Cluster Computing Administration Training Course

Name: Cluster Computing Administration and Performance Training Course
Availability: InStock

Operate shared compute clusters through controlled scheduling, parallel execution, monitoring, and performance tuning.

Cluster Computing Administration Training Course

At a glance

Duration: 1 Week
Format: Classroom
Cities: London, Vienna, Abu Dhabi, Cairo, Lisbon, Barcelona and more
Next session: 9 – 13 November 2026, London
Average fee: 5,750 €

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Cluster Computing Administration and Performance Training Course

Overview

Cluster Computing Administration and Performance Training Course is a one-week foundation-to-intermediate course for infrastructure, research computing, data, and scientific computing functions who leave with a Cluster Workload Deployment and Tuning Plan. Participants connect cluster architecture, Linux environments, networking, storage, scheduling, parallel execution, observability, security, and capacity with measurable workload behavior. The course turns shared compute resources into a controlled operating workflow. Agile Leaders Training Center develops cluster computing administration and performance capability.

Who Should Attend

Infrastructure functions responsible for Linux compute nodes, networking, storage, and access
Research computing functions responsible for supporting scientific and engineering workloads
Data functions responsible for running scheduled processing across shared compute resources
Application functions responsible for parallel execution, scaling, and software environments
Operations functions responsible for monitoring, faults, security, and capacity

The course assumes participants use Linux commands or support technical workloads and leaves out cloud service architecture, data center facilities, advanced parallel algorithm development, and vendor certification preparation.

Departments and Industries

The course supports departments and industries that operate or use shared compute clusters.

Infrastructure, platform operations, research computing, data engineering, and application support
Research, education, engineering, and scientific services
Energy, manufacturing, healthcare, and life sciences
Technology, telecommunications, finance, and media
Public services, weather, transport, and analytics functions

Learning Objectives

By the end of this course, participants will be able to:

Analyze cluster architecture, node roles, networks, and storage paths
Apply Lmod software environments and controlled user access
Build Slurm job scripts, resource requests, and job arrays
Compare MPI and shared-memory parallel execution approaches
Evaluate monitoring, benchmarking, profiling, scaling, and efficiency evidence
Prioritize fault, security, and capacity actions for cluster workloads

Course Agenda

Day 1: Cluster Architecture and Linux Environment

Head, Login, Compute, and Storage Node Architecture Map
Linux Access, Identity, Permission, and Shell Workflow
Cluster Network and Shared Filesystem Path Diagram
Lmod Module Discovery, Load, Swap, and Purge Procedure
Software Stack and Dependency Compatibility Record

Day 2: Slurm Scheduling and Resource Control

Slurm Controller, Node, Partition, Job, and Job-Step Model
Batch Script and Resource Request Template
Queue State, Priority, Dependency, and Time-Limit Analysis
Job Array and Repeated Workload Submission Pattern
CPU, Memory, GPU, and Task Binding Checklist

Day 3: Parallel Workloads and Containers

Serial, Shared-Memory, and Distributed-Memory Comparison
MPI Process Placement and Communication Map
Thread Count, Affinity, and Hybrid Execution Plan
Scheduled Container Image and Bind-Mount Workflow
Parallel Input, Output, Checkpoint, and Restart Design

Day 4: Performance and Cluster Operations

Baseline Benchmark and Reproducibility Record
Runtime, Utilization, Throughput, and Queue-Wait Dashboard
Profiling Bottleneck and Scaling Efficiency Analysis
Node Health, Alert, Fault, and Recovery Runbook
Cluster Security and Capacity Planning Register

Day 5: Cluster Workload Deployment Practice

Exercise: Map Cluster Nodes, Networks, Storage, and Software
Exercise: Submit, Monitor, and Diagnose a Slurm Job Array
Exercise: Run and Compare Parallel Workload Configurations
Exercise: Benchmark, Profile, and Tune Resource Requests
Capstone Exercise: Cluster Workload Deployment and Tuning Plan

Practical Exercises

The course uses suggested activities to connect cluster controls with workload behavior.

