Sökning: "Scalable Graph Processing"

Hittade 3 uppsatser innehållade orden Scalable Graph Processing.

1. 1. Dynamic Graph Embedding on Event Streams with Apache Flink

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Massimo Perini; [2019]
   Nyckelord :Dynamic Graph; Representation Learning; Stream; Real-Time Data Processing; Scalable Graph Processing; Graph Neural Network; Experience Replay; Grafi dinamici; Representation Learning; Flussi di dati; Elaborazione in tempo reale; Elaborazione di grafi scalabile; Reti neurali per grafi; Experience Replay; Dynamisk graf; Representationsinlärning; ström; databehandling i realtid; skalbar grafbehandling; grafiskt neuralt nätverk; erfarenhetsåterspelning;

   Sammanfattning : Graphs are often considered an excellent way of modeling complex real-world problems since they allow to capture relationships between items. Because of their ubiquity, graph embedding techniques have occupied research groups, seeking how vertices can be encoded into a low-dimensional latent space, useful to then perform machine learning. LÄS MER

3. 2. An Evaluation of TensorFlow as a Programming Framework for HPC Applications

   Master-uppsats, KTH/Beräkningsvetenskap och beräkningsteknik (CST); KTH/Parallelldatorcentrum, PDC

   Författare :Wei Der Chien; [2018]
   Nyckelord :HPC; GPU; TensorFlow;

   Sammanfattning : In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and performance. At the core of these application is dense matrix-matrix multiplication. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabilities. LÄS MER

4. 3. Scalable Streaming Graph Partitioning

   Master-uppsats, KTH/Skolan för informations- och kommunikationsteknik (ICT)

   Författare :Seyed Mohammadreza Seyed Khamoushi; [2017]
   Nyckelord :streaming graph; vertex-cut partitioning; graph partitioning; distributed hash table;

   Sammanfattning : Large-scale graph-structured datasets are growing at an increasing rate. Social network graphs are an example of these datasets. Processing large-scale graphstructured datasets are central to many applications ranging from telecommunication to biology and has led to the development of many parallel graph algorithms. LÄS MER