Search Results for "graph algorithms"

...With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. Spark supports multiple languages—Scala, Java, Python, R—and connects with many storage systems like HDFS, S3, Cassandra, and streaming platforms like Kafka, making it a versatile choice for big data workloads in analytics, ETL, and data science.

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement...

Adrix.NT Java Works :: Sources & Build Projects includes - Paged Lists of Object & Iterators Library for Java - Action / Entity Model for Java - VR Ax AWT Clock Java Component - VR Multi Dimensions Array Library - VR MDArray List Manager Library - VR Adjacency (List | Matrix) Direct Graph Lib - VRJMosaic cells sliding game with automatic resolver - VR RectsWorld Sample Applic - VR Free Chess 2D Applic also contains - Other Utils Libraries - Demo and Test Applications -...

Atomix is a cloud-native runtime for building stateful, scalable, configurable, and reliable distributed applications in Kubernetes. The Atomix API provides a set of high-level building blocks (referred to as distributed primitives) for building distributed systems. The architecture of Atomix incorporates the lessons learned from experience over the past decade of building high-availability cloud infrastructure. The primary focus of the project is to decouple applications from specific data...

G.A.V. (Graph Algorithm Visualizer) is a tool that visualizes algorithms from graph theory. A step-by-step visualization from each different algorithm allows the user to understand the particular algorithm very easily.

The Open Optimization for Java provides a framework and the implementation of commonly-used algorithms found in Graph Theory and Network Optimization, e.g. shortest path and postman problem.

An adaptive mini database learns algorithmic operations for data intensive algebraic computational methods within a pervasive computing graph. It also provides generic structures and algorithms as a data fabric.