Henri BalVrije Universiteit Amsterdam

High Performance Distributed Computing

Outline

1. Development of the field 2. Highlights VU-HPDC group 3. Links to data science cycle

4. Conclusions

Developments

• Multiple types of data explosions:– Big data: huge processing/transportation

demands– Complex heterogeneous data

LOFAR: ~15 PB/yearSKA: >300 PB/year, exascale processing

Complex data

Developments

• Infrastructure explosion– High complexity: heterogeneous systems with

diversity of processors, systems, networks

VU HPDC GROUP

• Bridge the gap between demanding applications and complex infrastructure

• Distributed programming systems for– Clusters, grids, clouds– Accelerators (GPUs) – Heterogeneous systems (``Jungles”) – Clouds & mobile devices

• Applications: multimedia, semantic web, model checking, games, astronomy, astrophysics, climate modeling ….

Highlights VU-HPDC group

1st Prize: SCALE 2008

AAAI-VC 2007 DACH 2008 - BS DACH 2008 - FT

3rd Prize: ISWC 2008 1st Prize: SCALE 2010 EYR 2011Sustainability award

Solved Awari 2002

Links to data science cycle

Understand and decide

Analyze and model

Store and process

Reasoning

Knowledge representati

on

MultimediaRetrieval

Modeling and

simulation

Machine Learning

Information Retrieval

Decision Theory

PerceptionCognition

VisualAnalytics

DistributedProcessing

Large Scale Databases

SoftwareEng.

System / Network

Eng.

Distributed reasoning

Jungle computingMapReduce

Reasoning – Semantic Web

• Make the Web smarter by injecting meaning so that machines can “understand” it.o initial idea by Tim Berners-Lee in 2001

• Now attracted the interest of big IT companies

Google Example

Google Example

Distributed Reasoning

• WebPIE: web-scale distributed reasoner doing full materialization

• QueryPIE: distributed reasoning with backward-chaining + pre-materialization of schema-triples

• DynamiTE: maintains materialization after updates (additions & removals)

Challenge: real-time incremental reasoning on web scale, combining new (streaming) data & existing historic data

With: Jacopo Urbani, Alessandro Margara, Frank van Harmelen

COMMIT/

Glasswing: MapReduceon Accelerators

• Use accelerators as a mainstream feature• Massive out-of-core data sets• Scale vertically & horizontally• Code portability using OpenCL• Maintain MapReduce abstraction

With: Ismail El Helw, Rutger Hofman

Glasswing Pipeline

• Overlaps computation, communication & disk access

• Supports multiple buffering levels

Evaluation of Glasswing

• Glasswing uses CPU, memory & disk resources more efficiently than Hadoop

• Compute-bound applications benefit dramatically from GPUs

• Better scalability than Hadoop• Runs on a variety of accelerators• E.g. k-means clustering:

– 8.5× (1 node) vs.15.5 × (64 nodes) vs. 107 × (GPU node)