Command and Control of a Massively Parallel GALS Environment

Transcription

1 Cameron Patterson Supervisor: Steve Furber SpiNNaker Team, APT Group, University of Manchester, UK. Command and Control of a Massively Parallel GALS Environment 1

2 SpiNNaker Management ASIC for modelling Artificial Neural Networks Utilises Asynchronous Interconnects On-chip System and Communication NoCs Off-chip inter-connects to other SpiNNaker chips Up to 20 cores per chip Each core simulates up to 1,000 neurons System Scales to >1M cores and >1Bn neurons 2

3 Resource Contention Management In the brain there are billions of neurons, massive interconnectivity, but biological operation is slow Computing technology is fast, so we can multiplex Contention is mapped by anticipated statistics Machine is homogenous However behaviour of NN is organic: we don t know where `hot-spots will form 3

4 Proposed Research (1of3) System Command and Control Many thousands of components in a large system: Chips, RAMs, Links, Ethernets They could go wrong `Fault Alerting Other facets of system management: Capacity Management Accounting Performance Analysis Security * Screen from HP Openview Management Software * O.S.I. FCAPS model 4

5 Proposed Research (2of3) Real-time Software Monitoring Neural Software running in biological time * Picture from: Powell K: Economy of the Mind. PLoS Biol 1/3/2003: e77. Rewarding the Brain Ventral midbrain activity The system doesn t have to be a like a `batch system / `black box Customers may be neuroscientists or psychologists Which parts of the artificial brain are`lit-up Save this activity for later timeline analysis 5

6 Proposed Research (3of3) In-Flight Reconfiguration Management Dealing with the consequences of a h/w or s/w issue A core is overloaded A chip is failing A link is congested Take automated remedial action Remap neurons to different fascicle processors Re-routing of data around the fault in the machine Turning on QoS mechanisms / prioritisation 6

7 Infrastructure To retrieve the information from the system: Two components: Protocol to get/set data Data stored Common solution used for network attached systems: SNMP * MIB * Research attempting implementation of SNMP & MIB on SpiNNaker * J Case, M Fedor, M Schoffstall, and C Davin. RFC 1067 A Simple Network Management Protocol (SNMP), 1989 * M. Rose. Management Information Base for Network Management of TCP/IP-based internets: MIB-II,

8 Infrastructure SpiNNaker is a very large scale system It may therefore require many command and control machines to monitor the whole system Functions can be split into management domains eg: Function: Capacity, Faults, Software Visualisation etc. Type: processor utilisation, memory use, link capacity Location: cluster of chips by ID, physical location etc. 8

9 Issues to Overcome SpiNNaker resources are limited Small instruction memory Restricted I/O via Ethernet links Want to limit all but essential load to maximise neural computation The management system domain will be large. A solution that minimises system resources is required, and one which is scalable Hierarchical System of both NMSs and agents AgentX * permits master/slave agent relationship delegates the collection and data store for the end systems * M Daniele, B Wijnen,M Ellison, and D Francisco. RFC 2741 Agent Extensibility (AgentX) Protocol,

10 Proposal Combine `system and `neural management data into single standardised command/control SNMP framework Offload as much processing as possible from the SpiNNaker machine A protocol translator is therefore proposed to provide facilities for both To SpiNNaker side, looks like a low cost native host MIB To NMS looks like a standard SNMP agent 10

11 Visualisation What might the management systems look like? 11

12 Work so Far Design of low processing cost IP compatible protocol for the SpiNNaker Ethernet links Permits routing - collaboration with partners off site & resiliency Test Software Doughnut Hunter Neural Network Application Implemented on test systems Validation of IP protocol Diagnostic testing and specification Existing Test Chip Proposals for next iteration of chip design Comms, Processor, RAM 12

13 Future Work Implementing the SNMP and Protocol Translator system for SpiNNaker Devising a MIB for hardware and software Software Creation, optimisation, comparison with other solutions Topological Testing placing the P.T. internally to the system Creation of Neural Visualisation Explore standard tools vs. bespoke Extend P.T. to provide standard neural imaging output format In-Flight System reconfiguration Use management output to command/control the system Look at automation of re-routing around hot spots and failures without a stop being required of the system 13

14 Conclusions The Protocol Translator is a promising idea to offload management functions, but providing a standard SNMP interface to an NMS It also seems that using the same framework in order to support both hardware and software visualisation is a valid one The low cost IP protocol developed has already been validated as a low-cost way to significantly improve network functionality 14

15 Questions? Contact Details - pattersc@cs.man.ac.uk Web