Condor daemons. condor_master. condor_startd. condor_starter

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Condor daemons

Æcondor_master

It is responsible for keeping the rest of the Condor daemons running in a pool The master spawns the other daemon

runs on every machine in your Condor pool

Æcondor_startd

Any machine that wants to execute jobs needs to have this daemon running It advertises a machine ClassAd

responsible for enforcing the policy under which the jobs will be started, suspended, resumed, vacated, or killed

Æcondor_starter

Spawned by condor_startd

sets up the execution environment , create a process to run the user job, and monitors the job running

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Æ

condor_schedd

Any machine that allows users to submit jobs needs to have the daemon running

Users submit jobs to the condor_schedd, where they are stored in the job queue.

condor_submit, condor_q, or condor_rm connect to the condor_schedd to view and manipulate the job queue

Æ

condor_shadow

Created by condor_schedd

runs on the machine where a job was submitted

Any system call performed on the remote execute machine is sent over the network to this daemon, and the shadow performs the

system call (such as file I/O) on the submit machine and the result is sent back over the network to the remote job

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Æ

condor_collector

collecting all the information about the status of a Condor pool

All other daemons periodically updates information to the collector

These information contain all the information about the state of the daemons, the resources they represent, or resource requirements of the submitted jobs

condor_status command connects to this daemon for information

Æ

_{condor_negotiator}

responsible for all the matchmaking within the Condor system

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Interactions among Condor daemons

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Condor demons in running

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Resource Management System : Condor

Most jobs are not independent:

Dependencies exists between jobs.

Second stage cannot start until first stage has completed.

Condor uses DAGMan - Directed Acyclic Graph

Manager

DAGMan allows you to specify dependencies between your Condor jobs, then it run the jobs automatically in the sequence satisfying the dependencies.

DAGs are the data structures used by DAGMan to represent these dependencies.

Each job is a “node” in the DAG.

Each node can have any number of “parent” or “children” nodes – as long as there are no loops.

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Resource Management System : Condor

A DAG is defined by an text file separate from the

Condor job description file, listing each of nodes and

their dependencies:

# diamond.dag Job A a.sub Job B b.sub Job C c.sub Job D d.sub Parent A Child B C Parent B C Child D

Dagman will exam the dag file, locate the submission

file for each job and run the jobs in the right sequence.

Job A

Job B Job C

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Example Clusters

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BlueGene

/L

Source: IBM

No. 1 in Top500 list from 2005-2007

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BlueGene

/L

–

networking

BlueGene system

employs various network

types.

Central is the torus

interconnection network:

3D torus with wrap-around.

Each node connects to six neighbours (bidirectional).

Routing achieved in hardware.

each link with 1.4 Gbit/s.

1.4 x 6 x 2= 16.8 Gbit/s aggregate bandwidth

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BlueGene

/L

Other three networks:

Binary combining tree

• Used for collective/global operations - reductions, sums, products , barriers etc. • Low latency (2μS)

Gigabit Ethernet I/O network

• Support file I/O

• An I/O node is responsible for performing I/O operations for 128 processors

Diagnostic & control network

• Booting nodes, monitoring processors.

Each chip has the above four network interfaces (torus, tree,

i/o, diagnostics)

Note specialised networks are used for different purposes

-quite different from many other HPC cluster architectures.

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BlueGene

/L

Message Passing:

The BlueGene focussed a good deal of energy developing an efficient MPI implementation to reduce latency in the software stack.

Using the MPICH code-base as a start-point:

• MPI library was enhanced with respect to machine architecture. • For example, using the combining tree for reductions & broadcasts.

Reading paper:

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ASCI Q

The Q supercomputing system at Los Alamos National Laboratory (LANL)

Product of Advanced Simulation and Computing (ASCI) program

Used for simulation and computational modelling

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ASCI Q

“Classical” cluster architecture.

1024 SMPs (AlphaServer ES45s from HP) are put in one segment

• Each with four EV-68 1.25Ghz CPUs with 16-MB cache

the whole system has 3 segments

• The three segments can operate independently or as a single system • Aggregate 60 TeraFLOPS capability.

• 33 Terabytes of memory

664 TB of global storage

Interconnection using

• Quadrics switch interconnect (QSNet)

• High bandwidth (250MB/s) and Low latency (5us) network.

