RUNNING PARALLEL APPLICATIONS BEYOND EP WORKLOADS IN DISTRIBUTED COMPUTING ENVIRONMENTS

Zholudev Yury

Traditional desktop grids• Scalable• Distributed• Dynamic• Heterogeneous

• Centralized tasks pool• Little or no

communications between client-side tasks

Traditional desktop grids• messaging between

computational nodes could be implemented via server-side communications

Traditional desktop grids• messaging between

computational nodes could be implemented via server-side communications

• yields significant amount of synchronization overhead handled mostly on server side

Traditional desktop grids• messaging between

computational nodes could be implemented via server-side communications

• yields significant amount of synchronization overhead handled mostly on server side

• reduces reliability and performance

Peer-to-peer desktop grids• Decentralized• Scalable• Dynamic• Internode

communications• Network topology

independence

• High latency• Hard to control• Discovery and routing

overhead

Peer-to-peer desktop grids• Jalapeno, JNGI, OurGrid, Triana, Xeerkat

• Distributed resource management and sequential tasks (bag-of-tasks) deployment using P2P-based file sharing

• Partially support EP applications• P3 (Personal Power Plant)

• Master-Worker and Message Passing programming models for static set of nodes using JXTA

• P2P-MPI• MPJ implementation for dynamic set of nodes located inside single

LAN with parallel processes replication using number of super-nodes• Cohesion Orbweb (SAT solving)

• Implements and employs distributed tasks pool for a subset of IS problems using XMPP

The goal

• Implement a light-weight p2p-based parallel computing middleware featuring:

• Network topology independence• Reliability• Support for Message Passing programming model

Requirements• Network topology independence• Parallel task can be submitted from any participating peer• Communications between parallel task processes• Support for simultaneous run of multiple tasks• Result data can be accessed via any participating peer• Support for dynamic sets of peers

• Parallel task may run on any number of peers• Disconnection of any peer should not result in overall failure

• Processes can migrate between peers• Processes can have multiple replicas running on different peers

• Multiple platforms support

Programming model• Parallel task – set of

interacting parallel processes (jobs)

• Each process can address any other process via messaging

• Parallel task execution is split into computation regions by task-wide control points

• Communication operations are carried out at control points

• Processes are topology-oblivious and address other processes via their numbers

Implementation: peer cycle

Implementation: architecture• Java

• Cross-platform• Ease of deployment

• JXTA (JXSE 2.6)• Discovery services• Routing• Content transfer services• Pipes and Sockets• Custom objects

advertisements• Peer group abstractions

and membershipjob process file system

file servicemessaging service

job and messages tracking and migration

JXTA peer group

file transfer service

messaging service

discovery services

network

Current work: X-P2P• Basic prototype implementation with fixed number of jobs

per application• Jobs migration and replication strategies

• Random job stealing/pushing• Failure detection and recovery strategies• Security

• Peer group membership• Topology and network performance awareness

• Application deployment• Jobs grouping

• Resource management• Experiments and performance evaluation

Ultimate goals

• Meet leading supercomputers performance on Linpack

• Make it to the top of Graph500

THANK YOUContact & participate!

code.google.com/p/xp2p/diemenator@gmail.com