Suggested activity: map login, compute, storage, network, identity, and software dependencies for a research cluster.
Suggested activity: write and diagnose Slurm jobs for data processing and simulation cases.
Suggested activity: compare MPI, threaded, hybrid, and containerized execution patterns.
Suggested activity: assemble benchmark, profiling, fault, security, and capacity evidence in a deployment and tuning plan.

FAQs

Who suits cluster computing administration training, and what does it assume?

Infrastructure, research computing, data, application, and operations functions suit the training. It assumes basic Linux command use or responsibility for technical workloads.

How does cluster computing training differ from general cloud computing training?

Cluster computing training focuses on nodes, shared filesystems, workload schedulers, parallel jobs, software modules, observability, and resource tuning, while general cloud training focuses on service models, virtual resources, managed services, and cloud governance.

What does Slurm manage in a compute cluster?

Slurm allocates compute resources, starts and monitors work, and manages queued jobs through concepts such as nodes, partitions, jobs, job steps, priorities, limits, and accounting.

How do MPI and shared-memory computing differ?

MPI coordinates explicit communication among processes, including processes on different nodes, while shared-memory approaches coordinate threads or processes that access memory on the same node.

How is cluster workload performance evaluated?

Cluster workload performance is evaluated through reproducible baselines, runtime, utilization, throughput, queue wait, scaling efficiency, profiling evidence, input and output behavior, and resource-request accuracy.

Conclusion

Participants take a Cluster Workload Deployment and Tuning Plan back to work. It connects architecture, access, software environments, scheduling, parallel execution, containers, observability, performance, faults, security, and capacity. It changes an ad hoc cluster workload into a controlled and measurable operating workflow with documented resource and tuning decisions.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Location	Dates	Duration	Mode	Price
Nice	Week 21, 2027 24 – 28 May 2027	1 Week	Onsite	€5,700
Phuket	Week 21, 2027 30 May – 3 June 2027	1 Week	Onsite	€6,000
Rome	Week 23, 2027 7 – 11 June 2027	1 Week	Onsite	€5,700
Milan	Week 23, 2027 7 – 11 June 2027	1 Week	Onsite	€5,700
Nairobi	Week 23, 2027 13 – 17 June 2027	1 Week	Onsite	€4,500
Tokyo	Week 24, 2027 14 – 18 June 2027	1 Week	Onsite	€10,000
Manama	Week 24, 2027 20 – 24 June 2027	1 Week	Onsite	€4,700
Madrid	Week 26, 2027 28 June – 2 July 2027	1 Week	Onsite	€5,700
Bali	Week 26, 2027 4 – 8 July 2027	1 Week	Onsite	€5,700
Zoom	Week 27, 2027 5 – 9 July 2027	1 Week	Online	€1,500
Paris	Week 28, 2027 12 – 16 July 2027	1 Week	Onsite	€5,700
New York	Week 28, 2027 12 – 16 July 2027	1 Week	Onsite	€12,000
Cape town	Week 28, 2027 18 – 22 July 2027	1 Week	Onsite	€4,500
Abu Dhabi	Week 30, 2027 26 – 30 July 2027	1 Week	Onsite	€4,700
Casablanca	Week 31, 2027 2 – 6 August 2027	1 Week	Onsite	€4,100
Vienna	Week 31, 2027 2 – 6 August 2027	1 Week	Onsite	€5,700
Muscat	Week 31, 2027 8 – 12 August 2027	1 Week	Onsite	€5,700
Dubai	Week 32, 2027 9 – 13 August 2027	1 Week	Onsite	€4,500
Doha	Week 32, 2027 15 – 19 August 2027	1 Week	Onsite	€5,500
Johannesburg	Week 33, 2027 22 – 26 August 2027	1 Week	Onsite	€4,500

Frequently asked questions

What does this course cover?

OverviewCluster Computing Administration and Performance Training Course is a one-week foundation-to-intermediate course for infrastructure, research computing, data, and scientific computing functions who leave with a Cluster Workload Deployment and Tuning Plan. Participants connect cluster architecture, Linux environments, networking, storage, schedulin…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Cluster Computing Administration Training Course