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Earth Simulator

Built by NEC, located in the Earth Simulator Centre in Japan

Used for running global climate models to evaluate the effects of global warming No.1 from 2002-04

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Earth Simulator

Æ640 nodes, each with 8 vector processors and 16GB memory

Two nodes are installed in one cabinet ÆIn total:

5120 processors (NEC SX-5)

10 TeraByte memory

700 TeraByte of disk storage and 1.6 PetaByte of Tape storage

Computing capacity: 36 TFlop/s

ÆNetworking: Crossbar interconnection (very expensive)

Bandwidth: 16GB/s between any two nodes

Latency: 5us

ÆDual level parallelism: OpenMP in-node, MPI out of node

ÆPhysical installation:

Machine resides on 3th floor; Cables on 2nd; _{Power generation & cooling}

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UK systems

–

Cambridge

PowerEdge

Æ

_{576 Dell PowerEdge 1950}

compute servers

Computing capability: 28TFlop/s

Æ

Each server has two

Dual-Core Intel Xeon 5160

processors

3GHz and 8GB of memory

Æ

InfiniBand network

Bandwidth: 10GBit

Latency: 7us

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Cluster Networks

Introduction

Communication has significant impact on application performance.

Interconnection networks therefore have a vital role in cluster systems.

As usual, the driver is performance…

An increase in compute power typically demands proportional increases in lower latency / higher bandwidth communication services.

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Cluster Networks

Issues with cluster interconnections are similar to those

with normal networks:

Latency & Bandwidth

• Latency= sender overhead + switching overhead + (message size / Bandwidth) + receiver overhead.

Topology type (bus, ring, torus, hypercube etc).

Routing, switching.

Direct connections (point-to-point) or indirect connections.

NIC (Network Interface Card) capabilities.

Physical media (wiring density, reliability)

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Interconnection Topologies

In standard LANs we have two general structures:

Shared network (bus)

• As used by “classic” Ethernet networks.

• All messages are broadcast… each processor listens to every message. • Requires complex access control (e.g. CSMA/CD).

• Collisions can occur: requires back-off policies and retransmissions.

• Suitable when the offered load is low - inappropriate for high performance

applications.

• Very little reason to use this form of network today.

Switched network

• Permits point-to-point communications between sender & receiver. • Fast internal transport provides high aggregate bandwidth.

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Metrics to evaluate network topology

Useful metrics for switched network topology:

Scalability : the network’s switch scalability with nodes.

Degree: number of links to / from a node.

Diameter: the shortest path between the furthest nodes.

Bisection width: the minimum number of links that must be cut in order to divide the topology into two independent networks of the same size (+/- one node). Essentially a measure of

bottleneck bandwidth - if higher, the network will perform better under load.

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Interconnection Topologies

Crossbar switch:

Low latency and high throughput.

Switch scalability is poor - O(N2₎

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Interconnection Topologies

Linear Arrays and Rings

Consider networks with switch scaling costs better than O(N2_).

In one dimension, we have simple linear arrays.

O(N) switches.

These can wrap around to make a ring or 1D torus.

good overall bandwidth but latency is high.

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Interconnection Topologies

2D Meshes

Can wrap-around as a 2D torus.

Switch scaling: O(N)

Average degree: 4

Diameter: O(2n1/2₎

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Interconnection Topologies

Hypercubes:

K dimension, Switches N= 2K_. Diameter: O(K).

Good bisectional width (O(2K-1_)).

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Interconnection Topologies

Binary Tree:

Scaling:

• n = 2d processor nodes (where d = depth)

• 2d+1-1 switches

Degree: 3

Diameter: O(2d)

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Interconnection Topologies

Fat trees:

Similar in diameter to a binary tree.

Bisection width (which equates to bottleneck) is greatly improved due to additional dimensions.

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Interconnection Topologies

Summary of topologies:

Topology Degree Diameter Bisection

1D Array 2 N-1 1

1D Ring 2 N/2 2

2D Mesh 4 2N1/2 _N1/2

2D Torus 4 N1/2 _2N1/2

Hypercube n=log(N) n N/2

There are others - we saw a 3D torus in the BlueGene/L section for instance.