Graph Systems and Algorithms
Designing a performant and scalable graph processing python package
Python has proven to be a popular choice for data scientists in
the domain of graph analytics. The multitude of freely available
frameworks and python packages allow to develop applications
quickly through ease of expressibility and reuse of code.
With petabytes of data generated everyday and an ever evolving
landscape of hardware solutions, we observe a graph processing
framework should expose the following characteristics: ease of
use, scalability, interoperability across data formats, and
portability across hardware vendors.
While existing python packages have been helping to drive
application development, our assessment is that none of the
packages address all the aforementioned challenges.
We propose a community led, open source effort, to design and
build
a graph processing python library to specifically address
these challenges.
Additional information
| Type | devroom |
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More sessions
| 2/1/20 |
Graffiti is the graph engine of Skydive - an open source networking analysis tool. Graffiti was created from scratch to provide the features required by Skydive : distributed, replicated, store the whole history of the graph, allow subcribing to events on the graph using WebSocket and visualization.
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| 2/1/20 |
Graph algorithms play an increasingly important role in real-world applications. The Neo4j Graph Algorithms library contains a set of ~50 graph algorithms covering a lot of different problem domains. In our talk, we’ll present the architecture of the library and demonstrate the different execution phases using a real world example.
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| 2/1/20 |
Gunrock is a CUDA library for graph-processing designed specifically for the GPU. It uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on vertex or edge frontiers. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies, particularly in the area of fine-grained load balancing, with a high-level programming model that allows programmers to quickly develop new graph ...
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| 2/1/20 |
Graph databases and applications have attracted much attention in the past few years due to the efficiency with which they can represent big data, connecting different layers of data structures and allowing analysis while preserving contextual relationships. This has resulted in a fast-growing community that has been developing various database and algorithmic innovations in this area, many of which will be gathering together in this conference. We joined this field as computer architecture ...
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| 2/1/20 |
Large scale graph analytics is essential to analyze relationships in big data sets. Thereto, the DARPA HIVE program targets a leap in power efficient graph analytics. In response to this program, Intel proposes the Programmable Unified Memory Architecture (PUMA). Based on graph workload analysis insights, PUMA consists of many multi-threaded cores, fine-grained memory and network accesses, a globally shared address space and powerful offload engines. In this talk, we will describe the PUMA ...
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| 2/1/20 |
In this talk we will introduce enhancements to the Cypher graph query language, enabling queries spanning multiple graphs, intended for use in sharding and federation scenarios. We will also present our experience with sharding the LDBC Social Network Benchmark dataset.
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| 2/1/20 |
Temporal graphs capture the development of relationships within data throughout time. This model fits naturally within a streaming architecture, where new events can be inserted directly into the graph upon arrival from a data source, being compared to related entities or historical state. However, the vast majority of graph processing systems only consider traditional graph analysis on static data, with some outliers supporting batched updating and temporal analysis across graph snapshots. This ...
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