This project implements a directed graph data structure in C++ with capabilities for loading graph data from files, storing both incoming and outgoing edges, and analyzing graph metrics. It's designed to work with large graph datasets efficiently.
This project implements a directed graph data structure in C++ with capabilities for loading graph data from files, storing both incoming and outgoing edges, and analyzing graph metrics. It's designed to work with large graph datasets efficiently.
A lightweight 2D graph library in Java
The projects implemented as a part of the Cloud Computing course using Big Data Technologies
A graphical editor for creating and running blossom programmes and graphs
A Graph Loader Application for the distributed in-memory system DXRAM
Exploring computational scaling of spiking neural network simulations across hardware architectures
Official CUDA implementation of "Streaming Right Multiplication over Grammar-Compressed Matrices: A Memory-Bounded GPU Engine for Genotype and Graph Data" (ALENEX 2027). Enables memory-bounded matrix-vector products and graph algorithms on massive dataset via grammar compression.
An interpreter for running blossom programmes
in-depth analysis and comparison of eight leading in-memory subgraph matching algorithms, QuickSI, GraphQL, CFL, CECI, DP-iso, RI, and VF2++.
CUDA/PyTorch operator for fused neighborhood sampling and mean aggregation, with reproducible KSEM 2026 experiments.
Hardware-aware compute runtime in C++ and CUDA for real sparse, dense, and graph workloads.
Simulator for Continuum Computer Architecture (CCA) class of designs
Python workflow for analyzing and visualizing connections between brain regions
Efficient Parallel Implementation of Single Source Shortest Path (SSSP) Algorithms using C++, including support for OpenMP and MPI for distributed and scalable graph processing.
Apache Giraph — independent third-party profile of a public API surface, by API Evangelist. Apache Giraph is an iterative graph processing system built for high scalability on Apache Hadoop. It is modeled after Google's Pregel and provides a simple yet flexible Java API for graph algorithms at massive scale using the Bulk Synchronous Parallel (BSP)
Pregel graph processing model for Tarantool, in pure Lua
Java graph theory library for data modeling and problem solving.
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