System Level Resource Discovery and Management for Multi Core Environment

1. Javad Zarrin S(o)OS Project - CASTNESS'11 Roma, January17-18 2011 System Level Resource Discovery and Management for Multi Core Environment

2. Outline • Introduction • Challenges • Resource Description • Resource Discovery • Resource Management • Current SDPs • Proposed Solution • Simulation & Results • COTSon • HPL • Conclusion System Level Resource Discovery & Management For Multi Core Environment

3. Core n Private Cache – L1 Introduction Shared Cache – L2 Resource Discovery in So(o)S Project – Scenario – network topology for a cluster combined of n heterogeneous nodes with n CPUs ( n core per CPU) , n>=100 System Level Resource Discovery & Management For Multi Core Environment

4. Introduction Board and Memory Parameters Chip Parameters Core Parameters Shared Cache – L2 • Problems? • Memory latency, Bandwidth Bottleneck, Interconnection Network • Using all available resources in an efficient manner • How to define resources as services? • What is a resource? • Core • Chip • Board • Memory Chip • Pluggable Device • What are relevant? System Level Resource Discovery & Management For Multi Core Environment

5. Challenges - Resource Description • How to describe a resource? • Resource description for a huge number of heterogeneous resources (cores) in an adequate and efficient manner. • The heterogonous resources in the network needs to be defined by set of strict parameters, these parameters describe the characteristics and performance factors of the corresponded resources as services on the network. • Example parameters> Clock rate, MIPS, GFLOPS, cache size, SPEC Benchmark, etc., System Level Resource Discovery & Management For Multi Core Environment

6. Challenges - Resource Discovery • Massive amount of resources • Discovering all the existing cores on the local chip or on the network with a large scale is costly due to the excessive information exchange • Scalable search for required resources • Rate of Discovery • Parallel search algorithms • Packet Propagation System Level Resource Discovery & Management For Multi Core Environment

7. Challenges - Resource Management • Smart Resource Management • What is the best resource for a specific requirement? • What is the metric? • Fault tolerance System Level Resource Discovery & Management For Multi Core Environment

8. Service Discovery Protocols System Level Resource Discovery & Management For Multi Core Environment

9. Service Discovery Protocols System Level Resource Discovery & Management For Multi Core Environment

10. The Proposed Solution • Architecture : combination of distributed and centralized • Search : Informed -Heuristic Search Methods • Message Propagation : Unicast, Anycast • Announcement : Pull (Reactive, Query-based) in Network , Push (Proactive, Announcement-based) in Node • Scalable (Consistency and Service Validation) System Level Resource Discovery & Management For Multi Core Environment

11. QMS QMS QMS resource(m).setrank=query(z).getorigin.getrank(m) Search in the next neighboring tires resourceQuery(minReq) QMS QMS QMS RCT reply(RO) If queue(i).lenght(i) > threshold then generate.query(minReq) QMS QMS QMS The Proposed Solution – RD Mechanism 5 5 3 2 4 5 1 System Level Resource Discovery & Management For Multi Core Environment

12. The Proposed Solution • Service Cost , Cost Table and Resource Ranking Algorithms • Performance Parameters and Metrics • Memory, Cache • Clock Rate • GFLOPS • Alternatives: • Real time Benchmarking, • Micro Benchmarks (MHPC, SMB, MIBA) System Level Resource Discovery & Management For Multi Core Environment

13. Simulation & Result –Simulation Tools • COTson • HP Lab’s COTSon is a full system simulation framework based on AMD’s SimNow.COTSon allows for simulating complete computing systems, ranging from a single node to a large cluster of hundreds of multicore nodes. • High Performance Linkpack Benchmark (HPL) • "HPL is a software package that solves a (random) dense linear • system in double precision (64 bits) arithmetic on distributed- • memory computers. It can thus be regarded as a portable as • well as freely available implementation of the High • Performance Computing Linpack Benchmark.” • Alternative:NAMD System Level Resource Discovery & Management For Multi Core Environment

14. Simulation & Result –Simulation System Level Resource Discovery & Management For Multi Core Environment • Objective of simulation • To make comparison between the performance results of running HPL on simulated cluster with the proposed RD and also with SNMP • Sample Resource Cost Table

15. COTSON Control Control Script DataBase XML-RPC Host Control Daemon SimNow-Node1 SimNow-Node2 Q –Mediator - Network Core1 Core2 Core1 Core2 Memory Memory HDD HDD BSD BSD SimNow-Node3 Simnow-Node4 Core1 Core2 Core1 Core2 Memory Memory Simulation & Result –Simulation Architecture 4 3 1 2 System Level Resource Discovery & Management For Multi Core Environment

16. Results System Level Resource Discovery & Management For Multi Core Environment

17. Results System Level Resource Discovery & Management For Multi Core Environment

18. Conclusion & Future Work System Level Resource Discovery & Management For Multi Core Environment • According to the results of the simulation, we can conclude that : • The proposed method is scalable , when we increase the problem size and the cluster size , it shows better results. • The proposed resource discovery mechanism enhanced the total performance of the cluster with multi core nodes • This work still is in preliminary states , we will extend it to be more efficient and adapted with multi core environment.

19. Thank You System Level Resource Discovery & Management For Multi Core Environment