Silviu Panica, Marian Neagul, Daniela Zaharie and Dana Petcu (Romania)

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1 Silviu Panica, Marian Neagul, Daniela Zaharie and Dana Petcu (Romania)

2 Outline Introduction EO challenges; EO and classical/cloud computing; EO Services The computing platform Cluster -> Grid -> Cloud Environment Application programming model Distributed -> Parallel/Multi-core (HPC) (Cluster -> Grid -> Cloud Environment)[HPC]; Conclusions

3 Introduction EO challenges: large amount of data to deal with; data and time consuming complex algorithms: large computing resources are required; complex network topologies are required in order to speedup the algorithms communication/data transfers (in some special cases); EO and classical/cloud environments cluster environment local cluster computational power / distributed FS / standalone FS

4 Introduction grid environment federative cluster infrastructure involves several services that permit cluster inter-connect for increased resource power and storage capacity; cloud environment a new distributed computing paradigm that should offer superior scalability in comparison with grid computing; it has several layers: Software/Application (SaaS) Platform (PaaS) Infrastructure (IaaS) EO being a computational and data intensive it suits better on IaaS layer;

5 Introduction parallel and multi-core computing (HPC) special programming model that fits on each computing group; it has some special requirement (regarding network topology and speed); speeds up EO application processing time (if the algorithm supports a parallel version);

6 The computing platform for EO Use case: GiSHEO Project ( ESA funded project through PECS programme; set-up and organize a virtual organization (VO) based on Grid technology for education, training and knowledge dissemination for Earth observation (EO) development of specific instruments dedicated to ondemand services for education activities based on Earth observation information; facilitate specific access for the academic and scientific community to on-demand services related to specific applications using ESA database and facilitate synergies;

7 Computing platform on Clusters and GRIDs env. current implementation: local distributed model; grid-enabled model (each project partner has its own cluster inter-connected into a federative clustering model); each local cluster can offer multi-core and parallel computing support depending on it s own configuration and hardware technology;

8 GiSHEO Architecture

9 GiSHEO main components

10 GiSHEO platform on CLOUDs how to migrate? fully cloud compliant or legacy support? each component must be analyzed and a decission has to be made: is suitable for cloud or should be ported as a legacy application? example: databases: in clouds we have DataStores (columnar, keyvalue etc.) and less possibilities to implement complex SQL queries processing: clouds supports Map/Reduce but is not always suitable for our classical algorithms

11 GiSHEO platform: legacy migration GiSHEO's G-PROC service and EO Application database (WAS) are moved completely into the clouds. This can be done in several steps because we need to migrate and adapt (rewrite) some components to be suitable for clouds technologies: G-PROC Service G-PROC Task database from SQLite to Key-Value Cloud Database (Store); G-PROC Processing storage (task directories and output) to a cloud storage; GiSHEO Image repository connector for downloading datasets from GiSHEO Database; Web Application Service (EO Application) cloud storage for storing the binaries; key-value store for storing applications parameters Processing platform as a VMs infrastructure (Condor HTC instances are in the cloud);

12 EO Platform cluster/grid

13 G2Clouds : New Architecture

14 G2Cloud: G-PROC service

15 G2Cloud: cloud compliant we propose to transform the most important parts of the GiSHEO Platform to fit the cloud; new GiSHEO components: MetaDataStore - a cloud database (columnar or key-value; depending on the type of data that are stored and the operations we want to support) for storing datasets metadata and EO application information; Processing component - a cloud VM infrastructure where EO application can be processed; Raw Storage Component - a cloud storage service for storing EO related data (datasets, tasks desc., app. binaries);

16 G2Cloud: GiSHEO EO (compliant) New GiSHEO services: DataProcessing Service - a service responsible with data processing (old G-PROC); DataIndexing Service - a service similar to GDIS; New GiSHEO Front-ends: FlowDefinition Service - a service where the UI will deploy the workflow description for data processing; DataUpload Service - a service for uploading datasets into clouds;

17 G2Cloud Architecture

18 G2Cloud Processing service

19 Application programming model distributed programming low inter-process communication/no communication; fits well in each computing platform type (cluster/grid/cloud) either we are talking about data or computing intensive application; application doesn t need any modification to be adapted for each platform type; use cases from GiSHEO training platform: in Geography: computing the vegetation index for various reasons could require the following operations: extract red band, extract infrared band, compute by using the previously obtained images the Normalized Difference Vegetation Index (NDVI).

20 Application programming model in Archeology : identifying human settlements might require the following chain of operations: gray level conversion, histogram equalization, quantization and threshold. (all operations in sequence)

21 Application programming model multi-core/parallel programming (HPC) main problem: communication infrastructure latency! on a local cluster this problems is solved by using a high speed inter-connect; on a grid environment there some restrictions: Grid sites must have a high speed inter-connect; application must support job splitting in order to submit parts of the parallel application on different site solving parts of the problem;

22 Application development model on a cloud infrastructure: multi-core programming can be used with a minimum of effort (although there some restrictions at inter-core bandwidth usage depending on the booked cloud resources type); parallel programming: only if cloud providers has HPC resources to offer (example: amazon.com offers such of resources); maybe, a new communication stack suitable for heterogeneous systems like the Clouds (on working); the cloud environment is not suitable for massively parallel -like application;

23 Remote sensing use case Unsupervised classification (clustering) = identify regions in the image characterized by similar feature values Classified image (3 classes) Original image (Heights of the Eyjafjallajökull Eruption Plume - April 19, 2010) Classified image (6 classes)

24 Parallel Fuzzy C-means Parallelization variant [T. Kwok, et. al Parallel Fuzzy c-means Clustering for Large Data Sets, LNCS 2400, , 2002] Each processor: deals with an image slice computes the membership values (U) corresponding to the image slice (local computation) computes the complete set of centroids (V) (global computation)

25 Experiments: Environments InfraGrid Computational Cluster : Nodes: 16 nodes, 8 cores / node; CPU: Intel Xeon 2.0Ghz CPUs, 4 cores per CPU (64 bits mode); RAM: 1.25GB / core; High-speed interconnect: 40Gbps 4xQDR Infiniband (2.5 μs response time on MPI communication); BlueGene/ P Nodes: 32 nodes x 32 compute cards x 1CPU CPU: 850Mhz PowerPC 450d, 4 cores per CPU (32 bits mode); RAM: 1GB / core; High-speed interconnect: 3D Torus ~48Gbps bandwith ( ns response time on MPI communication) Collective interconnect: 16Gbps bandwith (3μs response time for MPI collective)

26 Experiments: Test images Landsat image: image of the Danube region Multispectral image: AVIRIS low altitude, high spatial resolution Source: Romanian Catalogue Image size: width = 8786 pixels height = 7856 pixels no. spectral bands = 4 total files size = 270 Mb Source: NASA Catalog ( ris.freedata.html) Image size: width=614 pixels height = 1087 pixels no. spectral bands = 224 Total files size = 294 Mb

27 InfraGrid vs. BlueGene/P s s Time Time InfraGrid InfraGrid 200 BG P 4000 BG P log 2 P AVIRIS image (224 spectral bands) Landsat image (4 spectral bands)

28 Conclusions cluster/grid/cloud environments can exploited in both distributed and parallel/multi-core programming models but with some restrictions in case of parallel computing where communication is very important; a hybrid approach between CLOUDs and HPC can lead to a good cost per performance ratio in some cases (ex.: training in EO, EO image preprocessing etc.); in case of massively parallel -like applications supercomputers cannot be replaced, at this point, with any of the infrastructures presented;

29 Thank you. Questions?!

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