Concept

Computer cluster

Related lectures (32)

Distributed Actors: Principles and Failure Detection

Explores distributed actors, failure detection in clusters, and lifecycle monitoring for distributed fail-over.

Using SCITAS Clusters

Covers the setup and usage of general purpose computing clusters at EPFL.

Distributed Actors: Cluster Management

Explores distributed actor management in clusters, emphasizing consensus, failure detection, and lifecycle monitoring.

Graph metrics: Statistical analysis

Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Big Data Challenges: Distributed Computing with Spark

Explores big data challenges, distributed computing with Spark, RDDs, hardware requirements, MapReduce, transformations, and Spark DataFrames.

Advanced Design in Semiconductor Electronics

Explores challenges in advanced semiconductor design, focusing on power efficiency, bandwidth, and volume predictions in computing engines.

Dynamics of Singular Riemann Surface Foliations

Explores the dynamics of singular Riemann surface foliations on compact Kähler manifolds.

Efficient Data Clustering

Covers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.

Parallel Architectures: Shared Memory, Shared Disk, Shared Nothing

Covers the architectures for parallel databases, including Shared Memory, Shared Disk, and Shared Nothing.

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K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Open-World Semantic Scene Understanding: SCIM

Introduces SCIM for open-world semantic scene understanding through clustering, inference, and mapping.

Observable Universe: Contents and Properties

Explores the 3D distribution of galaxies, galaxy clustering, and the cosmic microwave background, shedding light on the observable universe's contents and properties.

Tackle the Type I - East Studio

Explores housing typologies through case studies and urban design analysis.

Hybrid Programming and Project Proposals

Covers hybrid programming models, project proposals, thread safety, topology problems, and performance expectations in MPI.

Genomic Data Analysis: Clustering and Survival

Explores genomic data clustering, survival analysis, gene identification, and statistical significance in cancer research.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.