Why Choose This Project?
High-Performance Computing (HPC) clusters are critical for scientific simulations, AI/ML model training, weather forecasting, genomics research, and large-scale data analysis. Traditionally, HPC setups require massive upfront investment in physical servers and networking.
With Google Cloud HPC, students can deploy a scalable, on-demand HPC cluster without purchasing hardware. This project helps students learn parallel computing, workload scheduling, cluster scaling, and cloud-based orchestration — all essential skills for research and enterprise applications.
What You Get
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On-demand deployment of an HPC cluster on Google Cloud
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Multiple compute nodes for parallel processing
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Job scheduling and workload management via Slurm or PBS
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Real-time monitoring of cluster performance and resources
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Scalable infrastructure (add/remove compute nodes dynamically)
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Centralized storage for input/output datasets
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Secure access with SSH and IAM roles
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Cost optimization using preemptible VMs
Key Features
| Feature |
Description |
| Cluster Deployment |
Deploy multi-node HPC clusters on Google Cloud Compute Engine |
| Parallel Computation |
Run parallel jobs using MPI (Message Passing Interface) or OpenMP |
| Job Scheduling |
Use Slurm or PBS for automated job allocation and management |
| Scalable Nodes |
Add or remove compute nodes based on workload demands |
| Centralized Storage |
Use Google Cloud Storage or Filestore for shared access across nodes |
| Monitoring & Logging |
Track CPU, GPU, memory usage, and job status via Stackdriver |
| Secure Access |
Manage access using SSH and Google Cloud IAM authentication |
| Cost Optimization |
Use preemptible VMs to lower costs for short-running HPC workloads |
Technology Stack
| Layer |
Tools/Technologies |
| Compute Nodes |
Google Compute Engine VMs (with optional GPUs) |
| Job Scheduler |
Slurm / PBS for job management |
| Parallel Processing |
OpenMPI / OpenMP |
| Storage |
Google Cloud Storage / Filestore |
| Monitoring |
Stackdriver / Google Cloud Monitoring |
| Automation |
Deployment Manager / Terraform |
| Authentication |
SSH keys / Google Cloud IAM roles |
| Networking |
VPC, subnets, firewall rules for cluster communication |
Google Cloud Services Used
| Service |
Purpose |
| Compute Engine |
Provision virtual machines for HPC nodes |
| Cloud Storage |
Centralized storage for datasets |
| Filestore |
Shared file system across compute nodes |
| Stackdriver / Monitoring |
Monitor resource utilization and logs |
| Cloud IAM |
Secure access control and permissions |
| Deployment Manager/Terraform |
Automate provisioning of the HPC cluster |
| GPU-enabled VMs (optional) |
Accelerate computations for AI/ML |
Working Flow
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Cluster Provisioning
Deploy multiple Compute Engine instances as compute nodes with shared networking and storage.
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Install HPC Software
Configure MPI/OpenMP, job scheduler (Slurm/PBS), and required scientific libraries.
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Upload Input Data
Store input datasets in Cloud Storage or Filestore accessible by all nodes.
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Submit Jobs
Users submit computational jobs to the scheduler for allocation across nodes.
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Parallel Processing
Compute nodes process workloads in parallel, exchanging data via MPI/OpenMP.
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Monitor Performance
Use Stackdriver to monitor CPU, GPU, memory, and job status.
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Collect Results
Output datasets are aggregated in Cloud Storage or Filestore for analysis.
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Scale Cluster
Dynamically add/remove nodes depending on workload demand.
Customization
Fully
Security
High
Performance
Fast
Future Updates
Free
Total Buyers
500+
Support
Lifetime
