilitate efficient access to a shared array for all cores running in a machine. This allows execution of faster algorithms with larger memory bounds. SiskinCC leverages the continuous inter-machine communication during the computation phase to reduce the final communication overhead and RobinCC leverages the structural properties of real-world graphs to optimize network bandwidth utilization. Our evaluation against a distributed state-of-the-art CC algorithm, using real-world and synthetic graphs with up to 500 billion edges and 11.7 billion vertices, and on up to 2048 CPU cores, demonstrates that SiskinCC and RobinCC achieve geometric mean speedups of 29.1 and 16.8 times.

cs.DC↗

arXiv2025

Selective Parallel Loading of Large-Scale Compressed Graphs with ParaGrapher

Comprehensive evaluation is one of the basis of experimental science. In High-Performance Graph Processing, a thorough evaluation of contributions becomes more achievable by supporting common input formats over different frameworks. However, each framework creates its specific format, which may not support reading large-scale real-world graph datasets. This shows a demand for high-performance libraries capable of loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions on a wide range of graph algorithms, and (iii) to facilitate easy and fast comparison over different graph frameworks. To that end, we present ParaGrapher, a high-performance API and library for loading large-scale and compressed graphs. ParaGrapher supports different types of requests for accessing graphs in shared- and distributed-memory and out-of-core graph processing. We explain the design of ParaGrapher and present a performance model of graph decompression, which is used for evaluation of ParaGrapher over three storage types. Our evaluation shows that by decompressing compressed graphs in WebGraph format, ParaGrapher delivers up to 3.2 times speedup in loading and up to 5.2 times speedup in end-to-end execution (i.e., through interleaved loading and execution) in comparison to the binary and textual formats. ParaGrapher is available online on https://blogs.qub.ac.uk/DIPSA/ParaGrapher/.

cs.AR↗

arXiv2025

Accelerating Loading WebGraphs in ParaGrapher

ParaGrapher is a graph loading API and library that enables graph processing frameworks to load large-scale compressed graphs with minimal overhead. This capability accelerates the design and implementation of new high-performance graph algorithms and their evaluation on a wide range of graphs and across different frameworks. However, our previous study identified two major limitations in ParaGrapher: inefficient utilization of high-bandwidth storage and reduced decompression bandwidth due to increased compression ratios. To address these limitations, we present two optimizations for ParaGrapher in this paper. To improve storage utilization, particularly for high-bandwidth storage, we introduce ParaGrapher-FUSE (PG-Fuse) a filesystem based on the FUSE (Filesystem in User Space). PG-Fuse optimizes storage access by increasing the size of requested blocks, reducing the number of calls to the underlying filesystem, and caching the received blocks in memory for future calls. To improve the decompression bandwidth, we introduce CompBin, a compact binary representation of the CSR format. CompBin facilitates direct accesses to neighbors while preventing storage usage for unused bytes. Our evaluation on 12 real-world and synthetic graphs with up to 128 billion edges shows that PG-Fuse and CompBin achieve up to 7.6 and 21.8 times speedup, respectively.

cs.DC↗

arXiv2025

On Optimizing Locality of Graph Transposition on Modern Architectures

This paper investigates the shared-memory Graph Transposition (GT) problem, a fundamental graph algorithm that is widely used in graph analytics and scientific computing. Previous GT algorithms have significant memory requirements that are proportional to the number of vertices and threads which obstructs their use on large graphs. Moreover, atomic memory operations have become comparably fast on recent CPU architectures, which creates new opportunities for improving the performance of concurrent atomic accesses in GT. We design PoTra, a GT algorithm which leverages graph structure and processor and memory architecture to optimize locality and performance. PoTra limits the size of additional data structures close to CPU cache sizes and utilizes the skewed degree distribution of graph datasets to optimize locality and performance. We present the performance model of PoTra to explain the connection between cache and memory response times and graph locality. Our evaluation of PoTra on three CPU architectures and 20 real-world and synthetic graph datasets with up to 128 billion edges demonstrates that PoTra achieves up to 8.7 times speedup compared to previous works and if there is a performance loss it remains limited to 15.7%, on average.

cs.DC↗

arXiv2023

MS-BioGraphs: Sequence Similarity Graph Datasets

Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets. To ensure continuation of this progress, we (i) investigate and optimize the process of generating large sequence similarity graphs as an HPC challenge and (ii) demonstrate this process in creating MS-BioGraphs, a new family of publicly available real-world edge-weighted graph datasets with up to $2.5$ trillion edges, that is, $6.6$ times greater than the largest graph published recently. The largest graph is created by matching (i.e., all-to-all similarity aligning) $1.7$ billion protein sequences. The MS-BioGraphs family includes also seven subgraphs with different sizes and direction types. We describe two main challenges we faced in generating large graph datasets and our solutions, that are, (i) optimizing data structures and algorithms for this multi-step process and (ii) WebGraph parallel compression technique. We present a comparative study of structural characteristics of MS-BioGraphs. The datasets are available online on https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs .

cs.DC↗